Update app.py

app.py

@@ -1,62 +1,66 @@
 import gradio as gr
-import pickle
 import numpy as np
+import joblib
 
-# Load the trained model
-model_file = 'voting_yield.sav'
-with open(model_file, 'rb') as file:
-    model = pickle.load(file)
-
-# Define prediction function
-def predict_yield(crop: str, year: int, rainfall: float, temperature: float, fertilizer: float):
-    """
-    Predicts crop yield based on user inputs.
-
-    Parameters:
-        crop (str): Name of the crop.
-        year (int): Year of prediction.
-        rainfall (float): Average annual rainfall (in mm).
-        temperature (float): Average annual temperature (in °C).
-        fertilizer (float): Amount of fertilizer used (in kg/ha).
-
-    Returns:
-        str: Predicted yield (in tons per hectare).
-    """
-    # Create a feature array for prediction
-    features = np.array([[crop_mapping[crop], year, rainfall, temperature, fertilizer]])
-    prediction = model.predict(features)[0]
-    return f"{prediction:.2f} tons/ha"
-
-# Crop mapping (example mapping; adjust as per your dataset)
-crop_mapping = {
-    "Wheat": 1,
-    "Rice": 2,
-    "Maize": 3,
-    "Sugarcane": 4
-}
-
-# Define Gradio interface
-with gr.Blocks() as yield_app:
-    gr.Markdown("## Yield Prediction")
-    gr.Markdown("Predict crop yield based on environmental and agricultural inputs.")
-
-    with gr.Row():
-        crop = gr.Dropdown(label="Select Crop", choices=list(crop_mapping.keys()), value="Wheat")
-        year = gr.Number(label="Year", value=2025)
-    
-    with gr.Row():
-        rainfall = gr.Number(label="Rainfall (in mm)", value=700.0)
-        temperature = gr.Number(label="Temperature (in °C)", value=25.0)
-        fertilizer = gr.Number(label="Fertilizer (in kg/ha)", value=120.0)
-    
-    output = gr.Textbox(label="Predicted Yield", interactive=False)
-    
-    predict_button = gr.Button("Predict Yield")
-    predict_button.click(
-        predict_yield, 
-        inputs=[crop, year, rainfall, temperature, fertilizer], 
-        outputs=output
-    )
-
-# Launch the app
-yield_app.launch()
+# Load the pre-trained crop yield prediction model
+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'
+]
+
+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]
+
+    input_features = np.array(state_encoded + crop_encoded + season_encoded + [pH, rainfall, temperature, area, production])
+    input_features = input_features.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."
+    except Exception as e:
+        return f"Error: {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),
+        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.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()
+
+if __name__ == "__main__":
+    interface()