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app.py

@@ -1,230 +1,230 @@
-import gradio as gr
-import os
-import time
-import cv2
-import numpy as np
-from PIL import Image
-import tempfile
-import json
-import re
-
-# Import consolidated modules
-from ocr_module import MVM2OCREngine
-from reasoning_engine import run_agent_orchestrator
-from verification_service import calculate_symbolic_score
-from consensus_fusion import evaluate_consensus
-from report_module import generate_mvm2_report, export_to_pdf
-from image_enhancing import ImageEnhancer
-from flow_module import generate_flow_html
-
-# Initialize Engines
-ocr_engine = MVM2OCREngine()
-enhancer = ImageEnhancer(sigma=1.2)
-
-# Load custom CSS
-with open("theme.css", "r") as f:
-    css_content = f.read()
-
-def create_gauge(label, value, color="#6366f1"):
-    """Generates an animated SVG circular gauge."""
-    percentage = max(0, min(100, value * 100))
-    dash_offset = 251.2 * (1 - percentage / 100)
-    return f"""
-    <div class="gauge-container">
-        <svg width="100" height="100" viewBox="0 0 100 100">
-            <circle class="circle-bg" cx="50" cy="50" r="40" />
-            <circle class="circle-progress" cx="50" cy="50" r="40" 
-                    stroke="{color}" stroke-dasharray="251.2" 
-                    stroke-dashoffset="{dash_offset}" 
-                    style="filter: drop-shadow(0 0 5px {color}88);"/>
-            <text x="50" y="55" text-anchor="middle" font-size="18" font-weight="bold" fill="white">{int(percentage)}%</text>
-        </svg>
-        <div style="font-size: 0.8em; color: #94a3b8; font-weight: 500;">{label}</div>
-    </div>
-    """
-
-def format_step_viewer(consensus_result):
-    """Formats the Reasoning Trace with Step-Level Consensus highlights."""
-    html = '<div style="display: flex; flex-direction: column; gap: 12px;">'
-    
-    # We aggregate steps from all agents for a collective view
-    agent_data = consensus_result.get("detail_scores", [])
-    
-    for agent in agent_data:
-        # Simulate step-level analysis for UI purposes: 
-        # In a real system, we'd have Score_j per step. Here we use the agent's overall score.
-        score = agent["Score_j"]
-        status_class = "step-valid" if score >= 0.7 else "step-warning"
-        icon = "✅" if score >= 0.7 else "⚠️"
-        glow_style = "box-shadow: 0 0 10px rgba(16, 185, 129, 0.2);" if score >= 0.7 else "box-shadow: 0 0 10px rgba(245, 158, 11, 0.2);"
-        
-        # Get matching agent response for trace
-        # (This assumes agent_responses were passed in or stored)
-        # For the UI, we'll just show the representative trace from valid agents
-        if not agent["is_hallucinating"] or score > 0.4:
-            html += f"""
-            <div class="glass-card reasoning-step {status_class}" style="{glow_style}">
-                <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 8px;">
-                    <span class="monospace" style="color: #6366f1; font-weight: 600;">{agent['agent']} Reasoning Path</span>
-                    <span style="font-size: 0.8em; background: rgba(0,0,0,0.3); padding: 2px 8px; border-radius: 4px;">Consensus: {score:.2f} {icon}</span>
-                </div>
-                <div style="font-size: 0.9em; line-height: 1.6; color: #cbd5e1;">
-                    {"<br>".join([f"• {step}" for step in agent.get('reasoning_trace', ['Processing...'])])}
-                </div>
-            </div>
-            """
-    html += "</div>"
-    return html
-
-def process_mvm2_pipeline(image, auto_enhance):
-    if image is None:
-        return None, "Please upload an image.", None, "", None, ""
-    
-    # 1. Preprocessing & Preview
-    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))
-    
-    # 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
-    
-    # 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
-    consensus_result = evaluate_consensus(agent_responses, ocr_confidence=ocr_conf)
-    
-    # Map traces to detail_scores for UI
-    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
-    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"]])
-    
-    gauges_html = f"""
-    <div class="signal-panel">
-        {create_gauge("Symbolic", avg_v_sym, "#10b981")}
-        {create_gauge("Logic", avg_l_logic, "#6366f1")}
-        {create_gauge("Classifier", avg_c_clf, "#8b5cf6")}
-    </div>
-    """
-    
-    # Final Calibration Bar
-    winner = consensus_result["winning_score"]
-    calibrated_conf = winner * (0.9 + 0.1 * ocr_conf)
-    conf_bar = f"""
-    <div style="margin-top: 20px;">
-        <div style="display: flex; justify-content: space-between; margin-bottom: 8px;">
-            <span style="font-weight: 600; color: #94a3b8;">Final Confidence Calibration</span>
-            <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>
