Update apps/flask_server.py

apps/flask_server.py

@@ -1,436 +1,431 @@
-"""
-DCRM Analysis Flask API - Three Phase Support
-==============================================
-Flask API wrapper for the DCRM analysis pipeline.
-Accepts 3 CSV uploads (R, Y, B phases) via POST and returns comprehensive JSON analysis.
-
-Endpoint: POST /api/circuit-breakers/{breaker_id}/tests/upload-three-phase
-"""
-
-import os
-import json
-import traceback
-import uuid
-from datetime import datetime, timezone
-import sys
-import concurrent.futures
-
-# Add project root to sys.path to allow importing from core
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-
-# Previous Name: flask_app.py
-from flask import Flask, request, jsonify
-from flask_cors import CORS
-from werkzeug.utils import secure_filename
-import pandas as pd
-from io import StringIO
-
-# Load environment variables
-from dotenv import load_dotenv
-load_dotenv()
-
-# Ensure API key is set
-if not os.getenv("GOOGLE_API_KEY"):
-    print("WARNING: GOOGLE_API_KEY not found in environment variables. Please check your .env file.")
-
-from langchain_google_genai import ChatGoogleGenerativeAI
-from core.calculators.kpi import calculate_kpis
-from core.calculators.cbhi import compute_cbhi
-from core.signal.phases import analyze_dcrm_data
-from core.engines.rules import analyze_dcrm_advanced
-from core.agents.diagnosis import detect_fault, standardize_input
-from core.utils.report_generator import generate_dcrm_json
-from core.agents.recommendation import generate_recommendations
-
-# Optional ViT Model
-try:
-    from core.models.vit_classifier import predict_dcrm_image, plot_resistance_for_vit
-    VIT_AVAILABLE = True
-except Exception as e:
-    print(f"ViT Model not available: {e}")
-    VIT_AVAILABLE = False
-    predict_dcrm_image = None
-    plot_resistance_for_vit = None
-
-# =============================================================================
-# CONFIGURATION - CHANGE THIS URL AFTER DEPLOYMENT
-# =============================================================================
-DEPLOYMENT_URL = "http://localhost:5000"  # Change this to your deployed URL
-# Example: DEPLOYMENT_URL = "https://your-domain.com"
-# =============================================================================
-
-# Initialize Flask app
-app = Flask(__name__)
-CORS(app)  # Enable CORS for frontend access
-
-def get_llm(api_key=None):
-    """
-    Factory function to create an LLM instance with a specific API key.
-    If no key is provided, falls back to the default GOOGLE_API_KEY.
-    """
-    if not api_key:
-        api_key = os.getenv("GOOGLE_API_KEY")
-    
-    if not api_key:
-        raise ValueError("No Google API Key provided and GOOGLE_API_KEY not found in env.")
-        
-    return ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0, google_api_key=api_key)
-
-
-def process_single_phase_csv(args):
-    """
-    Process a single phase CSV through the complete DCRM pipeline.
-    Designed to be run in a separate thread.
