Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import tensorflow as tf
+import joblib
+
+# 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
+iface = gr.Interface(
+    fn=predict_crop,
+    inputs=[
+        gr.inputs.Number(label="Nitrogen (N)"),
+        gr.inputs.Number(label="Phosphorous (P)"),
+        gr.inputs.Number(label="Potassium (K)"),
+        gr.inputs.Number(label="Temperature (°C)"),
+        gr.inputs.Number(label="Humidity (%)"),
+        gr.inputs.Number(label="pH"),
+        gr.inputs.Number(label="Rainfall (mm)")
+    ],
+    outputs=gr.outputs.Textbox(label="Recommended Crop"),
+    title="Crop Recommendation System",
+    description="Enter soil and climate parameters to get the recommended crop."
+)
+
+if __name__ == "__main__":
+    iface.launch()