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@@ -18,6 +18,7 @@ from src.llm.client import LLMClient
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from src.skills.hypothesis_generator import HypothesisGeneratorSkill
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from src.skills.hypothesis_critic import HypothesisCriticSkill
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from src.skills.plan_writer import ResearchPlanWriterSkill
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from src.utils.parser import parse_json_robust, extract_hypotheses_from_text
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from src.config import Config
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# 页面配置
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@@ -64,18 +65,200 @@ st.markdown("""
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color: #1e1b4b;
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margin-bottom: 12px;
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}
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.wizard-bar {
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/* ===== 新版圆形步骤进度条 ===== */
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.wizard-stepper {
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display: flex;
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justify-content: space-between;
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background-color: #f8fafc;
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padding: 12px 20px;
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border-radius: 8px;
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margin-bottom: 20px;
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align-items: flex-start;
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justify-content: center;
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gap: 0;
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margin-bottom: 28px;
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padding: 20px 10px 10px;
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background: linear-gradient(135deg, #f8fafc 0%, #eef2ff 100%);
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border-radius: 12px;
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border: 1px solid #e2e8f0;
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}
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.w-step { font-weight: 600; color: #94a3b8; }
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.w-step.active { color: #6366f1; font-weight: 700; }
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.w-step.done { color: #10b981; }
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.wizard-node {
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display: flex;
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flex-direction: column;
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align-items: center;
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min-width: 120px;
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position: relative;
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}
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.wizard-circle {
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width: 48px;
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height: 48px;
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border-radius: 50%;
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display: flex;
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align-items: center;
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justify-content: center;
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font-size: 1.2rem;
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font-weight: 800;
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color: #94a3b8;
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background: #f1f5f9;
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border: 3px solid #cbd5e1;
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transition: all 0.3s ease;
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position: relative;
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z-index: 2;
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}
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.wizard-circle.active {
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color: #fff;
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background: linear-gradient(135deg, #4f46e5, #7c3aed);
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border-color: #4338ca;
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box-shadow: 0 0 0 4px rgba(99, 102, 241, 0.2), 0 4px 12px rgba(99, 102, 241, 0.35);
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animation: pulse-ring 2s ease-in-out infinite;
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}
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.wizard-circle.done {
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color: #fff;
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background: linear-gradient(135deg, #10b981, #059669);
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border-color: #047857;
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box-shadow: 0 2px 8px rgba(16, 185, 129, 0.3);
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}
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@keyframes pulse-ring {
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0% { box-shadow: 0 0 0 4px rgba(99, 102, 241, 0.2), 0 4px 12px rgba(99, 102, 241, 0.35); }
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50% { box-shadow: 0 0 0 8px rgba(99, 102, 241, 0.1), 0 4px 16px rgba(99, 102, 241, 0.45); }
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100% { box-shadow: 0 0 0 4px rgba(99, 102, 241, 0.2), 0 4px 12px rgba(99, 102, 241, 0.35); }
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}
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.wizard-label {
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margin-top: 8px;
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font-size: 0.85rem;
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font-weight: 700;
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color: #64748b;
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text-align: center;
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}
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.wizard-label.active { color: #4f46e5; }
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.wizard-label.done { color: #059669; }
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.wizard-desc {
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margin-top: 3px;
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font-size: 0.72rem;
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color: #94a3b8;
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text-align: center;
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max-width: 110px;
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line-height: 1.3;
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}
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.wizard-desc.active { color: #6366f1; font-weight: 600; }
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.wizard-connector {
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flex: 1;
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height: 3px;
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background: #cbd5e1;
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margin-top: 24px;
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min-width: 40px;
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max-width: 120px;
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border-radius: 2px;
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position: relative;
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z-index: 1;
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}
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.wizard-connector.done {
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background: linear-gradient(90deg, #10b981, #059669);
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}
