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app.py

@@ -78,42 +78,44 @@ def format_step_viewer(consensus_result):
 
 def process_mvm2_pipeline(image, auto_enhance):
     if image is None:
-        return None, "Please upload an image.", None, "", None, ""
+        yield None, "Please upload an image.", None, "", None, None, generate_flow_html("idle")
+        return
     
     # 1. Preprocessing & Preview
+    yield None, "Enhancing image...", None, "", None, None, generate_flow_html("enhance")
     enhanced_img_np, meta = enhancer.enhance(image)
     temp_img_path = os.path.join(tempfile.gettempdir(), 'input_processed.png')
     cv2.imwrite(temp_img_path, enhanced_img_np)
     
     preview_img = Image.fromarray(cv2.cvtColor(enhanced_img_np, cv2.COLOR_BGR2RGB))
-    
+    yield preview_img, "Extracting LaTeX...", None, "", None, None, generate_flow_html("ocr")
+
     # 2. OCR Extraction
     ocr_results = ocr_engine.process_image(temp_img_path)
     latex_text = ocr_results['latex_output']
     ocr_conf = ocr_results['weighted_confidence']
     
-    # Update flow for current state (Visual sync)
-    flow_html = generate_flow_html("reasoning") # Transitioning to reasoning
-    
+    yield preview_img, latex_text, None, "", None, None, generate_flow_html("reasoning")
+
     # 3. Multi-Agent Reasoning
     agent_responses = run_agent_orchestrator(latex_text)
-    
-    # Attach traces back to detail_scores for UI formatting
-    for i, res in enumerate(agent_responses):
-        agent_responses[i]["response"]["agent_id"] = i # tag
-        
-    # 4. Consensus Fusion
+
+    # 4. Advanced Heuristics Refinement stage
+    yield preview_img, latex_text, None, "", None, None, generate_flow_html("heuristics")
+    time.sleep(0.5)
+
+    # 5. Consensus Fusion
+    yield preview_img, latex_text, None, "", None, None, generate_flow_html("consensus")
     consensus_result = evaluate_consensus(agent_responses, ocr_confidence=ocr_conf)
     
-    # Map traces to detail_scores for UI
+    # Attach traces back to detail_scores for UI formatting
     for i, score_data in enumerate(consensus_result["detail_scores"]):
-        # Match by agent name
         for res in agent_responses:
             if res["agent"] == score_data["agent"]:
                 consensus_result["detail_scores"][i]["reasoning_trace"] = res["response"].get("Reasoning Trace", [])
                 break
 
-    # 5. Gauges & UI Elements
+    # 6. Gauges & UI Elements
     avg_v_sym = np.mean([s["V_sym"] for s in consensus_result["detail_scores"]])
     avg_l_logic = np.mean([s["L_logic"] for s in consensus_result["detail_scores"]])
     avg_c_clf = np.mean([s["C_clf"] for s in consensus_result["detail_scores"]])
@@ -126,7 +128,6 @@ def process_mvm2_pipeline(image, auto_enhance):
     </div>
     """
     
-    # Final Calibration Bar
     winner = consensus_result["winning_score"]
     calibrated_conf = winner * (0.9 + 0.1 * ocr_conf)
     conf_bar = f"""
@@ -136,12 +137,12 @@ def process_mvm2_pipeline(image, auto_enhance):
             <span style="color: #10b981; font-weight: bold;">{calibrated_conf:.3f}</span>
         </div>
         <div style="width: 100%; bg: rgba(255,255,255,0.05); height: 8px; border-radius: 4px; overflow: hidden;">
-            <div style="width: {min(100, calibrated_conf*50)}%; background: linear-gradient(90deg, #6366f1 0%, #10b981 100%); height: 100%; transition: width 1s ease;"></div>
+            <div style="width: {min(100, calibrated_conf*100)}%; background: linear-gradient(90deg, #6366f1 0%, #10b981 100%); height: 100%; transition: width 1s ease;"></div>
         </div>
     </div>
     """
 
-    # 6. Report & PDF
+    # 7. Report & PDF
     reports = generate_mvm2_report(consensus_result, latex_text, ocr_conf)
     md_report = format_step_viewer(consensus_result)
     
@@ -150,7 +151,7 @@ def process_mvm2_pipeline(image, auto_enhance):
     
     final_flow = generate_flow_html("success")
     
-    return preview_img, latex_text, gauges_html, conf_bar, md_report, pdf_path, final_flow
+    yield preview_img, latex_text, gauges_html, conf_bar, md_report, pdf_path, final_flow
 
 # Build Interface
 with gr.Blocks(css=css_content, title="MVM²: Senior UI AI Dashboard") as demo: