Update app.py

app.py

@@ -3,64 +3,62 @@ import numpy as np
 import joblib
 
 # Load the pre-trained crop yield prediction model
-crop_yield_model = joblib.load('ensemble.pkl')
+crop_yield_model = joblib.load('voting_yield.sav')
 
-# Define states, crops, and seasons for dropdown menus
-states = [
-    'Andaman and Nicobar Islands', 'Andhra Pradesh', 'Arunachal Pradesh', 'Assam', 'Bihar', 'Chandigarh',
-    'Chhattisgarh', 'Dadra and Nagar Haveli', 'Goa', 'Gujarat', 'Haryana', 'Himachal Pradesh',
-    'Jammu and Kashmir', 'Jharkhand', 'Karnataka', 'Kerala', 'Madhya Pradesh', 'Maharashtra', 'Manipur',
-    'Meghalaya', 'Mizoram', 'Nagaland', 'Odisha', 'Puducherry', 'Punjab', 'Rajasthan', 'Sikkim', 'Tamil Nadu',
-    'Telangana', 'Tripura', 'Uttar Pradesh', 'Uttarakhand', 'West Bengal'
-]
+# Predefined options
+states = ['Andaman and Nicobar Islands', 'Andhra Pradesh', 'Arunachal Pradesh', 'Assam', 'Bihar', 'Chandigarh', 
+          'Chhattisgarh', 'Dadra and Nagar Haveli', 'Goa', 'Gujarat', 'Haryana', 'Himachal Pradesh', 
+          'Jammu and Kashmir', 'Jharkhand', 'Karnataka', 'Kerala', 'Madhya Pradesh', 'Maharashtra', 
+          'Manipur', 'Meghalaya', 'Mizoram', 'Nagaland', 'Odisha', 'Puducherry', 'Punjab', 
+          'Rajasthan', 'Sikkim', 'Tamil Nadu', 'Telangana', 'Tripura', 'Uttar Pradesh', 
+          'Uttarakhand', 'West Bengal']
 
-crops = [
-    'Arecanut', 'Barley', 'Banana', 'Blackpepper', 'Brinjal', 'Cabbage', 'Cardamom', 'Cashewnuts', 'Cauliflower',
-    'Coriander', 'Cotton', 'Garlic', 'Grapes', 'Horsegram', 'Jowar', 'Jute', 'Ladyfinger', 'Maize',
-    'Mango', 'Moong', 'Onion', 'Orange', 'Papaya', 'Pineapple', 'Potato', 'Rapeseed', 'Ragi', 'Rice',
-    'Sesamum', 'Soyabean', 'Sunflower', 'Sweetpotato', 'Tapioca', 'Tomato', 'Turmeric', 'Wheat'
-]
+crops = ['Arecanut', 'Barley', 'Banana', 'Blackpepper', 'Brinjal', 'Cabbage', 'Cardamom', 'Cashewnuts', 'Cauliflower', 
+         'Coriander', 'Cotton', 'Garlic', 'Grapes', 'Horsegram', 'Jowar', 'Jute', 'Ladyfinger', 'Maize', 
+         'Mango', 'Moong', 'Onion', 'Orange', 'Papaya', 'Pineapple', 'Potato', 'Rapeseed', 'Ragi', 'Rice', 
+         'Sesamum', 'Soyabean', 'Sunflower', 'Sweetpotato', 'Tapioca', 'Tomato', 'Turmeric', 'Wheat']
 
 seasons = ['Kharif', 'Rabi', 'Summer', 'Whole Year']
 
+
 def predict_yield(state, crop, season, pH, rainfall, temperature, area, production):
-    """Predict crop yield based on user inputs."""
-    state_encoded = [1 if s == state else 0 for s in states]
-    crop_encoded = [1 if c == crop else 0 for c in crops]
-    season_encoded = [1 if s == season else 0 for s in seasons]
+    try:
+        # Encode categorical inputs
+        state_encoded = [1 if state == s else 0 for s in states]
+        crop_encoded = [1 if crop == c else 0 for c in crops]
+        season_encoded = [1 if season == s else 0 for s in seasons]
 
-    input_features = np.array(state_encoded + crop_encoded + season_encoded + [pH, rainfall, temperature, area, production])
-    input_features = input_features.reshape(1, -1)
+        # Combine inputs
+        input_features = np.array(state_encoded + crop_encoded + season_encoded + [pH, rainfall, temperature, area, production]).reshape(1, -1)
 
-    try:
-        predicted_yield = crop_yield_model.predict(input_features)[0]
-        return f"The predicted yield is {predicted_yield:.2f} tons per hectare."
+        # Check feature length
+        expected_features = len(states) + len(crops) + len(seasons) + 5
+        if input_features.shape[1] != expected_features:
+            return "Error: Feature shape mismatch!"
+        else:
+            predicted_yield = crop_yield_model.predict(input_features)
+            return f"The predicted yield is: {predicted_yield[0]:.2f} tons/hectare"
     except Exception as e:
-        return f"Error: {e}"
+        return f"Error: {str(e)}"
 
-# Gradio interface
-def interface():
-    """Define the Gradio interface."""
-    inputs = [
-        gr.Dropdown(choices=states, label="Select State"),
-        gr.Dropdown(choices=crops, label="Select Crop"),
-        gr.Dropdown(choices=seasons, label="Select Season"),
-        gr.Number(label="Soil pH Value", value=7.0, precision=2),
-        gr.Number(label="Rainfall (mm)", value=100.0, precision=1),
-        gr.Number(label="Temperature (°C)", value=25.0, precision=1),
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict_yield,
+    inputs=[
+        gr.Dropdown(states, label="Select State"),
+        gr.Dropdown(crops, label="Select Crop"),
+        gr.Dropdown(seasons, label="Select Season"),
+        gr.Number(label="Soil pH Value", value=6.5, precision=2),
+        gr.Number(label="Rainfall (mm)", value=100.0, precision=2),
+        gr.Number(label="Temperature (°C)", value=25.0, precision=2),
         gr.Number(label="Area (hectares)", value=1.0, precision=2),
-        gr.Number(label="Production (tons)", value=2.0, precision=2)
-    ]
-
-    outputs = gr.Textbox(label="Predicted Yield")
+        gr.Number(label="Production (tons)", value=1.0, precision=2),
+    ],
+    outputs="text",
+    title="Crop Yield Prediction",
+    description="Enter the required parameters to predict the crop yield in tons/hectare.",
+)
 
-    gr.Interface(
-        fn=predict_yield,
-        inputs=inputs,
-        outputs=outputs,
-        title="Crop Yield Prediction",
-        description="Enter the details below to predict crop yield using the trained model."
-    ).launch()
+# Launch the app
+interface.launch()
 
-if __name__ == "__main__":
-    interface()