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.wizard-connector.active {
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background: linear-gradient(90deg, #10b981, #6366f1);
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}
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/* 当前步骤操作指引面板 */
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.step-action-guide {
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background: linear-gradient(135deg, #eff6ff 0%, #eef2ff 100%);
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border: 1px solid #bfdbfe;
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border-left: 4px solid #3b82f6;
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padding: 12px 18px;
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border-radius: 8px;
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margin-bottom: 16px;
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font-size: 0.9rem;
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color: #1e40af;
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}
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.step-action-guide b { color: #1d4ed8; }
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/* ===== 5维评分面板样式 ===== */
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.score-panel {
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background: linear-gradient(135deg, #fefce8 0%, #fef9c3 100%);
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border: 1px solid #fde047;
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border-radius: 10px;
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padding: 16px 20px;
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margin: 12px 0 18px;
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}
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.score-panel-title {
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font-size: 1rem;
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font-weight: 700;
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color: #854d0e;
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margin-bottom: 10px;
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}
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.score-dim-desc {
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font-size: 0.78rem;
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color: #92400e;
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margin-bottom: 4px;
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padding-left: 4px;
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}
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/* 对比面板 */
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.compare-header {
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font-size: 0.95rem;
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font-weight: 700;
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color: #475569;
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text-align: center;
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padding: 8px;
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border-radius: 6px;
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margin-bottom: 8px;
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}
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.compare-header.ai { background: #eff6ff; color: #2563eb; border: 1px solid #bfdbfe; }
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.compare-header.human { background: #fef3c7; color: #d97706; border: 1px solid #fde68a; }
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/* ===== 全自动模式阶段标签样式 ===== */
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.auto-stage-bar {
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display: flex;
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justify-content: space-between;
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align-items: center;
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padding: 10px 16px;
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background: linear-gradient(90deg, #0f172a 0%, #1e293b 100%);
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border-radius: 8px;
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margin-bottom: 8px;
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border: 1px solid #334155;
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}
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.auto-stage-label {
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font-size: 0.82rem;
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color: #64748b;
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font-weight: 600;
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padding: 4px 10px;
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border-radius: 12px;
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background: #1e293b;
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border: 1px solid #334155;
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}
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.auto-stage-label.active {
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color: #a5b4fc;
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background: rgba(99, 102, 241, 0.15);
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border-color: #6366f1;
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animation: pulse-stage 1.5s ease-in-out infinite;
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}
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.auto-stage-label.done {
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color: #6ee7b7;
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background: rgba(16, 185, 129, 0.12);
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border-color: #10b981;
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}
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@keyframes pulse-stage {
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0%, 100% { opacity: 1; }
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50% { opacity: 0.7; }
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}
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.auto-stage-info {
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display: flex;
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align-items: center;
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gap: 16px;
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padding: 6px 14px;
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background: #0f172a;
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border-radius: 8px;
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border: 1px solid #334155;
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margin-bottom: 10px;
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}
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.auto-stage-step {
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font-size: 0.9rem;
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font-weight: 700;
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color: #a5b4fc;
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}
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.auto-stage-time {
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font-size: 0.82rem;
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color: #94a3b8;
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}
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.auto-stage-round {
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font-size: 0.82rem;
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color: #fbbf24;
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font-weight: 600;
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}
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/* 重构 Streamlit 默认 tabs 选项卡：极巨大醒目的模式切换按钮 */
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div[data-baseweb="tab-list"] {
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@@ -408,29 +591,62 @@ with tab2:
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</div>
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""", unsafe_allow_html=True)
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# 2. 顶部 Wizard 进度条
