app.py

import gradio as gr
import pickle
import numpy as np

# Load the trained model
with open('fertilizer.pkl', 'rb') as f:
    model = pickle.load(f)

# Dummy encoders (customize these based on your model preprocessing)
soil_dict = {'Loamy': 0, 'Sandy': 1, 'Clayey': 2, 'Black': 3, 'Red': 4}
crop_dict = {'Rice': 0, 'Wheat': 1, 'Maize': 2, 'Sugarcane': 3, 'Cotton': 4}

class FertilizerPredictor:
    def __init__(self, model):
        self.model = model

    def preprocess_input(self, N, P, K, temperature, humidity, moisture, soil_type, crop_type):
        try:
            # Encode categorical data
            soil_encoded = soil_dict.get(soil_type, -1)
            crop_encoded = crop_dict.get(crop_type, -1)
            
            if soil_encoded == -1 or crop_encoded == -1:
                raise ValueError("Invalid soil_type or crop_type")
            
            features = np.array([[N, P, K, temperature, humidity, moisture, soil_encoded, crop_encoded]])
            return features
        except KeyError as e:
            raise ValueError(f"Missing field: {e}")

    def predict(self, N, P, K, temperature, humidity, moisture, soil_type, crop_type):
        features = self.preprocess_input(N, P, K, temperature, humidity, moisture, soil_type, crop_type)
        prediction = self.model.predict(features)
        return f"Recommended Fertilizer: {prediction[0]}"

# Create an instance
predictor = FertilizerPredictor(model)

# Define Gradio interface
demo = gr.Interface(
    fn=predictor.predict,
    inputs=[
        gr.Number(label="Nitrogen (N)"),
        gr.Number(label="Phosphorus (P)"),
        gr.Number(label="Potassium (K)"),
        gr.Number(label="Temperature (°C)"),
        gr.Number(label="Humidity (%)"),
        gr.Number(label="Moisture (%)"),
        gr.Dropdown(list(soil_dict.keys()), label="Soil Type"),
        gr.Dropdown(list(crop_dict.keys()), label="Crop Type")
    ],
    outputs=gr.Textbox(label="Fertilizer Prediction"),
    title="Fertilizer Recommendation System",
    description="Enter soil and environmental parameters to get the recommended fertilizer."
)

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