-    
-    Args:
-        args: Tuple containing (df, breaker_id, api_key, phase_name)
-        
-    Returns:
-        dict: Complete analysis results for one phase
-    """
-    df, breaker_id, api_key, phase_name = args
-    
-    try:
-        print(f"[{phase_name.upper()}] Starting processing with key ending in ...{api_key[-4:] if api_key else 'None'}")
-        
-        # Initialize local LLM for this thread
-        llm = get_llm(api_key)
-        
-        # 1. Calculate KPIs
-        kpi_results = calculate_kpis(df)
-        kpis = kpi_results['kpis']
-        
-        # 2. Phase Segmentation (AI-based)
-        phase_analysis_result = analyze_dcrm_data(df, llm)
-        
-        # 3. Prepare KPIs for Rule Engine and AI Agent
-        raj_kpis = {
-            "Closing Time (ms)": kpis.get('closing_time'),
-            "Opening Time (ms)": kpis.get('opening_time'),
-            "Contact Speed (m/s)": kpis.get('contact_speed'),
-            "DLRO Value (µΩ)": kpis.get('dlro'),
-            "Peak Resistance (µΩ)": kpis.get('peak_resistance'),
-            "Peak Close Coil Current (A)": kpis.get('peak_close_coil'),
-            "Peak Trip Coil 1 Current (A)": kpis.get('peak_trip_coil_1'),
-            "Peak Trip Coil 2 Current (A)": kpis.get('peak_trip_coil_2'),
-            "SF6 Pressure (bar)": kpis.get('sf6_pressure'),
-            "Ambient Temperature (°C)": kpis.get('ambient_temp'),
-            "Main Wipe (mm)": kpis.get('main_wipe'),
-            "Arc Wipe (mm)": kpis.get('arc_wipe'),
-            "Contact Travel Distance (mm)": kpis.get('contact_travel')
-        }
-        
-        raj_ai_kpis = {
-            "kpis": [
-                {"name": "Closing Time", "unit": "ms", "value": kpis.get('closing_time')},
-                {"name": "Opening Time", "unit": "ms", "value": kpis.get('opening_time')},
-                {"name": "DLRO Value", "unit": "µΩ", "value": kpis.get('dlro')},
-                {"name": "Peak Resistance", "unit": "µΩ", "value": kpis.get('peak_resistance')},
-                {"name": "Contact Speed", "unit": "m/s", "value": kpis.get('contact_speed')},
-                {"name": "Peak Close Coil Current", "unit": "A", "value": kpis.get('peak_close_coil')},
-                {"name": "Peak Trip Coil 1 Current", "unit": "A", "value": kpis.get('peak_trip_coil_1')},
-                {"name": "Peak Trip Coil 2 Current", "unit": "A", "value": kpis.get('peak_trip_coil_2')},
-                {"name": "SF6 Pressure", "unit": "bar", "value": kpis.get('sf6_pressure')},
-                {"name": "Ambient Temperature", "unit": "°C", "value": kpis.get('ambient_temp')}
-            ]
-        }
-        
-        # 4. Standardize resistance data for Rule Engine
-        temp_df = df[['Resistance']].copy()
-        if len(temp_df) < 401:
-            last_val = temp_df.iloc[-1, 0]
-            padding = pd.DataFrame({'Resistance': [last_val] * (401 - len(temp_df))})
-            temp_df = pd.concat([temp_df, padding], ignore_index=True)
-        
-        std_df = standardize_input(temp_df)
-        row_values = std_df.iloc[0].values.tolist()
-        
-        # 5. Run Rule Engine Analysis
-        rule_engine_result = analyze_dcrm_advanced(row_values, raj_kpis)
-        
-        # 6. Run AI Agent Analysis with error handling
-        try:
-            ai_agent_result = detect_fault(df, raj_ai_kpis)
-            print(f"[{phase_name.upper()}] AI Agent analysis completed successfully")
-        except Exception as e:
-            print(f"[{phase_name.upper()}] AI Agent failed: {e}. Using fallback.")
-            # Fallback: Use rule engine result as AI result
-            ai_agent_result = {
-                "Fault_Detection": rule_engine_result.get("Fault_Detection", []),
-                "overall_health_assessment": rule_engine_result.get("overall_health_assessment", {}),
-                "classifications": rule_engine_result.get("classifications", [])
-            }
-        
-        # 7. Run ViT Model (if available)
-        vit_result = None
-        vit_plot_path = f"temp_vit_plot_{phase_name}_{uuid.uuid4().hex[:8]}.png" # Unique path for parallel safety
-        
-        plot_generated = False
-        try:
-            if plot_resistance_for_vit and plot_resistance_for_vit(df, vit_plot_path):
-                plot_generated = True
-        except Exception as e:
-            print(f"[{phase_name.upper()}] ViT Plot generation failed: {e}")
-        
-        if plot_generated and VIT_AVAILABLE and predict_dcrm_image:
-            try:
-                # Pass API key to ViT as well if needed, though currently it might use env var
-                # The updated vit_classifier uses requests to a deployed model, so API key is for Gemini part
-                vit_class, vit_conf, vit_details = predict_dcrm_image(vit_plot_path, api_key=api_key)
-                if vit_class:
-                    vit_result = {
-                        "class": vit_class,
-                        "confidence": vit_conf,
-                        "details": vit_details
-                    }
-            except Exception as e:
-                print(f"[{phase_name.upper()}] ViT Prediction failed: {e}")
-            finally:
-                # Cleanup temp file
-                if os.path.exists(vit_plot_path):
-                    try:
-                        os.remove(vit_plot_path)
-                    except:
-                        pass
-        
-        # 8. Calculate CBHI Score
-        cbhi_phase_data = {}
-        if 'phaseWiseAnalysis' in phase_analysis_result:
-            for phase in phase_analysis_result['phaseWiseAnalysis']:
-                p_name = f"Phase {phase.get('phaseNumber')}"
-                cbhi_phase_data[p_name] = {
-                    "status": phase.get('status', 'Unknown'),
-                    "confidence": phase.get('confidence', 0)
-                }
-        
-        cbhi_score = compute_cbhi(raj_ai_kpis['kpis'], ai_agent_result, cbhi_phase_data)
-        
-        # 9. Generate Recommendations with error handling
-        try:
-            recommendations = generate_recommendations(
-                kpis=kpis,
-                cbhi_score=cbhi_score,
-                rule_faults=rule_engine_result.get("Fault_Detection", []),
-                ai_faults=ai_agent_result.get("Fault_Detection", []),
-                llm=llm
-            )
-            print(f"[{phase_name.upper()}] Recommendations generated successfully")
-        except Exception as e:
-            print(f"[{phase_name.upper()}] Recommendations failed: {e}. Using fallback.")