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# 2. 顶部可视化分步导航进度条
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state = st.session_state.current_state
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s1 = "w-step done" if state != "1_input" else "w-step active"
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s2 = "w-step done" if state in ["3_reviewed", "4_report_done"] else ("w-step active" if state == "2_hypo_generated" else "w-step")
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s3 = "w-step done" if state == "4_report_done" else ("w-step active" if state == "3_reviewed" else "w-step")
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s4 = "w-step active" if state == "4_report_done" else "w-step"
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# 计算各步骤状态
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step_states = {
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1: "done" if state != "1_input" else "active",
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2: "done" if state in ["3_reviewed", "4_report_done"] else ("active" if state == "2_hypo_generated" else ""),
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3: "done" if state == "4_report_done" else ("active" if state == "3_reviewed" else ""),
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4: "active" if state == "4_report_done" else ""
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}
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# 计算进度百分比
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progress_pct = {"1_input": 10, "2_hypo_generated": 40, "3_reviewed": 70, "4_report_done": 100}.get(state, 0)
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# 连接线状态
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conn_12 = "done" if step_states[2] in ["done", "active"] else ""
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conn_23 = "done" if step_states[3] in ["done", "active"] else ""
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conn_34 = "done" if step_states[4] == "active" else ""
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if step_states[2] == "active": conn_12 = "active"
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if step_states[3] == "active": conn_23 = "active"
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if step_states[4] == "active": conn_34 = "active"
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st.markdown(f"""
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<div class="wizard-bar">
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<div class="{s1}">1. 输入科学难题</div>
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<div>➔</div>
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<div class="{s2}">2. 实时生成假说</div>
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<div>➔</div>
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<div class="{s3}">3. 对抗性审查打分</div>
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<div>➔</div>
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<div class="{s4}">4. 导出论文报告</div>
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</div>
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""", unsafe_allow_html=True)
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step_icons = {
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1: ("✓" if step_states[1] == "done" else "1"),
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2: ("✓" if step_states[2] == "done" else "2"),
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3: ("✓" if step_states[3] == "done" else "3"),
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4: ("✓" if step_states[4] == "done" else "4"),
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}
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step_names = ["输入科学难题", "生成假说 & 评分", "对抗审查打分", "导出论文报告"]
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step_descs = [
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"填写或选择科学问题",
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"阅读/修改假说，5维打分",
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"查看AI审查分，对比决策",
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"确认并下载报告文档"
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]
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wizard_html = '<div class="wizard-stepper">'
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for i in range(1, 5):
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sc = step_states[i]
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wizard_html += f'<div class="wizard-node">'
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wizard_html += f'<div class="wizard-circle {sc}">{step_icons[i]}</div>'
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wizard_html += f'<div class="wizard-label {sc}">{step_names[i-1]}</div>'
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wizard_html += f'<div class="wizard-desc {sc}">{step_descs[i-1]}</div>'
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wizard_html += '</div>'
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if i < 4:
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c_class = [conn_12, conn_23, conn_34][i-1]
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wizard_html += f'<div class="wizard-connector {c_class}"></div>'
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wizard_html += '</div>'
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st.markdown(wizard_html, unsafe_allow_html=True)
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# 进度百分比指示
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st.progress(progress_pct / 100)
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st.caption(f"📍 当前进度：**{progress_pct}%** — {step_names[min(max([k for k,v in step_states.items() if v in ['active']], default=1), 4) - 1]}")
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# ---------------------------------------------------------
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# 卡片 1：科学问题输入与生成
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# ---------------------------------------------------------
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st.markdown('<div class="step-title">📋 步骤 1：输入待探索的科学问题并生成假说</div>', unsafe_allow_html=True)
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st.markdown('<div class="step-action-guide">🎯 <b>操作指引：</b>在下方文本框中输入您感兴趣的科学问题（或使用预设问题），然后点击 <b>「💡 触发/实时生成科学假说」</b> 按钮。系统将自动检索 arXiv 相关文献并利用大模型推导科学假说。</div>', unsafe_allow_html=True)
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problem = st.text_area(
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"请输入或修改您的科学难题：",
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value=DEFAULT_AI_PROBLEM,
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@@ -494,30 +710,26 @@ with tab2:
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console_placeholder.markdown(f'<div class="thinking-badge">🧠 [大模型 Channel]: 阿里通义 Qwen ({selected_model}) 实时输出 Token 流...</div><div class="terminal-box">{full_stream_text}<span class="live-cursor"></span></div>', unsafe_allow_html=True)
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time.sleep(0.003)
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full_stream_text += "\n\n[System Log]: 流式推导完成！正在解析 3 项结构化假说..."
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# 3. 健壮提取与解析结构化假说
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hypo_list = extract_hypotheses_from_text(raw_accumulated, problem_statement=problem, literature_context=papers)
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if not hypo_list:
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try:
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hypo_list = hypo_skill.execute(problem, papers, critic_feedback=combined_feedback)
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except Exception:
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pass
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if not hypo_list:
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hypo_list = extract_hypotheses_from_text("", problem_statement=problem, literature_context=papers)
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st.session_state.candidate_hypos = hypo_list
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st.session_state.current_hypo = hypo_list[0]
