Update app.py

app.py

@@ -1,452 +1,181 @@
-# ### app.py for vercel deployment #####
-
-# from fastapi import FastAPI, HTTPException
-# from pydantic import BaseModel
-# import joblib
-# import numpy as np
-# # import pandas as pd
-# import os
-
-# app = FastAPI(title="ML Model API", version="1.0.0")
-
-# # Load model and feature names
-# try:
-#     model = joblib.load('model/model.joblib')
-#     feature_names = joblib.load('model/feature_names.joblib')
-#     print("Model loaded successfully!")
-# except Exception as e:
-#     print(f"Error loading model: {e}")
-#     model = None
-#     feature_names = []
-
-# # Define input schema
-# class PredictionInput(BaseModel):
-#     sepal_length: float
-#     sepal_width: float
-#     petal_length: float
-#     petal_width: float
-
-# class BatchPredictionInput(BaseModel):
-#     data: list[list[float]]  # List of feature arrays
-
-# @app.get("/")
-# async def root():
-#     return {
-#         "message": "ML Model API is running!",
-#         "endpoints": {
-#             "health": "/health",
-#             "single_prediction": "/predict",
-#             "batch_prediction": "/predict-batch",
-#             "model_info": "/model-info"
-#         }
-#     }
-
-# @app.get("/health")
-# async def health_check():
-#     return {
-#         "status": "healthy",
-#         "model_loaded": model is not None,
-#         "model_type": "RandomForestClassifier" if model else "None"
-#     }
-
-# @app.get("/model-info")
-# async def model_info():
-#     if not model:
-#         raise HTTPException(status_code=500, detail="Model not loaded")
-    
-#     return {
-#         "model_type": str(type(model).__name__),
-#         "feature_names": feature_names,
-#         "n_features": len(feature_names),
-#         "n_classes": getattr(model, 'n_classes_', 'Unknown')
-#     }
-
-# @app.post("/predict")
-# async def predict_single(input_data: PredictionInput):
-#     if not model:
-#         raise HTTPException(status_code=500, detail="Model not loaded")
-    
-#     try:
-#         # Convert input to array
-#         features = np.array([
-#             input_data.sepal_length,
-#             input_data.sepal_width,
-#             input_data.petal_length,
-#             input_data.petal_width
-#         ]).reshape(1, -1)
-        
-#         # Make prediction
-#         prediction = model.predict(features)
-#         probabilities = model.predict_proba(features)
-        
-#         return {
-#             "prediction": int(prediction[0]),
-#             "probabilities": probabilities[0].tolist(),
-#             "class_names": ["setosa", "versicolor", "virginica"],  # Replace with your class names
-#             "input_features": input_data.dict()
-#         }
-#     except Exception as e:
-#         raise HTTPException(status_code=400, detail=f"Prediction error: {str(e)}")
-
-# @app.post("/predict-batch")
-# async def predict_batch(input_data: BatchPredictionInput):
-#     if not model:
-#         raise HTTPException(status_code=500, detail="Model not loaded")
-    
-#     try:
-#         # Convert to numpy array
-#         features = np.array(input_data.data)
-        
-#         # Validate input shape
-#         if features.shape[1] != len(feature_names):
-#             raise HTTPException(
-#                 status_code=400, 
-#                 detail=f"Expected {len(feature_names)} features, got {features.shape[1]}"
-#             )
-        
-#         # Make predictions
-#         predictions = model.predict(features)
-#         probabilities = model.predict_proba(features)
-        
-#         return {
-#             "predictions": predictions.tolist(),
-#             "probabilities": probabilities.tolist(),
-#             "batch_size": len(predictions)
-#         }
-#     except Exception as e:
-#         raise HTTPException(status_code=400, detail=f"Batch prediction error: {str(e)}")
-
-# # For local development
-# if __name__ == "__main__":
-#     import uvicorn
-#     uvicorn.run(app, host="0.0.0.0", port=8000)
-
-
-############ Via HuggingFace ################
-# from fastapi import FastAPI, HTTPException
-# from pydantic import BaseModel
-# from huggingface_hub import hf_hub_download
-# import joblib
-# import numpy as np
-# import os
-
-# app = FastAPI(title="ML Model API on Hugging Face", version="1.0.0")
-
-# # Hugging Face model repository
-# HF_REPO_ID = "SagarChhabriya/ml-model-api"  
-# MODEL_FILENAME = "model.joblib"
-# FEATURE_NAMES_FILENAME = "feature_names.joblib"
-
-# # Load model from Hugging Face Hub
-# def load_model_from_hf():
-#     try:
-#         print("📥 Downloading model from Hugging Face Hub...")
-        
-#         # Download model file
-#         model_path = hf_hub_download(
-#             repo_id=HF_REPO_ID,
-#             filename=MODEL_FILENAME
-#         )
-        
-#         # Download feature names file
-#         feature_names_path = hf_hub_download(
-#             repo_id=HF_REPO_ID,
-#             filename=FEATURE_NAMES_FILENAME
-#         )
-        
-#         # Load files
-#         model = joblib.load(model_path)
-#         feature_names = joblib.load(feature_names_path)
-        
-#         print("Model and feature names loaded successfully from Hugging Face!")
-#         return model, feature_names
-        
-#     except Exception as e:
-#         print(f"Error loading from Hugging Face: {e}")
-#         return None, []
-
-# # Load model on startup
-# model, feature_names = load_model_from_hf()
-
-# # Define input schema
-# class PredictionInput(BaseModel):
-#     sepal_length: float
-#     sepal_width: float
-#     petal_length: float
-#     petal_width: float
-
-# class BatchPredictionInput(BaseModel):
-#     data: list[list[float]]  # List of feature arrays
-
-# @app.get("/")
-# async def root():
-#     return {
-#         "message": "ML Model API deployed on Hugging Face Spaces! 🚀",
-#         "endpoints": {
-#             "health": "/health",
-#             "single_prediction": "/predict",
-#             "batch_prediction": "/predict-batch",
-#             "model_info": "/model-info",
-#             "docs": "/docs"
-#         },
-#         "model_source": "Hugging Face Hub",
-#         "repository": HF_REPO_ID
-#     }
-
-# @app.get("/health")
-# async def health_check():
-#     return {
-#         "status": "healthy",
-#         "model_loaded": model is not None,
-#         "model_type": "RandomForestClassifier" if model else "None",
-#         "features_loaded": len(feature_names) > 0
-#     }
-
-# @app.get("/model-info")
-# async def model_info():
-#     if not model:
-#         raise HTTPException(status_code=500, detail="Model not loaded")
-    
-#     return {
-#         "model_type": str(type(model).__name__),
-#         "feature_names": feature_names,
-#         "n_features": len(feature_names),
-#         "n_classes": getattr(model, 'n_classes_', 'Unknown'),
-#         "source": HF_REPO_ID
-#     }
-
-# @app.post("/predict")
-# async def predict_single(input_data: PredictionInput):
-#     if not model:
-#         raise HTTPException(status_code=500, detail="Model not loaded")
-    
-#     try:
-#         # Convert input to array
-#         features = np.array([
-#             input_data.sepal_length,
-#             input_data.sepal_width,
-#             input_data.petal_length,
-#             input_data.petal_width
-#         ]).reshape(1, -1)
-        
-#         # Make prediction
-#         prediction = model.predict(features)
-#         probabilities = model.predict_proba(features)
-        
-#         return {
-#             "prediction": int(prediction[0]),
-#             "probabilities": probabilities[0].tolist(),
-#             "class_names": ["setosa", "versicolor", "virginica"],
-#             "input_features": input_data.dict(),
-#             "model_source": HF_REPO_ID
-#         }
-#     except Exception as e:
-#         raise HTTPException(status_code=400, detail=f"Prediction error: {str(e)}")
-
-# @app.post("/predict-batch")
-# async def predict_batch(input_data: BatchPredictionInput):
-#     if not model:
-#         raise HTTPException(status_code=500, detail="Model not loaded")
-    
-#     try:
-#         # Convert to numpy array
-#         features = np.array(input_data.data)
-        
-#         # Validate input shape
-#         if features.shape[1] != len(feature_names):
-#             raise HTTPException(
-#                 status_code=400, 
-#                 detail=f"Expected {len(feature_names)} features, got {features.shape[1]}"
-#             )
-        
-#         # Make predictions
-#         predictions = model.predict(features)
-#         probabilities = model.predict_proba(features)
-        
-#         return {
-#             "predictions": predictions.tolist(),