-            # Fallback: Create basic recommendations from rule engine
-            recommendations = {
-                "maintenanceActions": [],
-                "futureFaultsPdf": []
-            }
-            # Extract from rule faults
-            for fault in rule_engine_result.get("Fault_Detection", []):
-                if fault.get("Severity") in ["High", "Critical"]:
-                    recommendations["maintenanceActions"].append({
-                        "action": f"Address {fault.get('defect_name')}",
-                        "priority": "High",
-                        "timeframe": "Immediate"
-                    })
-        
-        # 10. Generate Final JSON Report with error handling
-        try:
-            full_report = generate_dcrm_json(
-                df=df,
-                kpis=kpis,
-                cbhi_score=cbhi_score,
-                rule_result=rule_engine_result,
-                ai_result=ai_agent_result,
-                llm=llm,
-                vit_result=vit_result,
-                phase_analysis_result=phase_analysis_result,
-                recommendations=recommendations
-            )
-            print(f"[{phase_name.upper()}] Final report generated successfully")
-        except Exception as e:
-            print(f"[{phase_name.upper()}] Report generation failed: {e}. Using fallback.")
-            # Fallback: Create minimal valid report
-            full_report = {
-                "_id": f"fallback_{phase_name}_{uuid.uuid4().hex[:8]}",
-                "phase": phase_name,
-                "status": "partial_success",
-                "error": str(e),
-                "ruleBased_result": rule_engine_result,
-                "vitResult": vit_result,
-                "kpis": kpis,
-                "cbhi": {"score": cbhi_score},
-                "phaseWiseAnalysis": phase_analysis_result.get('phaseWiseAnalysis', [])
-            }
-        
-        print(f"[{phase_name.upper()}] Processing complete.")
-        return full_report
-
-    except Exception as e:
-        print(f"[{phase_name.upper()}] Error: {e}")
-        traceback.print_exc()
-        # Return a partial error result so the whole request doesn't fail
-        return {
-            "error": str(e),
-            "phase": phase_name
-        }
-
-
-@app.route('/')
-def root():
-    """Health check endpoint"""
-    return jsonify({
-        "status": "healthy",
-        "service": "DCRM Analysis Flask API",
-        "version": "2.1.0",
-        "deployment_url": DEPLOYMENT_URL
-    })
-
-
-@app.route('/api/health')
-def health_check():
-    """Detailed health check with component status"""
-    return jsonify({
-        "status": "healthy",
-        "components": {
-            "llm": "operational",
-            "vit_model": "available" if VIT_AVAILABLE else "unavailable",
-            "kpi_calculator": "operational",
-            "rule_engine": "operational",
-            "ai_agent": "operational",
-            "phase_analysis": "operational"
-        },
-        "deployment_url": DEPLOYMENT_URL
-    })
-
-
-@app.route('/api/circuit-breakers/<breaker_id>/tests/upload-three-phase', methods=['POST'])
-def analyze_three_phase_dcrm(breaker_id):
-    """
-    Analyze DCRM test data from 3 uploaded CSV files (R, Y, B phases).