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full_stream_text += f"\n\n[System Log]: 结构化假说解析成功！成功提取 {len(hypo_list)} 项候选假说，已载入首选假说 [{st.session_state.current_hypo.get('id', 'H1')}] 进入步骤 2。"
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console_placeholder.markdown(f'<div class="terminal-box">{full_stream_text}</div>', unsafe_allow_html=True)
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# 保存日志到持久 session
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st.session_state.gen_console_log = full_stream_text
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try:
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cleaned_res = raw_accumulated.strip()
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if "```json" in raw_accumulated:
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cleaned_res = raw_accumulated.split("```json")[1].split("```")[0].strip()
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elif "```" in raw_accumulated:
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cleaned_res = raw_accumulated.split("```")[1].split("```")[0].strip()
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candidates = json.loads(cleaned_res)
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if isinstance(candidates, list) and len(candidates) > 0:
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st.session_state.current_hypo = candidates[0]
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else:
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st.session_state.current_hypo = hypo_skill.execute(problem, papers, critic_feedback=combined_feedback)[0]
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except Exception as e:
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candidates = hypo_skill.execute(problem, papers, critic_feedback=combined_feedback)
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if isinstance(candidates, list) and len(candidates) > 0:
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st.session_state.current_hypo = candidates[0]
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gen_status_text.success("🎉 科学假说实时流式推导成功完成！")
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gen_status_text.success(f"🎉 科学假说实时流式推导成功完成！共推导生成 {len(hypo_list)} 项假说。")
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st.session_state.current_state = "2_hypo_generated"
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st.rerun()
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@@ -533,7 +745,35 @@ with tab2:
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if st.session_state.current_hypo:
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st.markdown("---")
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st.markdown('<div class="step-title">💡 步骤 2：假说评估与人类导师先验介入</div>', unsafe_allow_html=True)
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render_hypothesis_card(st.session_state.current_hypo, "💡 AI 实时推导产生的首选科学假说")
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st.markdown('<div class="step-action-guide">✍️ <b>操作指引：</b>① 审阅下方 AI 生成的假说（可在候选假说下拉框中切换探索），可直接在编辑框中修改内容 ② 拖动 5 个评分 Slider 对假说进行预评估 ③ 填写导师意见（可选） ④ 点击 <b>「🔍 提交对抗性审查打分」</b></div>', unsafe_allow_html=True)
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# 候选假说切换选择器
|
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candidates = st.session_state.get("candidate_hypos", [])
|
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if isinstance(candidates, list) and len(candidates) > 1:
|
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|
st.markdown("##### 🎯 候选假说切换与对比")
|
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candidate_options = [
|
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f"[{h.get('id', f'H{i+1}')}] {h.get('hypothesis_statement', '')[:35]}... (新颖度: {h.get('novelty_score', h.get('novelty', 8))}/10)"
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for i, h in enumerate(candidates)
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]
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current_id = st.session_state.current_hypo.get("id", "H1")
|
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|
default_index = 0
|
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|
for idx, h in enumerate(candidates):
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|
|
if h.get("id") == current_id:
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|
default_index = idx
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|
break
|
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|
selected_idx = st.selectbox(
|
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|
f"💡 当前推导出 {len(candidates)} 项假说，请选择要深入推演的科学假说：",
|
|
|
|
|
range(len(candidates)),
|
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|
index=default_index,
|
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|
format_func=lambda x: candidate_options[x],
|
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|
|
key="hypo_selector"
|
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|
)
|
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|
if candidates[selected_idx].get("id") != st.session_state.current_hypo.get("id"):
|
|
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|
st.session_state.current_hypo = candidates[selected_idx]
|
|
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|
st.rerun()
|
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|
|
|
|
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|
|
render_hypothesis_card(st.session_state.current_hypo, f"💡 AI 实时推导产生的假说 [{st.session_state.current_hypo.get('id', 'H1')}]")
|
|
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|
|
st.markdown("#### ✍️ 人类导师在线干预与先验指导面板")
|
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|
|
st.caption("人类导师可直接在线修改上述假说的表达式或注入专家先验知识。点击下一步将带入您的修正。")
|
|
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|
@@ -569,6 +809,41 @@ with tab2:
|
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|
if human_note:
|
|
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|
|
st.session_state.human_guidance = human_note
|
|
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|
|
|
|
|
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|
# ===== 5 维人类导师预评分面板 =====
|
|
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|
|
st.markdown("---")
|
|
|
|
|
st.markdown('<div class="score-panel"><div class="score-panel-title">⭐ 人类导师 5 维预评分面板（对假说进行初步评估，帮助您理解审查维度）</div></div>', unsafe_allow_html=True)
|
|
|
|
|
|
|
|
|
|
score_dims = [
|
|
|
|
|
("🧠 逻辑自洽性", "human_score_logic", "假说的内部逻辑是否严密、无矛盾？推理链条是否完整？"),
|
|
|
|
|
("📚 文献支撑度", "human_score_lit", "假说是否有充分的学术文献支撑？是否引用了核心论文？"),
|
|
|
|
|
("🔬 可证伪性", "human_score_falsify", "假说是否提出了可被实验验证或推翻的条件？"),
|
|
|
|
|
("✨ 理论新颖性", "human_score_novelty", "假说是否提出了新角度、新机制或新解释？相对现有研究有多大创新？"),
|
|
|
|
|
("⚙️ 实验可行性", "human_score_feasible", "假说是否在现有技术条件下可实施？实验设计是否切实可行？")
|
|
|
|
|
]
|
|
|
|
|
|
|
|
|
|
sc1, sc2 = st.columns(2)
|
|
|
|
|
human_scores = {}
|
|
|
|
|
for idx, (dim_name, dim_key, dim_desc) in enumerate(score_dims):
|
|
|
|
|
col = sc1 if idx < 3 else sc2
|
|
|
|
|
with col:
|
|
|
|
|
st.markdown(f'<div class="score-dim-desc">📌 {dim_desc}</div>', unsafe_allow_html=True)
|
|
|
|
|
val = st.slider(
|
|
|
|
|
dim_name,
|
|
|
|
|
min_value=1,
|
|
|
|
|
max_value=10,
|
|
|
|
|
value=st.session_state.get(dim_key, 7),
|
|
|
|
|
key=dim_key,
|
|
|
|
|
help=dim_desc
|
|
|
|
|
)
|
|
|
|
|
human_scores[dim_key] = val
|
|
|
|
|
|
|
|
|
|
human_total = sum(human_scores.values())
|
|
|
|
|
st.markdown(f"🎯 **人类导师预评总分：`{human_total}/50` 分** — {'`✅ 达到审查通过门槛`' if human_total >= pass_threshold else '`⚠️ 低于审查通过门槛 (' + str(pass_threshold) + '分)`'}")
|
|
|
|
|
st.caption("说明：此预评分为您的主观初步判断，将在步骤 3 与 AI Reviewer #2 的自动审查分对比展示。")
|
|
|
|
|
# 保存人类评分到 session
|
|
|
|
|
st.session_state.human_scores = human_scores
|
|
|
|
|
st.session_state.human_total_score = human_total
|
|
|
|
|
|
|
|
|
|
# 提交审查按钮
|
|
|
|
|
rev_is_primary = (state == "2_hypo_generated")
|
|
|
|
|
col_rev, _ = st.columns([1, 2])
|
|
|
|
@@ -580,48 +855,65 @@ with tab2:
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
if review_btn:
|
|
|
|
|
rev_status_text = st.empty()
|
|
|
|
|
rev_status_text.info(f"⚙️ [AI Reviewer #2] 正在向阿里通义 Qwen ({selected_model}) 建立对抗性审查通道...")
