Create app.py

app.py

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+import gradio as gr
+import pickle
+import numpy as np
+
+# Load the trained model
+model_file = 'Yield_Ridge.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()