-#             "probabilities": probabilities.tolist(),
-#             "batch_size": len(predictions),
-#             "feature_names": feature_names,
-#             "model_source": HF_REPO_ID
-#         }
-#     except Exception as e:
-#         raise HTTPException(status_code=400, detail=f"Batch prediction error: {str(e)}")
-
-
-
-
-
-
-
-
-
-############################ HuggingFace + GitHub ###############################
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-import joblib
-import numpy as np
-import requests
-import tempfile
-import os
-
-app = FastAPI(title="ML Model API", version="1.0.0")
-
-# GitHub raw content URLs - using YOUR actual GitHub repository
-GITHUB_MODEL_URL = "https://raw.githubusercontent.com/SagarChhabriya/ml-model-api/main/model/model.joblib"
-GITHUB_FEATURES_URL = "https://raw.githubusercontent.com/SagarChhabriya/ml-model-api/main/model/feature_names.joblib"
-
-# Load model from GitHub
-def load_model_from_github():
-    try:
-        print("📥 Downloading model from GitHub...")
-        
-        # Download model file
-        model_response = requests.get(GITHUB_MODEL_URL)
-        model_response.raise_for_status()  # Raise error if download fails
-        
-        # Download feature names file  
-        features_response = requests.get(GITHUB_FEATURES_URL)
-        features_response.raise_for_status()
-        
-        # Save to temporary files
-        with tempfile.NamedTemporaryFile(delete=False, suffix='.joblib') as model_tmp:
-            model_tmp.write(model_response.content)
-            model_path = model_tmp.name
-        
-        with tempfile.NamedTemporaryFile(delete=False, suffix='.joblib') as features_tmp:
-            features_tmp.write(features_response.content)
-            features_path = features_tmp.name
-        
-        # Load the files
-        model = joblib.load(model_path)
-        feature_names = joblib.load(features_path)
-        
-        # Clean up temporary files
-        os.unlink(model_path)
-        os.unlink(features_path)
-        
-        print("✅ Model loaded successfully from GitHub!")
-        print(f"📊 Model type: {type(model).__name__}")
-        print(f"📈 Features: {feature_names}")
-        
-        return model, feature_names
-        
-    except Exception as e:
-        print(f"❌ Error loading from GitHub: {e}")
-        return None, []
-
-# Load model on startup
-model, feature_names = load_model_from_github()
-
-# Define input schema
-class PredictionInput(BaseModel):
-    sepal_length: float
-    sepal_width: float
-    petal_length: float
-    petal_width: float
-
-class BatchPredictionInput(BaseModel):
-    data: list[list[float]]
-
-@app.get("/")
-async def root():
-    return {
-        "message": "ML Model API deployed on Hugging Face Spaces! 🚀",
-        "endpoints": {
-            "health": "/health",
-            "single_prediction": "/predict", 
-            "batch_prediction": "/predict-batch",
-            "model_info": "/model-info",
-            "docs": "/docs"
-        },
-        "model_loaded": model is not None,
-        "model_source": "GitHub"
-    }
-
-@app.get("/health")
-async def health_check():
-    return {
-        "status": "healthy" if model else "unhealthy",
-        "model_loaded": model is not None,
-        "model_type": "RandomForestClassifier" if model else "None"
-    }
-
-@app.get("/model-info")
-async def model_info():
-    if not model:
-        raise HTTPException(status_code=500, detail="Model not loaded")
-    
-    return {
-        "model_type": str(type(model).__name__),
-        "feature_names": feature_names,
-        "n_features": len(feature_names),
-        "n_classes": getattr(model, 'n_classes_', 'Unknown')
-    }
-
-@app.post("/predict")
-async def predict_single(input_data: PredictionInput):
-    if not model:
-        raise HTTPException(status_code=500, detail="Model not loaded")
-    
-    try:
-        # Convert input to array
-        features = np.array([
-            input_data.sepal_length,
-            input_data.sepal_width,
-            input_data.petal_length,
-            input_data.petal_width