-    </div>
-    """
-
-    # 6. Report & PDF
-    reports = generate_mvm2_report(consensus_result, latex_text, ocr_conf)
-    md_report = format_step_viewer(consensus_result)
-    
-    pdf_path = os.path.join(tempfile.gettempdir(), f'MVM2_Report_{reports["report_id"]}.pdf')
-    export_to_pdf(json.loads(reports['json']), pdf_path)
-    
-    final_flow = generate_flow_html("success")
-    
-    return 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:
-    with gr.Row(elem_id="header-row"):
-        gr.Markdown(
-            """
-            <div style="text-align: center; padding: 20px 0;">
-                <h1 style="font-size: 2.5em; margin-bottom: 0;">MVM² <span style="color: #6366f1;">Neuro-Symbolic</span></h1>
-                <p style="color: #94a3b8; font-size: 1.1em; margin-top: 8px;">High-Fidelity Mathematical Verification & Consensus Dashboard</p>
-            </div>
-            """
-        )
-    
-    with gr.Row():
-        # --- LEFT PANEL: Upload & Preview ---
-        with gr.Column(scale=1, variant="panel"):
-            gr.Markdown("### 📤 Input Intelligence")
-            input_img = gr.Image(type="pil", label="Capture Solution", elem_classes="glass-card")
-            enhance_toggle = gr.Checkbox(label="Enable Opti-Scan Preprocessing", value=True)
-            run_btn = gr.Button("INITIALIZE VERIFICATION", variant="primary", elem_classes="download-btn")
-            
-            gr.Markdown("#### 🔍 Preprocessing Preview")
-            preview_output = gr.Image(label="Enhanced Signal", interactive=False, elem_classes="preview-img")
-
-        # --- CENTER STAGE: Canvas ---
-        with gr.Column(scale=2):
-            with gr.Tabs():
-                with gr.TabItem("Solver Visualization"):
-                    gr.Markdown("### 🎨 MVM² Verification Canvas")
-                    with gr.Group(elem_classes="glass-card"):
-                        canvas_latex = gr.Textbox(label="Canonical LaTeX Transcription", lines=2, interactive=False, elem_classes="monospace")
-                        calib_bar_html = gr.HTML()
-                    
-                    gr.Markdown("### 🪜 Dynamic Reasoning Trace")
-                    trace_html = gr.HTML()
-
-                with gr.TabItem("How It Works (Architecture Flow)"):
-                    gr.Markdown("### 🚀 Real-Time Pipeline Visualization")
-                    flow_view = gr.HTML(generate_flow_html("idle"))
-                    gr.Markdown(
-                        """
-                        **Pipeline Phases:**
-                        1. **Enhance:** CLAHE & Gaussian Blur noise reduction.
-                        2. **OCR:** Pix2Text LaTeX structure reconstruction.
-                        3. **Reasoning:** Quad-agent parallel logic processing.
-                        4. **Verification:** SymPy deterministic symbolic check.
-                        5. **Consensus:** Multi-signal weighted confidence fusion.
-                        """
-                    )
-
-        # --- RIGHT PANEL: Signal Intel ---
-        with gr.Column(scale=1, variant="panel"):
-            gr.Markdown("### ⚡ Signal Intelligence")
-            with gr.Group(elem_classes="glass-card"):
-                signal_gauges = gr.HTML()
-            
-            gr.Markdown("### 📄 Educational Assessment")
-            download_btn = gr.File(label="Download Diagnostic PDF", elem_classes="download-btn")
-            
-            with gr.Group(elem_classes="glass-card status-box"):
-                gr.Markdown(
-                    """
-                    **System Status**
-                    - Pix2Text VLM: `Online`
-                    - SymPy Core: `1.12.0`
-                    - Consensus: `4-Agent parallel`
-                    """
-                )
-
-    run_btn.click(
-        fn=process_mvm2_pipeline,
-        inputs=[input_img, enhance_toggle],
-        outputs=[preview_output, canvas_latex, signal_gauges, calib_bar_html, trace_html, download_btn, flow_view]
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+import gradio as gr
+import os
+import time
+import cv2
+import numpy as np
+from PIL import Image
+import tempfile
+import json
+import re
+
+# Import consolidated modules
+from ocr_module import MVM2OCREngine
+from reasoning_engine import run_agent_orchestrator
+from verification_service import calculate_symbolic_score
+from consensus_fusion import evaluate_consensus
+from report_module import generate_mvm2_report, export_to_pdf
+from image_enhancing import ImageEnhancer
+from flow_module import generate_flow_html
+
+# Initialize Engines
+ocr_engine = MVM2OCREngine()
+enhancer = ImageEnhancer(sigma=1.2)
+
+# Load custom CSS
+with open("theme.css", "r") as f:
+    css_content = f.read()
+
+def create_gauge(label, value, color="#6366f1"):
+    """Generates an animated SVG circular gauge."""