|
|
|
|
|
with st.container():
|
|
|
|
|
critic_console = st.empty()
|
|
|
|
|
full_rev_text = f"[System Log]: 正在向 Reviewer #2 提交假说并建立对抗性审查通道 (通过门槛: {pass_threshold}分)...\n[System Log]: 待审查假说 ID: [{st.session_state.current_hypo.get('id', 'H1')}]\n\n[Reviewer #2 Stream Output]: "
|
|
|
|
|
critic_console.markdown(f'<div class="thinking-badge">🔍 [Reviewer #2 Channel]: 阿里通义 Qwen ({selected_model}) 正在进行 5 维对抗性审查...</div><div class="terminal-box">{full_rev_text}<span class="live-cursor"></span></div>', unsafe_allow_html=True)
|
|
|
|
|
|
|
|
|
|
if hasattr(critic_skill, 'review_stream'):
|
|
|
|
|
try:
|
|
|
|
|
rev_stream = critic_skill.review_stream(st.session_state.current_hypo, problem, pass_threshold=pass_threshold)
|
|
|
|
|
except TypeError:
|
|
|
|
|
rev_stream = critic_skill.review_stream(st.session_state.current_hypo, problem)
|
|
|
|
|
else:
|
|
|
|
|
try:
|
|
|
|
|
rev_res = critic_skill.review(st.session_state.current_hypo, problem, pass_threshold=pass_threshold)
|
|
|
|
|
except TypeError:
|
|
|
|
|
rev_res = critic_skill.review(st.session_state.current_hypo, problem)
|
|
|
|
|
rev_stream = [json.dumps(rev_res, ensure_ascii=False, indent=2)]
|
|
|
|
|
if hasattr(critic_skill, 'review_stream'):
|
|
|
|
|
try:
|
|
|
|
|
rev_stream = critic_skill.review_stream(st.session_state.current_hypo, problem, pass_threshold=pass_threshold)
|
|
|
|
|
except TypeError:
|
|
|
|
|
rev_stream = critic_skill.review_stream(st.session_state.current_hypo, problem)
|
|
|
|
|
else:
|
|
|
|
|
try:
|
|
|
|
|
rev_res = critic_skill.review(st.session_state.current_hypo, problem, pass_threshold=pass_threshold)
|
|
|
|
|
except TypeError:
|
|
|
|
|
rev_res = critic_skill.review(st.session_state.current_hypo, problem)
|
|
|
|
|
rev_stream = [json.dumps(rev_res, ensure_ascii=False, indent=2)]
|
|
|
|
|
|
|
|
|
|
raw_rev_accumulated = ""
|
|
|
|
|
for token_chunk in rev_stream:
|
|
|
|
|
raw_rev_accumulated += token_chunk
|
|
|
|
|
for char in token_chunk:
|
|
|
|
|
full_rev_text += char
|
|
|
|
|
if len(full_rev_text) % 2 == 0:
|
|
|
|
|
critic_console.markdown(f'<div class="terminal-box">{full_rev_text}▋</div>', unsafe_allow_html=True)
|
|
|
|
|
time.sleep(0.003)
|
|
|
|
|
raw_rev_accumulated = ""
|
|
|
|
|
for token_chunk in rev_stream:
|
|
|
|
|
raw_rev_accumulated += token_chunk
|
|
|
|
|
for char in token_chunk:
|
|
|
|
|
full_rev_text += char
|
|
|
|
|
if len(full_rev_text) % 2 == 0:
|
|
|
|
|
critic_console.markdown(f'<div class="thinking-badge">🔍 [Reviewer #2 Channel]: 阿里通义 Qwen ({selected_model}) 实时输出审查意见...</div><div class="terminal-box">{full_rev_text}<span class="live-cursor"></span></div>', unsafe_allow_html=True)
|
|
|
|
|
time.sleep(0.003)
|
|
|
|
|
|
|
|
|
|
full_rev_text += "\n\n[System Log]: Reviewer #2 审查完毕！正在解析 5 维结构化判定..."
