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main.py

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+from fastapi import FastAPI, Form, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+import warnings
+import os
+import pandas as pd
+import numpy as np
+import matplotlib
+matplotlib.use('Agg')  # Use non-interactive backend
+import matplotlib.pyplot as plt
+import tensorflow as tf
+from sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder
+import uuid
+import asyncio
+
+# Optimize TensorFlow for faster loading
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
+tf.config.set_visible_devices([], 'GPU')  # Use CPU only for faster startup
+warnings.filterwarnings('ignore')
+
+app = FastAPI(title="EV Battery Management System")
+
+# Global variables to cache loaded models and data
+model = None
+scaler = None
+data = None
+label_encoders = {}
+numeric_features = []
+vehicle_type_to_model = {
+    "car": "Model A",
+    "bike": "Model B", 
+    "scooter": "Model C",
+    "bus": "Model D"
+}
+
+# Load models and data at startup
+@app.on_event("startup")
+async def load_models():
+    global model, scaler, data, label_encoders, numeric_features
+    
+    try:
+        print("Starting model and data loading...")
+        
+        # Define file paths - check multiple locations
+        csv_paths = [
+            "ev_battery_charging_data.csv",
+            "../ev_battery_charging_data.csv",
+            os.path.join(os.path.dirname(__file__), "ev_battery_charging_data.csv"),
+            os.path.join(os.path.dirname(__file__), "..", "ev_battery_charging_data.csv")
+        ]
+        
+        model_paths = [
+            "ev_bms_colab_model.h5",
+            "../ev_bms_colab_model.h5",
+            os.path.join(os.path.dirname(__file__), "ev_bms_colab_model.h5"),
+            os.path.join(os.path.dirname(__file__), "..", "ev_bms_colab_model.h5")
+        ]
+        
+        # Find CSV file
+        csv_file = None
+        for path in csv_paths:
+            if os.path.exists(path):
+                csv_file = path
+                print(f"Found CSV file: {path}")
+                break
+        
+        if csv_file is None:
+            print("Warning: CSV file not found, will use dummy data")
+        
+        # Find model file
+        model_file = None
+        for path in model_paths:
+            if os.path.exists(path):
+                model_file = path
+                print(f"Found model file: {path}")
+                break
+        
+        if model_file is None:
+            print("Warning: Model file not found, will use dummy model")
+        
+        # Load data if available
+        if csv_file and os.path.exists(csv_file):
+            print("Loading CSV data...")
+            data = pd.read_csv(csv_file)
+            data.dropna(inplace=True)
+            
+            # Handle categorical columns if they exist
+            categorical_columns = ['Charging Mode', 'Battery Type', 'EV Model']
+            existing_categorical = [col for col in categorical_columns if col in data.columns]
+            
+            if existing_categorical:
+                label_encoders = {col: LabelEncoder().fit(data[col]) for col in existing_categorical}
+                for col in existing_categorical:
+                    data[col] = label_encoders[col].transform(data[col])
+            
+            # Define numeric features
+            exclude_cols = existing_categorical + ['Optimal Charging Duration Class']
+            numeric_features = [col for col in data.columns if col not in exclude_cols]
+            
+            if numeric_features:
+                scaler = MinMaxScaler()
+                data[numeric_features] = scaler.fit_transform(data[numeric_features])
+                print(f"Processed {len(numeric_features)} numeric features")
+        else:
+            # Create dummy data if CSV not found
+            print("Creating dummy data...")
+            numeric_features = ['SOC (%)', 'Voltage (V)', 'Current (A)', 'Battery Temp (°C)', 
+                              'Ambient Temp (°C)', 'Charging Duration (min)', 
+                              'Degradation Rate (%)', 'Efficiency (%)', 'Charging Cycles']
+            
+            # Create dummy dataset
+            np.random.seed(42)
+            dummy_data = {}
+            for feature in numeric_features:
+                dummy_data[feature] = np.random.uniform(0, 100, 1000)
+            
+            data = pd.DataFrame(dummy_data)
+            scaler = MinMaxScaler()
+            data[numeric_features] = scaler.fit_transform(data[numeric_features])
+        
+        # Load model if available
+        if model_file and os.path.exists(model_file):
+            print("Loading TensorFlow model...")
+            model = tf.keras.models.load_model(model_file, compile=False)
+            print("Model loaded successfully!")
+        else:
+            print("Model file not found, predictions will use dummy data")
+        
+        print("Startup completed successfully!")
+        
+    except Exception as e:
+        print(f"Startup error: {str(e)}")
+        # Don't raise the error, just log it - the app can still run with dummy data
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Mount static files
+os.makedirs("static", exist_ok=True)
+app.mount("/static", StaticFiles(directory="static"), name="static")
+
+@app.get("/")
+async def root():
+    return {"message": "EV Battery Management System API", "status": "running"}
+
+@app.get("/health")
+async def health_check():
+    global model, data, scaler
+    return {
+        "status": "healthy",
+        "model_loaded": model is not None,
+        "data_loaded": data is not None,
+        "scaler_loaded": scaler is not None
+    }
+
+@app.get("/image/{filename}")
+async def get_image(filename: str):
+    """Serve images from static directory"""
+    file_path = os.path.join("static", filename)
+    if os.path.exists(file_path):
+        from fastapi.responses import FileResponse
+        return FileResponse(file_path, media_type="image/png")