+    percentage = max(0, min(100, value * 100))
+    dash_offset = 251.2 * (1 - percentage / 100)
+    return f"""
+    <div class="gauge-container">
+        <svg width="100" height="100" viewBox="0 0 100 100">
+            <circle class="circle-bg" cx="50" cy="50" r="40" />
+            <circle class="circle-progress" cx="50" cy="50" r="40" 
+                    stroke="{color}" stroke-dasharray="251.2" 
+                    stroke-dashoffset="{dash_offset}" 
+                    style="filter: drop-shadow(0 0 5px {color}88);"/>
+            <text x="50" y="55" text-anchor="middle" font-size="18" font-weight="bold" fill="white">{int(percentage)}%</text>
+        </svg>
+        <div style="font-size: 0.8em; color: #94a3b8; font-weight: 500;">{label}</div>
+    </div>
+    """
+
+def format_step_viewer(consensus_result):
+    """Formats the Reasoning Trace with Step-Level Consensus highlights."""
+    html = '<div style="display: flex; flex-direction: column; gap: 12px;">'
+    
+    # We aggregate steps from all agents for a collective view
+    agent_data = consensus_result.get("detail_scores", [])
+    
+    for agent in agent_data:
+        # Simulate step-level analysis for UI purposes: 
+        # In a real system, we'd have Score_j per step. Here we use the agent's overall score.
+        score = agent["Score_j"]
+        status_class = "step-valid" if score >= 0.7 else "step-warning"
+        icon = "✅" if score >= 0.7 else "⚠️"
+        glow_style = "box-shadow: 0 0 10px rgba(16, 185, 129, 0.2);" if score >= 0.7 else "box-shadow: 0 0 10px rgba(245, 158, 11, 0.2);"
+        
+        # Get matching agent response for trace
+        # (This assumes agent_responses were passed in or stored)
+        # For the UI, we'll just show the representative trace from valid agents
+        if not agent["is_hallucinating"] or score > 0.4:
+            html += f"""
+            <div class="glass-card reasoning-step {status_class}" style="{glow_style}">
+                <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 8px;">
+                    <span class="monospace" style="color: #6366f1; font-weight: 600;">{agent['agent']} Reasoning Path</span>
+                    <span style="font-size: 0.8em; background: rgba(0,0,0,0.3); padding: 2px 8px; border-radius: 4px;">Consensus: {score:.2f} {icon}</span>
+                </div>
+                <div style="font-size: 0.9em; line-height: 1.6; color: #cbd5e1;">
+                    {"<br>".join([f"• {step}" for step in agent.get('reasoning_trace', ['Processing...'])])}
+                </div>
+            </div>
+            """
+    html += "</div>"
+    return html
+
+def process_mvm2_pipeline(image, auto_enhance):
+    if image is None:
+        return None, "Please upload an image.", None, "", None, ""
+    
+    # 1. Preprocessing & Preview
+    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))
+    
+    # 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
+    
+    # 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
+    consensus_result = evaluate_consensus(agent_responses, ocr_confidence=ocr_conf)
+    
+    # Map traces to detail_scores for UI
+    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
+    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"]])
+    
+    gauges_html = f"""
+    <div class="signal-panel">
+        {create_gauge("Symbolic", avg_v_sym, "#10b981")}
+        {create_gauge("Logic", avg_l_logic, "#6366f1")}
+        {create_gauge("Classifier", avg_c_clf, "#8b5cf6")}
+    </div>
+    """
+    
+    # Final Calibration Bar
+    winner = consensus_result["winning_score"]
+    calibrated_conf = winner * (0.9 + 0.1 * ocr_conf)
+    conf_bar = f"""
+    <div style="margin-top: 20px;">
+        <div style="display: flex; justify-content: space-between; margin-bottom: 8px;">
+            <span style="font-weight: 600; color: #94a3b8;">Final Confidence Calibration</span>
+            <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>
+    </div>
+    """
