app.py

import gradio as gr
import pickle
import numpy as np
import pandas as pd
import os

# Load Model
model_path = "crop_yield_model.pkl"

if not os.path.exists(model_path):
    raise FileNotFoundError(f"Model file not found at {model_path}")

with open(model_path, "rb") as file:
    model = pickle.load(file)

# Example mapping for categorical variables (if encoded during training)
season_mapping = {"Kharif": 0, "Rabi": 1, "Zaid": 2}
district_mapping = {"District A": 0, "District B": 1, "District C": 2}
crop_mapping = {"Wheat": 0, "Rice": 1, "Maize": 2, "Sugarcane": 3}

# Prediction Function
def predict_yield(area, season, district, crop):
    try:
        # Convert categorical inputs to numerical if necessary
        season_encoded = season_mapping.get(season, -1)
        district_encoded = district_mapping.get(district, -1)
        crop_encoded = crop_mapping.get(crop, -1)

        if -1 in [season_encoded, district_encoded, crop_encoded]:
            return "Error: Invalid categorical input value."

        # Prepare input
        features = np.array([[area, season_encoded, district_encoded, crop_encoded]])
        prediction = model.predict(features)
        return f"Predicted Crop Yield: {float(prediction[0]):.2f}"
    except Exception as e:
        return f"Error in prediction: {str(e)}"

# Gradio Interface
demo = gr.Interface(
    fn=predict_yield,
    inputs=[
        gr.Number(label="Area (in acres)"),
        gr.Dropdown(["Kharif", "Rabi", "Zaid"], label="Season"),
        gr.Dropdown(["District A", "District B", "District C"], label="District"),
        gr.Dropdown(["Wheat", "Rice", "Maize", "Sugarcane"], label="Crop"),
    ],
    outputs=gr.Textbox(label="Prediction Result"),
    title="Crop Yield Prediction",
    description="Enter the details to predict crop yield.",
)

if __name__ == "__main__":
    demo.launch()