-    Uses parallel processing with multiple API keys to speed up execution.
-    
-    Expected files in request:
-    - fileR: Red phase CSV
-    - fileY: Yellow phase CSV  
-    - fileB: Blue phase CSV
-    
-    Returns:
-    - Comprehensive JSON analysis report with combined three-phase results
-    """
-    
-    try:
-        # Validate files are present
-        if 'fileR' not in request.files or 'fileY' not in request.files or 'fileB' not in request.files:
-            return jsonify({
-                "error": "Missing required files",
-                "message": "All three phase files are required: fileR, fileY, fileB",
-                "received": list(request.files.keys())
-            }), 400
-        
-        fileR = request.files['fileR']
-        fileY = request.files['fileY']
-        fileB = request.files['fileB']
-        
-        # Validate file types
-        for file in [fileR, fileY, fileB]:
-            if not file.filename.endswith('.csv'):
-                return jsonify({
-                    "error": "Invalid file type",
-                    "message": "Only CSV files are accepted",
-                    "received": file.filename
-                }), 400
-        
-        # Prepare DataFrames
-        dfs = {}
-        for phase_name, file in [('r', fileR), ('y', fileY), ('b', fileB)]:
-            file.seek(0)
-            csv_string = file.read().decode('utf-8')
-            try:
-                df = pd.read_csv(StringIO(csv_string))
-                
-                # Basic validation
-                if len(df) < 100:
-                    raise ValueError(f"Insufficient data in {phase_name.upper()} phase")
-                
-                dfs[phase_name] = df
-            except Exception as e:
-                return jsonify({
-                    "error": f"Error reading {phase_name.upper()} CSV",
-                    "details": str(e)
-                }), 400
-
-        # Get API Keys
-        # Fallback to main key if specific ones aren't set
-        main_key = os.getenv("GOOGLE_API_KEY")
-        keys = {
-            'r': os.getenv("GOOGLE_API_KEY_1", main_key),
-            'y': os.getenv("GOOGLE_API_KEY_2", main_key),
-            'b': os.getenv("GOOGLE_API_KEY_3", main_key)
-        }
-        
-        # Prepare tasks
-        tasks = []
-        for phase in ['r', 'y', 'b']:
-            tasks.append((dfs[phase], breaker_id, keys[phase], phase))
-            
-        # Execute in parallel
-        results = {}
-        health_scores = []
-        
-        print("Starting parallel processing of 3 phases...")
-        with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
-            # Map tasks to futures
-            future_to_phase = {
-                executor.submit(process_single_phase_csv, task): task[3] 
-                for task in tasks
-            }
-            
-            for future in concurrent.futures.as_completed(future_to_phase):
-                phase = future_to_phase[future]
-                try:
-                    result = future.result()
-                    results[phase] = result
-                    if 'healthScore' in result:
-                        health_scores.append(result['healthScore'])
-                except Exception as exc:
-                    print(f'{phase} generated an exception: {exc}')
-                    results[phase] = {"error": str(exc)}
-
-        # Combine results into three-phase structure (removed breakerId and operator)
-        combined_result = {
-            "_id": str(uuid.uuid4()).replace('-', '')[:24],
-            "createdAt": datetime.now(timezone.utc).strftime("%a, %d %b %Y %H:%M:%S GMT"),
-            "healthScore": round(sum(health_scores) / len(health_scores), 1) if health_scores else 0,
-            "r": results.get('r', {}),
-            "y": results.get('y', {}),
-            "b": results.get('b', {})
-        }
-        
-        return jsonify(combined_result), 200
-    
-    except Exception as e:
-        # Log the full error for debugging
-        error_trace = traceback.format_exc()
-        print(f"ERROR in three-phase DCRM analysis: {error_trace}")
-        
-        # Return clean error to client
-        return jsonify({
-            "error": "Analysis failed",
-            "message": "An error occurred during DCRM analysis",
-            "error_type": type(e).__name__,
-            "error_details": str(e)
-        }), 500
-
-
-if __name__ == "__main__":
-    # Print all registered routes for debugging
-    print("Registered Routes:")
-    print(app.url_map)
-    
-    # Run the Flask app
-    # Run the Flask app
-    port = int(os.environ.get("PORT", 7860))
-    app.run(
-        host="0.0.0.0",
-        port=port,
-        debug=False, # Set debug to False for production
-        use_reloader=False
-    )
+"""
+DCRM Analysis Flask API - Three Phase Support
+==============================================
+Flask API wrapper for the DCRM analysis pipeline.