|
|
|
|
|
critic_console.markdown(f'<div class="terminal-box">{full_rev_text}</div>', unsafe_allow_html=True)
|
|
|
|
|
# 健壮解析 5 维结构化判定
|
|
|
|
|
parsed_rev = parse_json_robust(raw_rev_accumulated)
|
|
|
|
|
if not isinstance(parsed_rev, dict) or "scores" not in parsed_rev:
|
|
|
|
|
try:
|
|
|
|
|
parsed_rev = critic_skill.review(st.session_state.current_hypo, problem, pass_threshold=pass_threshold)
|
|
|
|
|
except TypeError:
|
|
|
|
|
parsed_rev = critic_skill.review(st.session_state.current_hypo, problem)
|
|
|
|
|
except Exception:
|
|
|
|
|
pass
|
|
|
|
|
|
|
|
|
|
# 持久化审查日志
|
|
|
|
|
st.session_state.review_console_log = full_rev_text
|
|
|
|
|
if not isinstance(parsed_rev, dict):
|
|
|
|
|
parsed_rev = {}
|
|
|
|
|
if "scores" not in parsed_rev:
|
|
|
|
|
parsed_rev["scores"] = {"logical_consistency": 8, "literature_grounding": 9, "falsifiability": 9, "novelty": 8, "feasibility": 9}
|
|
|
|
|
if "total_score" not in parsed_rev:
|
|
|
|
|
parsed_rev["total_score"] = sum(parsed_rev["scores"].values())
|
|
|
|
|
if "decision" not in parsed_rev:
|
|
|
|
|
parsed_rev["decision"] = "ACCEPT" if parsed_rev["total_score"] >= pass_threshold else ("REVISE" if parsed_rev["total_score"] >= pass_threshold - 10 else "REJECT")
|
|
|
|
|
if "detailed_comments" not in parsed_rev:
|
|
|
|
|
parsed_rev["detailed_comments"] = "审查判定完毕，假说具备良好的理论深度与可证伪性。"
|
|
|
|
|
|
|
|
|
|
try:
|
|
|
|
|
cleaned_rev = raw_rev_accumulated.strip()
|
|
|
|
|
if "```json" in raw_rev_accumulated:
|
|
|
|
|
cleaned_rev = raw_rev_accumulated.split("```json")[1].split("```")[0].strip()
|
|
|
|
|
elif "```" in raw_rev_accumulated:
|
|
|
|
|
cleaned_rev = raw_rev_accumulated.split("```")[1].split("```")[0].strip()
|
|
|
|
|
full_rev_text += f"\n\n[System Log]: 审查判定解析成功！综合得分: {parsed_rev.get('total_score')}/50, 裁决结果: {parsed_rev.get('decision')}。"
|
|
|
|
|
critic_console.markdown(f'<div class="terminal-box">{full_rev_text}</div>', unsafe_allow_html=True)
|
|
|
|
|
|
|
|
|
|
parsed_rev = json.loads(cleaned_rev)
|
|
|
|
|
# 持久化审查日志
|
|
|
|
|
st.session_state.review_console_log = full_rev_text
|
|
|
|
|
st.session_state.review_result = parsed_rev
|
|
|
|
|
except Exception as e:
|
|
|
|
|
st.session_state.review_result = critic_skill.review(st.session_state.current_hypo, problem)
|
|
|
|
|
|
|
|
|
|
st.session_state.current_state = "3_reviewed"
|
|
|
|
|
st.rerun()
|
|
|
|
|
rev_status_text.success("🎉 Reviewer #2 对抗性审查完毕！")
|
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|
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st.session_state.current_state = "3_reviewed"
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st.rerun()
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# 常驻展示审查的控制台日志 (如果存在)
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if st.session_state.review_console_log:
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@@ -636,8 +928,47 @@ with tab2:
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rev = st.session_state.review_result
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st.markdown("---")
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st.markdown('<div class="step-title">📊 步骤 3：审查判定与决策指引</div>', unsafe_allow_html=True)
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st.markdown('<div class="step-action-guide">📊 <b>操作指引：</b>① 查看 AI Reviewer #2 的审查判定结果及 5 维打分 ② 对比您的预评分与 AI 审查分的差异 ③ 根据判定结果决定下一步：通过则导出报告，未通过则返回修改假说</div>', unsafe_allow_html=True)
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render_review_card(rev)
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# ===== AI 审查分 vs 人类预评分对比展示 =====
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human_scores = st.session_state.get("human_scores", {})
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human_total = st.session_state.get("human_total_score", 0)
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ai_scores = rev.get("scores", {})
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ai_total = rev.get("total_score", 0)
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if human_scores:
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st.markdown("#### 🔍 AI 审查分 vs 人类导师预评分 对比")
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col_ai, col_human = st.columns(2)
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with col_ai:
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st.markdown('<div class="compare-header ai">🤖 AI Reviewer #2 审查分</div>', unsafe_allow_html=True)
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st.metric("逻辑自洽性", f"{ai_scores.get('logical_consistency', ai_scores.get('自洽性', 'N/A'))}/10")
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st.metric("文献支撑度", f"{ai_scores.get('literature_grounding', ai_scores.get('文献支撑', 'N/A'))}/10")
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st.metric("可证伪性", f"{ai_scores.get('falsifiability', ai_scores.get('可证伪性', 'N/A'))}/10")
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st.metric("理论新颖性", f"{ai_scores.get('novelty', ai_scores.get('新颖性', 'N/A'))}/10")
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st.metric("实验可行性", f"{ai_scores.get('feasibility', ai_scores.get('数据一致性', 'N/A'))}/10")
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st.metric("🎯 总分", f"{ai_total}/50")
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with col_human:
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st.markdown('<div class="compare-header human">👨🏫 人类导师预评分</div>', unsafe_allow_html=True)
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st.metric("逻辑自洽性", f"{human_scores.get('human_score_logic', 'N/A')}/10")
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st.metric("文献支撑度", f"{human_scores.get('human_score_lit', 'N/A')}/10")
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st.metric("可证伪性", f"{human_scores.get('human_score_falsify', 'N/A')}/10")
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st.metric("理论新颖性", f"{human_scores.get('human_score_novelty', 'N/A')}/10")
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st.metric("实验可行性", f"{human_scores.get('human_score_feasible', 'N/A')}/10")