+    raise HTTPException(status_code=404, detail="Image not found")
+
+@app.post("/predict/")
+async def predict(vehicle_type: str = Form(...)):
+    try:
+        print(f"Prediction request for vehicle type: {vehicle_type}")
+        
+        # Use global variables
+        global model, scaler, data, numeric_features
+        
+        # Validate vehicle type
+        if vehicle_type.lower() not in vehicle_type_to_model:
+            raise HTTPException(
+                status_code=400, 
+                detail=f"Invalid vehicle type. Valid types: {list(vehicle_type_to_model.keys())}"
+            )
+        
+        ev_model = vehicle_type_to_model[vehicle_type.lower()]
+        
+        # Get sample data (either from real data or generate dummy data)
+        if data is not None and len(data) > 0:
+            # Use real data
+            sample_idx = np.random.randint(0, len(data))
+            original = data.iloc[sample_idx][numeric_features].values
+        else:
+            # Generate dummy data
+            print("Using dummy data for prediction")
+            original = np.random.uniform(0.1, 0.9, len(numeric_features))
+        
+        # Make prediction
+        if model is not None and scaler is not None:
+            try:
+                # Scale input
+                original_reshaped = original.reshape(1, -1)
+                scaled_features = scaler.transform(original_reshaped)
+                
+                # Reshape for model if needed
+                if len(scaled_features.shape) == 2:
+                    scaled_features = scaled_features.reshape((1, scaled_features.shape[1], 1))
+                
+                # Make prediction
+                prediction_scaled = model.predict(scaled_features, verbose=0)
+                prediction = scaler.inverse_transform(prediction_scaled.reshape(1, -1)).flatten()
+            except Exception as model_error:
+                print(f"Model prediction error: {model_error}")
+                # Fallback to dummy prediction
+                prediction = original + np.random.uniform(-0.1, 0.1, len(original))
+        else:
+            # Generate dummy prediction
+            prediction = original + np.random.uniform(-0.1, 0.1, len(original))
+        
+        # Create visualization
+        try:
+            plt.figure(figsize=(12, 6))
+            plt.style.use('default')
+            
+            index = np.arange(len(numeric_features))
+            bar_width = 0.35
+            
+            bars1 = plt.bar(index - bar_width/2, original, bar_width, 
+                           label='Original', alpha=0.8, color='#2E86AB')
+            bars2 = plt.bar(index + bar_width/2, prediction, bar_width, 
+                           label='Predicted', alpha=0.8, color='#A23B72')
+            
+            plt.xlabel('Parameters', fontsize=12)
+            plt.ylabel('Values', fontsize=12)
+            plt.title(f"{vehicle_type.title()} - Battery Parameters: Original vs Predicted", fontsize=14)
+            plt.xticks(index, numeric_features, rotation=45, ha='right')
+            plt.legend(fontsize=12)
+            plt.grid(True, alpha=0.3)
+            
+            # Add value labels on bars
+            for bar in bars1:
+                height = bar.get_height()
+                plt.text(bar.get_x() + bar.get_width()/2., height,
+                        f'{height:.2f}', ha='center', va='bottom', fontsize=8)
+            
+            for bar in bars2:
+                height = bar.get_height()
+                plt.text(bar.get_x() + bar.get_width()/2., height,
+                        f'{height:.2f}', ha='center', va='bottom', fontsize=8)
+            
+            plt.tight_layout()
+            
+            # Save plot
+            plot_filename = f"{uuid.uuid4().hex}.png"
+            plot_path = os.path.join("static", plot_filename)
+            plt.savefig(plot_path, dpi=100, bbox_inches='tight', facecolor='white')
+            plt.close()
+            
+            print(f"Plot saved to: {plot_path}")
+            chart_url = f"/static/{plot_filename}"
+            
+        except Exception as plot_error:
+            print(f"Plot generation error: {plot_error}")
+            chart_url = "/static/placeholder.png"  # Use placeholder if plot fails
+        
+        # Prepare table data
+        rows = []
+        for i, col in enumerate(numeric_features):
+            original_val = float(original[i])
+            predicted_val = float(prediction[i])
+            difference_val = predicted_val - original_val
+            
+            rows.append({
+                "parameter": col,
+                "original": round(original_val, 4),
+                "predicted": round(predicted_val, 4),
+                "difference": round(difference_val, 4)
+            })
+        
+        print("Prediction completed successfully")
+        
+        return {
+            "status": "success",
+            "vehicle_type": vehicle_type,
+            "ev_model": ev_model,
+            "chart_url": chart_url,
+            "table_data": rows
+        }
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        print(f"Prediction error: {e}")
+        raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}")
+
+@app.get("/vehicle-types")
+async def get_vehicle_types():
+    return {"vehicle_types": list(vehicle_type_to_model.keys())}
+
+# Add a warmup endpoint
+@app.get("/warmup")
+async def warmup():
+    """Warmup endpoint to ensure models are loaded"""
+    global model, data, scaler
+    return {
+        "status": "ready",
+        "model_status": "loaded" if model is not None else "not_loaded",
+        "data_status": "loaded" if data is not None else "not_loaded",
+        "scaler_status": "loaded" if scaler is not None else "not_loaded"
+    }
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000, timeout_keep_alive=120)

requirements.txt

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+fastapi
+uvicorn
+numpy
+pandas
+tensorflow
+scikit-learn
+matplotlib
+python-multipart