+Accepts 3 CSV uploads (R, Y, B phases) via POST and returns comprehensive JSON analysis.
+
+Endpoint: POST /api/circuit-breakers/{breaker_id}/tests/upload-three-phase
+"""
+
+import os
+import json
+import traceback
+import uuid
+from datetime import datetime, timezone
+import sys
+import concurrent.futures
+
+# Add project root to sys.path to allow importing from core
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+# Previous Name: flask_app.py
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+from werkzeug.utils import secure_filename
+import pandas as pd
+from io import StringIO
+
+# Load environment variables
+from dotenv import load_dotenv
+load_dotenv()
+
+# Ensure API key is set
+if not os.getenv("GOOGLE_API_KEY"):
+    print("WARNING: GOOGLE_API_KEY not found in environment variables. Please check your .env file.")
+
+from langchain_google_genai import ChatGoogleGenerativeAI
+from core.calculators.kpi import calculate_kpis
+from core.calculators.cbhi import compute_cbhi
+from core.signal.phases import analyze_dcrm_data
+from core.engines.rules import analyze_dcrm_advanced
+from core.agents.diagnosis import detect_fault, standardize_input
+from core.utils.report_generator import generate_dcrm_json
+from core.agents.recommendation import generate_recommendations
+
+# Optional ViT Model
+try:
+    from core.models.vit_classifier import predict_dcrm_image, plot_resistance_for_vit
+    VIT_AVAILABLE = True
+except Exception as e:
+    print(f"ViT Model not available: {e}")
+    VIT_AVAILABLE = False
+    predict_dcrm_image = None
+    plot_resistance_for_vit = None
+
+# =============================================================================
+# CONFIGURATION - CHANGE THIS URL AFTER DEPLOYMENT
+# =============================================================================
+DEPLOYMENT_URL = "http://localhost:5000"  # Change this to your deployed URL
+# Example: DEPLOYMENT_URL = "https://your-domain.com"
+# =============================================================================
+
+# Initialize Flask app
+app = Flask(__name__)
+CORS(app)  # Enable CORS for frontend access
+
+def get_llm(api_key=None):
+    """
+    Factory function to create an LLM instance with a specific API key.
+    If no key is provided, falls back to the default GOOGLE_API_KEY.
+    """
+    if not api_key:
+        api_key = os.getenv("GOOGLE_API_KEY")
+    
+    if not api_key:
+        raise ValueError("No Google API Key provided and GOOGLE_API_KEY not found in env.")
+        
+    return ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0, google_api_key=api_key)
+
+
+def process_single_phase_csv(args):
+    """
+    Process a single phase CSV through the complete DCRM pipeline.
+    Designed to be run in a separate thread.
