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import gradio as gr
import numpy as np
import tensorflow as tf
import joblib
import sklearn # Required for unpickling label_encoder
# Load the saved model and label encoder
model = tf.keras.models.load_model("crop_recommendation_model.h5")
label_encoder = joblib.load("label_encoder.pkl")
def predict_crop(N, P, K, temperature, humidity, ph, rainfall):
# Prepare the input sample as a 2D numpy array
sample = np.array([[N, P, K, temperature, humidity, ph, rainfall]])
# Get model prediction
pred_probs = model.predict(sample)
pred_class = np.argmax(pred_probs, axis=1)[0]
# Convert numeric prediction back to crop label
predicted_crop = label_encoder.inverse_transform([pred_class])[0]
return predicted_crop
# Define Gradio Interface using the updated API
iface = gr.Interface(
fn=predict_crop,
inputs=[
gr.Number(label="Nitrogen (N)"),
gr.Number(label="Phosphorous (P)"),
gr.Number(label="Potassium (K)"),
gr.Number(label="Temperature (°C)"),
gr.Number(label="Humidity (%)"),
gr.Number(label="pH"),
gr.Number(label="Rainfall (mm)")
],
outputs=gr.Textbox(label="Recommended Crop"),
title="Crop Recommendation System",
description="Enter soil and climate parameters to get the recommended crop."
)
if __name__ == "__main__":
iface.launch()
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