Update app.py

app.py

@@ -3,82 +3,26 @@ from fastapi import FastAPI
 import pandas as pd
 import numpy as np
 import os
-from sklearn.ensemble import RandomForestRegressor
-from sklearn.preprocessing import StandardScaler
-from sklearn.pipeline import Pipeline
-from sklearn.compose import ColumnTransformer
-from sklearn.preprocessing import OneHotEncoder
+import joblib
+import warnings
+warnings.filterwarnings('ignore')
 
 app = FastAPI(title="Crop Yield Predictor API")
 
-# ======== SIMPLE MODEL TRAINING ========
-def create_and_train_model():
-    """Create a simple model that will definitely work"""
-    try:
-        # Create sample training data with the same features
-        sample_data = {
-            'Area': ['India', 'USA', 'China', 'Brazil', 'India', 'USA'],
-            'Item': ['Maize', 'Wheat', 'Rice', 'Soybean', 'Wheat', 'Maize'],
-            'Year': [2020, 2021, 2022, 2020, 2021, 2022],
-            'average_rain_fall_mm_per_year': [800, 900, 1200, 1100, 850, 950],
-            'pesticides_tonnes': [5000, 6000, 7000, 5500, 5200, 5800],
-            'avg_temp': [20, 18, 22, 25, 19, 21]
-        }
-        
-        # Sample target (yield in hg/ha)
-        sample_target = [25000, 30000, 35000, 28000, 32000, 27000]
-        
-        df = pd.DataFrame(sample_data)
-        
-        # Define preprocessing
-        numeric_features = ['Year', 'average_rain_fall_mm_per_year', 'pesticides_tonnes', 'avg_temp']
-        categorical_features = ['Area', 'Item']
-        
-        preprocessor = ColumnTransformer(
-            transformers=[
-                ('num', StandardScaler(), numeric_features),
-                ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features)
-            ])
-        
-        # Create simple pipeline
-        model = Pipeline(steps=[
-            ('preprocessor', preprocessor),
-            ('regressor', RandomForestRegressor(n_estimators=10, random_state=42))
-        ])
-        
-        # Train on sample data
-        model.fit(df, sample_target)
-        
-        return model, "✅ New model created and trained successfully!"
-        
-    except Exception as e:
-        return None, f"❌ Model creation failed: {str(e)}"
-
-# ======== LOAD OR CREATE MODEL ========
+# ======== MODEL LOADING WITH JOBLIB ========
 def load_model_properly():
-    """Try to load existing model, else create new one"""
-    model_path = 'CropYieldPredictor.pkl'
+    model_path = 'CropYieldPredictor_COMPATIBLE.joblib'
     
-    if os.path.exists(model_path):
-        try:
-            # Try to load existing model
-            with open(model_path, 'rb') as file:
-                model = pickle.load(file)
-            return model, "✅ Existing model loaded successfully!"
-        except:
-            # If loading fails, create new model
-            return create_and_train_model()
-    else:
-        # No model file, create new one
-        return create_and_train_model()
-
-# Try to load pickle if needed
-try:
-    import pickle
-    model, load_status = load_model_properly()
-except:
-    model, load_status = create_and_train_model()
+    if not os.path.exists(model_path):
+        return None, f"❌ Model file '{model_path}' not found!"
+    
+    try:
+        model = joblib.load(model_path)
+        return model, "✅ Model loaded successfully with joblib!"
+    except Exception as e:
+        return None, f"❌ Loading failed: {str(e)}"
 
+model, load_status = load_model_properly()
 print(load_status)
 
 # ======== AVAILABLE AREAS ========