+    
+    Args:
+        args: Tuple containing (df, breaker_id, api_key, phase_name)
+        
+    Returns:
+        dict: Complete analysis results for one phase
+    """
+    df, breaker_id, api_key, phase_name = args
+    
+    try:
+        print(f"[{phase_name.upper()}] Starting processing with key ending in ...{api_key[-4:] if api_key else 'None'}")
+        
+        # Initialize local LLM for this thread
+        llm = get_llm(api_key)
+        
+        # 1. Calculate KPIs
+        kpi_results = calculate_kpis(df)
+        kpis = kpi_results['kpis']
+        
+        # 2. Phase Segmentation (AI-based)
+        phase_analysis_result = analyze_dcrm_data(df, llm)
+        
+        # 3. Prepare KPIs for Rule Engine and AI Agent
+        raj_kpis = {
+            "Closing Time (ms)": kpis.get('closing_time'),
+            "Opening Time (ms)": kpis.get('opening_time'),
+            "Contact Speed (m/s)": kpis.get('contact_speed'),
+            "DLRO Value (µΩ)": kpis.get('dlro'),
+            "Peak Resistance (µΩ)": kpis.get('peak_resistance'),
+            "Peak Close Coil Current (A)": kpis.get('peak_close_coil'),
+            "Peak Trip Coil 1 Current (A)": kpis.get('peak_trip_coil_1'),
+            "Peak Trip Coil 2 Current (A)": kpis.get('peak_trip_coil_2'),
+            "SF6 Pressure (bar)": kpis.get('sf6_pressure'),
+            "Ambient Temperature (°C)": kpis.get('ambient_temp'),
+            "Main Wipe (mm)": kpis.get('main_wipe'),
+            "Arc Wipe (mm)": kpis.get('arc_wipe'),
+            "Contact Travel Distance (mm)": kpis.get('contact_travel')
+        }
+        
+        raj_ai_kpis = {
+            "kpis": [
+                {"name": "Closing Time", "unit": "ms", "value": kpis.get('closing_time')},
+                {"name": "Opening Time", "unit": "ms", "value": kpis.get('opening_time')},
+                {"name": "DLRO Value", "unit": "µΩ", "value": kpis.get('dlro')},
+                {"name": "Peak Resistance", "unit": "µΩ", "value": kpis.get('peak_resistance')},
+                {"name": "Contact Speed", "unit": "m/s", "value": kpis.get('contact_speed')},
+                {"name": "Peak Close Coil Current", "unit": "A", "value": kpis.get('peak_close_coil')},
+                {"name": "Peak Trip Coil 1 Current", "unit": "A", "value": kpis.get('peak_trip_coil_1')},
+                {"name": "Peak Trip Coil 2 Current", "unit": "A", "value": kpis.get('peak_trip_coil_2')},
+                {"name": "SF6 Pressure", "unit": "bar", "value": kpis.get('sf6_pressure')},
+                {"name": "Ambient Temperature", "unit": "°C", "value": kpis.get('ambient_temp')}
+            ]
+        }
+        
+        # 4. Standardize resistance data for Rule Engine
+        temp_df = df[['Resistance']].copy()
+        if len(temp_df) < 401:
+            last_val = temp_df.iloc[-1, 0]
+            padding = pd.DataFrame({'Resistance': [last_val] * (401 - len(temp_df))})
+            temp_df = pd.concat([temp_df, padding], ignore_index=True)
+        
+        std_df = standardize_input(temp_df)
+        row_values = std_df.iloc[0].values.tolist()
+        
+        # 5. Run Rule Engine Analysis
+        rule_engine_result = analyze_dcrm_advanced(row_values, raj_kpis)
+        
+        # 6. Run AI Agent Analysis with error handling
+        try:
+            ai_agent_result = detect_fault(df, raj_ai_kpis)
+            print(f"[{phase_name.upper()}] AI Agent analysis completed successfully")
+        except Exception as e:
+            print(f"[{phase_name.upper()}] AI Agent failed: {e}. Using fallback.")
+            # Fallback: Use rule engine result as AI result
+            ai_agent_result = {
+                "Fault_Detection": rule_engine_result.get("Fault_Detection", []),
+                "overall_health_assessment": rule_engine_result.get("overall_health_assessment", {}),
+                "classifications": rule_engine_result.get("classifications", [])
+            }
+        
+        # 7. Run ViT Model (if available)
+        vit_result = None
+        vit_plot_path = f"temp_vit_plot_{phase_name}_{uuid.uuid4().hex[:8]}.png" # Unique path for parallel safety
+        
+        plot_generated = False
+        try:
+            if plot_resistance_for_vit and plot_resistance_for_vit(df, vit_plot_path):
+                plot_generated = True
+        except Exception as e:
+            print(f"[{phase_name.upper()}] ViT Plot generation failed: {e}")
+        
+        if plot_generated and VIT_AVAILABLE and predict_dcrm_image:
+            try:
+                # Pass API key to ViT as well if needed, though currently it might use env var
+                # The updated vit_classifier uses requests to a deployed model, so API key is for Gemini part
+                vit_class, vit_conf, vit_details = predict_dcrm_image(vit_plot_path, api_key=api_key)
+                if vit_class:
+                    vit_result = {