-        ]).reshape(1, -1)
-        
-        # Make prediction
-        prediction = model.predict(features)
-        probabilities = model.predict_proba(features)
-        
-        return {
-            "prediction": int(prediction[0]),
-            "probabilities": probabilities[0].tolist(),
-            "class_names": ["setosa", "versicolor", "virginica"],
-            "input_features": input_data.dict()
-        }
-    except Exception as e:
-        raise HTTPException(status_code=400, detail=f"Prediction error: {str(e)}")
-
-@app.post("/predict-batch")
-async def predict_batch(input_data: BatchPredictionInput):
-    if not model:
-        raise HTTPException(status_code=500, detail="Model not loaded")
-    
-    try:
-        # Convert to numpy array
-        features = np.array(input_data.data)
-        
-        # Validate input shape
-        if features.shape[1] != len(feature_names):
-            raise HTTPException(
-                status_code=400, 
-                detail=f"Expected {len(feature_names)} features, got {features.shape[1]}"
-            )
-        
-        # Make predictions
-        predictions = model.predict(features)
-        probabilities = model.predict_proba(features)
-        
-        return {
-            "predictions": predictions.tolist(),
-            "probabilities": probabilities.tolist(),
-            "batch_size": len(predictions)
-        }
-    except Exception as e:
-        raise HTTPException(status_code=400, detail=f"Batch prediction error: {str(e)}")
-
-@app.get("/debug")
-async def debug():
-    """Debug endpoint to check model loading status"""
-    return {
-        "model_loaded": model is not None,
-        "features_loaded": len(feature_names) > 0 if feature_names else False,
-        "feature_names": feature_names if feature_names else "Not loaded",
-        "model_type": str(type(model).__name__) if model else "Not loaded",
-        "github_model_url": GITHUB_MODEL_URL,
-        "github_features_url": GITHUB_FEATURES_URL
+############################ HuggingFace + GitHub ###############################
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import joblib
+import numpy as np
+import requests
+import tempfile
+import os
+from fastapi.middleware.cors import CORSMiddleware
+
+
+
+app = FastAPI(title="ML Model API", version="1.0.0")
+
+# Add CORS middleware - THIS IS CRITICAL FOR HUGGING FACE
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Allows all origins
+    allow_credentials=True,
+    allow_methods=["*"],  # Allows all methods
+    allow_headers=["*"],  # Allows all headers
+)
+
+# GitHub raw content URLs - using YOUR actual GitHub repository
+GITHUB_MODEL_URL = "https://raw.githubusercontent.com/SagarChhabriya/ml-model-api/main/model/model.joblib"
+GITHUB_FEATURES_URL = "https://raw.githubusercontent.com/SagarChhabriya/ml-model-api/main/model/feature_names.joblib"
+
+# Load model from GitHub
+def load_model_from_github():
+    try:
+        print("📥 Downloading model from GitHub...")
+        
+        # Download model file
+        model_response = requests.get(GITHUB_MODEL_URL)
+        model_response.raise_for_status()  # Raise error if download fails
+        
+        # Download feature names file  
+        features_response = requests.get(GITHUB_FEATURES_URL)
+        features_response.raise_for_status()
+        
+        # Save to temporary files
+        with tempfile.NamedTemporaryFile(delete=False, suffix='.joblib') as model_tmp:
+            model_tmp.write(model_response.content)
+            model_path = model_tmp.name
+        
+        with tempfile.NamedTemporaryFile(delete=False, suffix='.joblib') as features_tmp:
+            features_tmp.write(features_response.content)
+            features_path = features_tmp.name
+        
+        # Load the files
+        model = joblib.load(model_path)
+        feature_names = joblib.load(features_path)
+        
+        # Clean up temporary files
+        os.unlink(model_path)
+        os.unlink(features_path)
+        
+        print("✅ Model loaded successfully from GitHub!")
