Update main.py

main.py

@@ -1,83 +1,63 @@
-import os
-import joblib
-import pandas as pd
-import logging
-from fastapi import FastAPI
-from pydantic import BaseModel
-from datetime import datetime
-
-# LOGGING: Console par log karein taake Docker ya cloud environments mein logs asani se milain
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(levelname)s - %(message)s'
-)
-
-app = FastAPI(title="Flight Delay Prediction API")
-
-# --- PATH SETUP (DOCKER FRIENDLY) ---
-# BASE_DIR ko aik level up le kar jana hai (api folder se bahar)
-# Example: /app/api/main.py -> /app/
-BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-MODEL_PATH = os.path.join(BASE_DIR, 'models', 'flight_model.joblib')
-
-logging.info(f"Looking for model at: {MODEL_PATH}")
-
-try:
-    # Model load logic
-    model = joblib.load(MODEL_PATH)
-    logging.info("Model successfully loaded.")
-except Exception as e:
-    logging.error(f"Model load karne mein masla: {e}")
-    model = None 
-
-# --- DATA SCHEMA ---
-class FlightData(BaseModel):
-    MONTH: int
-    DAY_OF_WEEK: int
-    DISTANCE: float
-    CRS_DEP_TIME: int
-    OP_UNIQUE_CARRIER: str
-    ORIGIN: str
-    DEST: str
-
-# --- ENDPOINTS ---
-@app.get("/")
-def home():
-    return {"message": "Flight Delay Prediction API is Running!"}
-
-@app.post("/predict")
-def predict(data: FlightData):
-    if model is None:
-        logging.error("Prediction failed: Model is not loaded.")
-        return {"error": "Model not loaded on server. Check path and logs."}
-
-    logging.info(f"Prediction requested for: {data.dict()}")
-
-    try:
-        # Convert incoming Pydantic model to DataFrame
-        input_df = pd.DataFrame([data.dict()])
-        
-        # Categorical columns ko numeric mein badalne ke liye hash use ho raha hai
-        for col in ['OP_UNIQUE_CARRIER', 'ORIGIN', 'DEST']:
-            input_df[col] = input_df[col].apply(lambda x: abs(hash(str(x))) % 1000)
-
-        # Make Prediction
-        prediction = model.predict(input_df)[0]
-        probability = model.predict_proba(input_df)[0][1]
-
-        result = {
-            "delay_probability": round(float(probability), 2),
-            "prediction": "Delayed" if prediction == 1 else "On Time"
-        }
-        
-        logging.info(f"Prediction successful: {result}")
-        return result
-
-    except Exception as e:
-        logging.error(f"Prediction error: {str(e)}")
-        return {"error": "There is an issue with the prediction process", "details": str(e)}
-
-if __name__ == "__main__":
-    import uvicorn
-    # Docker/External access ke liye 0.0.0.0 zaroori hai
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+import os
+import joblib
+import pandas as pd
+import logging
+from fastapi import FastAPI
+from pydantic import BaseModel
+
+# LOGGING setup
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+app = FastAPI(title="Flight Delay Prediction API")
+
+# --- PATH SETUP (DOCKER FIXED) ---
+# Docker mein hum /app folder mein hotay hain
+MODEL_PATH = '/app/models/flight_model.joblib'
+
+logging.info(f"Looking for model at: {MODEL_PATH}")
+
+try:
+    if os.path.exists(MODEL_PATH):
+        model = joblib.load(MODEL_PATH)
+        logging.info("Model successfully loaded.")
+    else:
+        logging.error(f"Model file not found at {MODEL_PATH}")
+        model = None
+except Exception as e:
+    logging.error(f"Model load karne mein masla: {e}")
+    model = None 
+
+class FlightData(BaseModel):
+    MONTH: int
+    DAY_OF_WEEK: int
+    DISTANCE: float
+    CRS_DEP_TIME: int
+    OP_UNIQUE_CARRIER: str
+    ORIGIN: str
+    DEST: str
+
+@app.get("/")
+def home():
+    return {"message": "Flight Delay Prediction API is Running!", "status": "online"}
+
+@app.post("/predict")
+def predict(data: FlightData):
+    if model is None:
+        return {"error": "Model not loaded on server."}
+
+    try:
+        input_df = pd.DataFrame([data.dict()])
+        
+        # Hashing for categorical columns
+        for col in ['OP_UNIQUE_CARRIER', 'ORIGIN', 'DEST']:
+            input_df[col] = input_df[col].apply(lambda x: abs(hash(str(x))) % 1000)
+
+        prediction = model.predict(input_df)[0]
+        probability = model.predict_proba(input_df)[0][1]
+
+        return {
+            "delay_probability": round(float(probability), 2),
+            "prediction": "Delayed" if prediction == 1 else "On Time"
+        }
+    except Exception as e:
+        return {"error": str(e)}