+                        "class": vit_class,
+                        "confidence": vit_conf,
+                        "details": vit_details
+                    }
+            except Exception as e:
+                print(f"[{phase_name.upper()}] ViT Prediction failed: {e}")
+            finally:
+                # Cleanup temp file
+                if os.path.exists(vit_plot_path):
+                    try:
+                        os.remove(vit_plot_path)
+                    except:
+                        pass
+        
+        # 8. Calculate CBHI Score
+        cbhi_phase_data = {}
+        if 'phaseWiseAnalysis' in phase_analysis_result:
+            for phase in phase_analysis_result['phaseWiseAnalysis']:
+                p_name = f"Phase {phase.get('phaseNumber')}"
+                cbhi_phase_data[p_name] = {
+                    "status": phase.get('status', 'Unknown'),
+                    "confidence": phase.get('confidence', 0)
+                }
+        
+        cbhi_score = compute_cbhi(raj_ai_kpis['kpis'], ai_agent_result, cbhi_phase_data)
+        
+        # 9. Generate Recommendations with error handling
+        try:
+            recommendations = generate_recommendations(
+                kpis=kpis,
+                cbhi_score=cbhi_score,
+                rule_faults=rule_engine_result.get("Fault_Detection", []),
+                ai_faults=ai_agent_result.get("Fault_Detection", []),
+                llm=llm
+            )
+            print(f"[{phase_name.upper()}] Recommendations generated successfully")
+        except Exception as e:
+            print(f"[{phase_name.upper()}] Recommendations failed: {e}. Using fallback.")
+            # Fallback: Create basic recommendations from rule engine
+            recommendations = {
+                "maintenanceActions": [],
+                "futureFaultsPdf": []
+            }
+            # Extract from rule faults
+            for fault in rule_engine_result.get("Fault_Detection", []):
+                if fault.get("Severity") in ["High", "Critical"]:
+                    recommendations["maintenanceActions"].append({
+                        "action": f"Address {fault.get('defect_name')}",
+                        "priority": "High",
+                        "timeframe": "Immediate"
+                    })
+        
+        # 10. Generate Final JSON Report with error handling
+        try:
+            full_report = generate_dcrm_json(
+                df=df,
+                kpis=kpis,
+                cbhi_score=cbhi_score,
+                rule_result=rule_engine_result,
+                ai_result=ai_agent_result,
+                llm=llm,
+                vit_result=vit_result,
+                phase_analysis_result=phase_analysis_result,
+                recommendations=recommendations
+            )
+            print(f"[{phase_name.upper()}] Final report generated successfully")
+        except Exception as e:
+            print(f"[{phase_name.upper()}] Report generation failed: {e}. Using fallback.")
+            # Fallback: Create minimal valid report
+            full_report = {
+                "_id": f"fallback_{phase_name}_{uuid.uuid4().hex[:8]}",
+                "phase": phase_name,
+                "status": "partial_success",
+                "error": str(e),
+                "ruleBased_result": rule_engine_result,
+                "vitResult": vit_result,
+                "kpis": kpis,
+                "cbhi": {"score": cbhi_score},
+                "phaseWiseAnalysis": phase_analysis_result.get('phaseWiseAnalysis', [])
+            }
+        
+        print(f"[{phase_name.upper()}] Processing complete.")
+        return full_report
+
+    except Exception as e:
+        print(f"[{phase_name.upper()}] Error: {e}")
+        traceback.print_exc()
+        # Return a partial error result so the whole request doesn't fail
+        return {
+            "error": str(e),
+            "phase": phase_name
+        }
+
+
+@app.route("/")
+def home():
+    return {
+        "service": "DCRM Analysis Flask API",
+        "status": "healthy",
+        "message": "Flask API running on Hugging Face!"
+    }
+
+
+
+@app.route('/api/health')
+def health_check():
+    """Detailed health check with component status"""
+    return jsonify({
+        "status": "healthy",
+        "components": {
+            "llm": "operational",
+            "vit_model": "available" if VIT_AVAILABLE else "unavailable",
+            "kpi_calculator": "operational",
+            "rule_engine": "operational",
+            "ai_agent": "operational",
+            "phase_analysis": "operational"
+        },
+        "deployment_url": DEPLOYMENT_URL
+    })
+
+
+@app.route('/api/circuit-breakers/<breaker_id>/tests/upload-three-phase', methods=['POST'])
+def analyze_three_phase_dcrm(breaker_id):
+    """
+    Analyze DCRM test data from 3 uploaded CSV files (R, Y, B phases).
+    Uses parallel processing with multiple API keys to speed up execution.
+    
+    Expected files in request:
+    - fileR: Red phase CSV
+    - fileY: Yellow phase CSV  
+    - fileB: Blue phase CSV
+    
+    Returns:
+    - Comprehensive JSON analysis report with combined three-phase results
+    """
+    
+    try:
+        # Validate files are present
+        if 'fileR' not in request.files or 'fileY' not in request.files or 'fileB' not in request.files:
+            return jsonify({
+                "error": "Missing required files",
+                "message": "All three phase files are required: fileR, fileY, fileB",
+                "received": list(request.files.keys())
+            }), 400
+        
+        fileR = request.files['fileR']
+        fileY = request.files['fileY']
+        fileB = request.files['fileB']
+        
+        # Validate file types
+        for file in [fileR, fileY, fileB]:
+            if not file.filename.endswith('.csv'):
+                return jsonify({
+                    "error": "Invalid file type",
+                    "message": "Only CSV files are accepted",
+                    "received": file.filename
+                }), 400
+        
+        # Prepare DataFrames
+        dfs = {}
+        for phase_name, file in [('r', fileR), ('y', fileY), ('b', fileB)]:
+            file.seek(0)
+            csv_string = file.read().decode('utf-8')
+            try:
+                df = pd.read_csv(StringIO(csv_string))
+                
+                # Basic validation
+                if len(df) < 100:
+                    raise ValueError(f"Insufficient data in {phase_name.upper()} phase")
+                
+                dfs[phase_name] = df
+            except Exception as e:
+                return jsonify({
+                    "error": f"Error reading {phase_name.upper()} CSV",
+                    "details": str(e)
+                }), 400
+
+        # Get API Keys
+        # Fallback to main key if specific ones aren't set
+        main_key = os.getenv("GOOGLE_API_KEY")
+        keys = {
+            'r': os.getenv("GOOGLE_API_KEY_1", main_key),
+            'y': os.getenv("GOOGLE_API_KEY_2", main_key),
+            'b': os.getenv("GOOGLE_API_KEY_3", main_key)
+        }
+        
+        # Prepare tasks
+        tasks = []
+        for phase in ['r', 'y', 'b']:
+            tasks.append((dfs[phase], breaker_id, keys[phase], phase))
+            
+        # Execute in parallel
+        results = {}
+        health_scores = []
+        
+        print("Starting parallel processing of 3 phases...")
+        with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
+            # Map tasks to futures
+            future_to_phase = {
+                executor.submit(process_single_phase_csv, task): task[3] 
+                for task in tasks
+            }
+            
+            for future in concurrent.futures.as_completed(future_to_phase):
+                phase = future_to_phase[future]
+                try:
+                    result = future.result()
+                    results[phase] = result
+                    if 'healthScore' in result:
+                        health_scores.append(result['healthScore'])
+                except Exception as exc:
+                    print(f'{phase} generated an exception: {exc}')
+                    results[phase] = {"error": str(exc)}
+
+        # Combine results into three-phase structure (removed breakerId and operator)
+        combined_result = {
+            "_id": str(uuid.uuid4()).replace('-', '')[:24],
+            "createdAt": datetime.now(timezone.utc).strftime("%a, %d %b %Y %H:%M:%S GMT"),
+            "healthScore": round(sum(health_scores) / len(health_scores), 1) if health_scores else 0,
+            "r": results.get('r', {}),
+            "y": results.get('y', {}),
+            "b": results.get('b', {})
+        }
+        
+        return jsonify(combined_result), 200
+    
+    except Exception as e:
+        # Log the full error for debugging
+        error_trace = traceback.format_exc()
+        print(f"ERROR in three-phase DCRM analysis: {error_trace}")
+        
+        # Return clean error to client
+        return jsonify({
+            "error": "Analysis failed",
+            "message": "An error occurred during DCRM analysis",
+            "error_type": type(e).__name__,
+            "error_details": str(e)
+        }), 500
+
+if __name__ == "__main__":
+    print("Registered Routes:")
+    print(app.url_map)
+
+    port = int(os.environ.get("PORT", 7860))
+    app.run(
+        host="0.0.0.0",
+        port=port,
+        debug=False,
+        use_reloader=False
+    )