Add application file

app.py

@@ -0,0 +1,40 @@
+import gradio as gr
+import pandas as pd
+import joblib
+# Load model
+best_model = joblib.load("crop_recommendation_model.pkl")
+y_labels = best_model.classes_
+# Prediction function
+def predict_crop(N, P, K, temperature, humidity, ph, rainfall):
+    input_df = pd.DataFrame([[N, P, K, temperature, humidity, ph, rainfall]],
+                            columns=['N', 'P', 'K', 'temperature', 'humidity', 'ph', 'rainfall'])
+
+    prediction = best_model.predict(input_df)[0]
+    probabilities = best_model.predict_proba(input_df)[0]
+    sorted_probs = sorted(enumerate(probabilities), key=lambda x: x[1], reverse=True)
+
+    best_crop = prediction
+    alternatives = [y_labels[i] for i, _ in sorted_probs[1:4]]
+
+    return f"✅ Best Crop: {best_crop}", f"✨ Alternatives: {', '.join(alternatives)}"
+    # Gradio Interface with api_name enabled
+interface = 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="Best Crop"),
+        gr.Textbox(label="Alternative Crops")
+    ],
+    title="🌱 Smart Crop Recommender",
+    description="Predicts the best crop and top 3 alternatives based on your soil and climate data.",
+    api_name="/api/predict" # 👈 This enables API endpoint!
+)
+interface.launch(share=True)