+
+    # 6. Report & PDF
+    reports = generate_mvm2_report(consensus_result, latex_text, ocr_conf)
+    md_report = format_step_viewer(consensus_result)
+    
+    pdf_path = os.path.join(tempfile.gettempdir(), f'MVM2_Report_{reports["report_id"]}.pdf')
+    export_to_pdf(json.loads(reports['json']), pdf_path)
+    
+    final_flow = generate_flow_html("success")
+    
+    return 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:
+    with gr.Row(elem_id="header-row"):
+        gr.Markdown(
+            """
+            <div style="text-align: center; padding: 20px 0;">
+                <h1 style="font-size: 2.5em; margin-bottom: 0;">MVM² <span style="color: #6366f1;">Neuro-Symbolic</span></h1>
+                <p style="color: #94a3b8; font-size: 1.1em; margin-top: 8px;">High-Fidelity Mathematical Verification & Consensus Dashboard</p>
+            </div>
+            """
+        )
+    
+    with gr.Row():
+        # --- LEFT PANEL: Upload & Preview ---
+        with gr.Column(scale=1, variant="panel"):
+            gr.Markdown("### 📤 Input Intelligence")
+            input_img = gr.Image(type="pil", label="Capture Solution", elem_classes="glass-card")
+            enhance_toggle = gr.Checkbox(label="Enable Opti-Scan Preprocessing", value=True)
+            run_btn = gr.Button("INITIALIZE VERIFICATION", variant="primary", elem_classes="download-btn")
+            
+            gr.Markdown("#### 🔍 Preprocessing Preview")
+            preview_output = gr.Image(label="Enhanced Signal", interactive=False, elem_classes="preview-img")
+
+        # --- CENTER STAGE: Canvas ---
+        with gr.Column(scale=2):
+            with gr.Tabs():
+                with gr.TabItem("Solver Visualization"):
+                    gr.Markdown("### 🎨 MVM² Verification Canvas")
+                    with gr.Group(elem_classes="glass-card"):
+                        canvas_latex = gr.Textbox(label="Canonical LaTeX Transcription", lines=2, interactive=False, elem_classes="monospace")
+                        calib_bar_html = gr.HTML()
+                    
+                    gr.Markdown("### 🪜 Dynamic Reasoning Trace")
+                    trace_html = gr.HTML()
+
+                with gr.TabItem("How It Works (Architecture Flow)"):
+                    gr.Markdown("### 🚀 Real-Time Pipeline Visualization")
+                    flow_view = gr.HTML(generate_flow_html("idle"))
+                    gr.Markdown(
+                        """
+                        **Pipeline Phases:**
+                        1. **Enhance:** CLAHE & Gaussian Blur noise reduction.
+                        2. **OCR:** Pix2Text LaTeX structure reconstruction.
+                        3. **Reasoning:** Quad-agent parallel logic processing.
+                        4. **Verification:** SymPy deterministic symbolic check.
+                        5. **Consensus:** Multi-signal weighted confidence fusion.
+                        """
+                    )
+
+        # --- RIGHT PANEL: Signal Intel ---
+        with gr.Column(scale=1, variant="panel"):
+            gr.Markdown("### ⚡ Signal Intelligence")
+            with gr.Group(elem_classes="glass-card"):
+                signal_gauges = gr.HTML()
+            
+            gr.Markdown("### 📄 Educational Assessment")
+            download_btn = gr.File(label="Download Diagnostic PDF", elem_classes="download-btn")
+            
+            with gr.Group(elem_classes="glass-card status-box"):
+                gr.Markdown(
+                    """
+                    **System Status**
+                    - Pix2Text VLM: `Online`
+                    - SymPy Core: `1.12.0`
+                    - Consensus: `4-Agent parallel`
+                    """
+                )
+
+    run_btn.click(
+        fn=process_mvm2_pipeline,
+        inputs=[input_img, enhance_toggle],
+        outputs=[preview_output, canvas_latex, signal_gauges, calib_bar_html, trace_html, download_btn, flow_view]
+    )
+
+if __name__ == "__main__":
+    demo.launch()