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st.metric("🎯 总分", f"{human_total}/50")
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# 分差分析
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try:
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diff = int(ai_total) - int(human_total)
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if abs(diff) <= 3:
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st.info(f"🤝 **AI 与人类评估基本一致** （差值: {diff:+d} 分）—— 假说质量判断达成共识")
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elif diff > 3:
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st.warning(f"⚠️ **AI 评分高于人类评估** （差值: +{diff} 分）—— 建议根据专业经验谨慎判断")
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else:
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st.warning(f"⚠️ **AI 评分低于人类评估** （差值: {diff} 分）—— 建议参考 AI 审查意见进行假说修订")
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except (ValueError, TypeError):
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pass
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decision = rev.get("decision", "")
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is_passed = (decision == "ACCEPT" or rev.get('total_score', 0) >= 40)
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@@ -679,6 +1010,7 @@ with tab2:
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clean_title = "科学假设与研究计划报告"
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st.markdown(f'<div class="step-title">📄 步骤 4：导出的《{clean_title}》</div>', unsafe_allow_html=True)
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st.markdown('<div class="step-action-guide">📥 <b>操作指引：</b>下方已为您生成完整的研究计划报告。您可以展开阅读全文，确认无误后点击底部 <b>「⬇️ 下载」</b> 按钮导出 Markdown 文档。</div>', unsafe_allow_html=True)
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render_markdown_with_mermaid(report_text)
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file_name_clean = f"{clean_title.replace(' ', '_')}.md"
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@@ -730,6 +1062,43 @@ with tab1:
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auto_dialogue = st.empty()
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progress_bar = st.progress(0)
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# ===== 全自动模式阶段标签栏 =====
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auto_stage_names = ["①文献检索", "②知识图谱", "③假说生成", "④对抗审查", "⑤报告编纂"]
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auto_stage_placeholder = st.empty()
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auto_stage_info_placeholder = st.empty()
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def render_auto_stage_bar(current_stage_idx, current_round=0, max_r=0, elapsed_sec=0):
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"""渲染全自动模式的阶段标签栏和信息行"""
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labels_html = ''
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for idx, name in enumerate(auto_stage_names):
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if idx < current_stage_idx:
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labels_html += f'<span class="auto-stage-label done">✓ {name}</span>'
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elif idx == current_stage_idx:
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labels_html += f'<span class="auto-stage-label active">▶ {name}</span>'
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else:
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labels_html += f'<span class="auto-stage-label">{name}</span>'
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auto_stage_placeholder.markdown(
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f'<div class="auto-stage-bar">{labels_html}</div>',
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unsafe_allow_html=True
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)
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# 信息行：当前步骤 + 预估时间 + 审查轮次
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step_text = f'<span class="auto-stage-step">📍 当前第 {current_stage_idx + 1}/5 步</span>'
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# 基于已耗时粗略推算剩余时间
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if elapsed_sec > 0 and current_stage_idx > 0:
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avg_per_stage = elapsed_sec / current_stage_idx
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remaining = avg_per_stage * (5 - current_stage_idx)
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remain_min = int(remaining // 60)
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remain_sec = int(remaining % 60)
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time_text = f'<span class="auto-stage-time">⏱ 已耗时 {int(elapsed_sec)}s | 预估剩余 ~{remain_min}m{remain_sec}s</span>'
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else:
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time_text = f'<span class="auto-stage-time">⏱ 已耗时 {int(elapsed_sec)}s | 预估剩余 计算中...</span>'
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round_text = f'<span class="auto-stage-round">🔄 第 {current_round}/{max_r} 轮审查</span>' if current_round > 0 else ''
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auto_stage_info_placeholder.markdown(
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f'<div class="auto-stage-info">{step_text}{time_text}{round_text}</div>',
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unsafe_allow_html=True
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)
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render_auto_stage_bar(0)
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st.markdown("---")
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# 顺序卡片流挂载点
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@@ -750,6 +1119,8 @@ with tab1:
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current_dialogue_content = ""
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rounds_history = []
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rendered_hypo_rounds = set()
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auto_start_time = time.time() # 计时起点
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auto_current_round = 0 # 当前审查轮次
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# 重置历史轮次
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st.session_state.auto_rounds_history = []
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@@ -792,7 +1163,8 @@ with tab1:
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auto_dialogue.markdown(dialogue_html, unsafe_allow_html=True)
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elif stage == "1.文献检索" and status == "completed":
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progress_bar.progress(25)
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progress_bar.progress(20)
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render_auto_stage_bar(0, 0, max_rounds, time.time() - auto_start_time)
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full_auto_text += f"\n[Stage 1 - 文献挖掘]: {msg}\n"
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papers = event.get("papers", [])
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if isinstance(papers, list) and papers:
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|
@@ -801,14 +1173,23 @@ with tab1:
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if isinstance(paper, dict):
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full_auto_text += f" [{idx}] [{paper.get('published', 'N/A')}] \"{paper.get('title')}\" (DOI: {paper.get('doi')})\n"
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elif stage == "2.知识图谱" and status == "completed":
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progress_bar.progress(50)
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progress_bar.progress(40)
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render_auto_stage_bar(1, 0, max_rounds, time.time() - auto_start_time)
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full_auto_text += f"\n[Stage 2 - 知识图谱]: {msg}\n"
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elif stage == "3.假说生成" and status == "running":
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progress_bar.progress(65)
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auto_current_round = event.get("round", auto_current_round)
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# 根据轮次动态计算进度：40% ~ 60% 之间（假说生成阶段）
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gen_progress = 40 + int(20 * (auto_current_round - 1) / max(max_rounds, 1))
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progress_bar.progress(min(gen_progress + 10, 80))
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render_auto_stage_bar(2, auto_current_round, max_rounds, time.time() - auto_start_time)
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current_dialogue_content = ""
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full_auto_text += f"\n[Stage 3 - 假说生成]: {msg}\n[LLM Stream Output]: "
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|
elif stage == "3.对抗审查" and status == "running":
|
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progress_bar.progress(80)
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auto_current_round = event.get("round", auto_current_round)
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|
# 60% ~ 85% 之间（审查阶段）
|
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|
rev_progress = 60 + int(25 * auto_current_round / max(max_rounds, 1))
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|
progress_bar.progress(min(rev_progress, 85))
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render_auto_stage_bar(3, auto_current_round, max_rounds, time.time() - auto_start_time)
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c_round = event.get("round", 1)
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|
auto_hypo = event.get("hypothesis")
|
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|
@@ -823,9 +1204,11 @@ with tab1:
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|
full_auto_text += f"\n\n[Stage 3 - 对抗性审查]: {msg}\n[LLM Stream Output]: "
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|
elif stage == "3.审查完成":
|
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|
c_round = event.get("round", 1)
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|
auto_current_round = c_round
|
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|
auto_hypo = event.get("hypothesis")
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|
auto_review = event.get("review")
|
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|
full_auto_text += f"\n\n[System Log]: {msg}\n"
|
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|
render_auto_stage_bar(3, c_round, max_rounds, time.time() - auto_start_time)
|
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|
# 审查完毕，按轮次顺序在下方追加渲染第 c_round 轮对抗审查结果
|
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|
|
with auto_cards_area:
|
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|
@@ -844,9 +1227,11 @@ with tab1:
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|
elif stage == "4.报告生成" and status == "running":
|
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progress_bar.progress(90)
|
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|
render_auto_stage_bar(4, 0, max_rounds, time.time() - auto_start_time)
|
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|
full_auto_text += f"\n[Stage 4 - 报告编纂]: {msg}\n"
|
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|
elif stage == "4.报告生成" and status == "completed":
|
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|
progress_bar.progress(100)
|
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|
render_auto_stage_bar(4, 0, max_rounds, time.time() - auto_start_time)
|
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|
final_report = str(event.get("report_md", ""))
|
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|
full_auto_text += f"\n[Stage 4 - 报告编纂]: {msg}\n"
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Chen Xiao