+        print(f"📊 Model type: {type(model).__name__}")
+        print(f"📈 Features: {feature_names}")
+        
+        return model, feature_names
+        
+    except Exception as e:
+        print(f"❌ Error loading from GitHub: {e}")
+        return None, []
+
+# Load model on startup
+model, feature_names = load_model_from_github()
+
+# Define input schema
+class PredictionInput(BaseModel):
+    sepal_length: float
+    sepal_width: float
+    petal_length: float
+    petal_width: float
+
+class BatchPredictionInput(BaseModel):
+    data: list[list[float]]
+
+@app.get("/")
+async def root():
+    return {
+        "message": "ML Model API deployed on Hugging Face Spaces! 🚀",
+        "endpoints": {
+            "health": "/health",
+            "single_prediction": "/predict", 
+            "batch_prediction": "/predict-batch",
+            "model_info": "/model-info",
+            "docs": "/docs"
+        },
+        "model_loaded": model is not None,
+        "model_source": "GitHub"
+    }
+
+@app.get("/health")
+async def health_check():
+    return {
+        "status": "healthy" if model else "unhealthy",
+        "model_loaded": model is not None,
+        "model_type": "RandomForestClassifier" if model else "None"
+    }
+
+@app.get("/model-info")
+async def model_info():
+    if not model:
+        raise HTTPException(status_code=500, detail="Model not loaded")
+    
+    return {
+        "model_type": str(type(model).__name__),
+        "feature_names": feature_names,
+        "n_features": len(feature_names),
+        "n_classes": getattr(model, 'n_classes_', 'Unknown')
+    }
+
+@app.post("/predict")
+async def predict_single(input_data: PredictionInput):
+    if not model:
+        raise HTTPException(status_code=500, detail="Model not loaded")
+    
+    try:
+        # Convert input to array
+        features = np.array([
+            input_data.sepal_length,
+            input_data.sepal_width,
+            input_data.petal_length,
+            input_data.petal_width
+        ]).reshape(1, -1)
+        
+        # Make prediction
+        prediction = model.predict(features)
+        probabilities = model.predict_proba(features)
+        
+        return {
+            "prediction": int(prediction[0]),
+            "probabilities": probabilities[0].tolist(),
+            "class_names": ["setosa", "versicolor", "virginica"],
+            "input_features": input_data.dict()
+        }
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Prediction error: {str(e)}")
+
+@app.post("/predict-batch")
+async def predict_batch(input_data: BatchPredictionInput):
+    if not model:
+        raise HTTPException(status_code=500, detail="Model not loaded")
+    
+    try:
+        # Convert to numpy array
+        features = np.array(input_data.data)
+        
+        # Validate input shape
+        if features.shape[1] != len(feature_names):
+            raise HTTPException(
+                status_code=400, 
+                detail=f"Expected {len(feature_names)} features, got {features.shape[1]}"
+            )
+        
+        # Make predictions
+        predictions = model.predict(features)
+        probabilities = model.predict_proba(features)
+        
+        return {
+            "predictions": predictions.tolist(),
+            "probabilities": probabilities.tolist(),
+            "batch_size": len(predictions)
+        }
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Batch prediction error: {str(e)}")
+
+@app.get("/debug")
+async def debug():
+    """Debug endpoint to check model loading status"""
+    return {
+        "model_loaded": model is not None,
+        "features_loaded": len(feature_names) > 0 if feature_names else False,
+        "feature_names": feature_names if feature_names else "Not loaded",
+        "model_type": str(type(model).__name__) if model else "Not loaded",
+        "github_model_url": GITHUB_MODEL_URL,
+        "github_features_url": GITHUB_FEATURES_URL
     }