Update app.py

app.py

@@ -4,25 +4,39 @@ from sklearn.preprocessing import LabelEncoder
 from xgboost import XGBClassifier
 import pickle
 
-model = pickle.load('crop_recommendation_model.pkl')
-le = pickle.load('label_encoder.pkl')
+# Load the trained model and label encoder
+model = pickle.load(open('crop_recommendation_model.pkl', 'rb'))
+le = pickle.load(open('label_encoder.pkl', 'rb'))
 
 def recommend_crop(nitrogen, phosphorus, potassium, temperature, humidity, ph, rainfall):
-    X_sample = nitrogen, phosphorus, potassium, temperature, humidity, ph, rainfall
+    # Prepare the input sample as a 2D array
+    X_sample = np.array([[nitrogen, phosphorus, potassium, temperature, humidity, ph, rainfall]])
         
     # Predict crop recommendations
     y_pred_sample = model.predict(X_sample)
         
-    # Decode the predictions and ground truth back to crop names
+    # Decode the prediction back to crop name
     crops_pred = le.inverse_transform(y_pred_sample)
 
-    return crops_pred
+    return crops_pred[0]  # Return the predicted crop name
 
 # Create the Gradio interface
 interface = gr.Interface(
-    fn=classify_potato_plant,
-    inputs=[gr.Number(label="Nitrogen - Ratio of Nitrogen in the soil"), gr.Number(label="Phosphorus - Ratio of Phosphorus in the soil"), gr.Number(label="Potassium - Ratio of Potassium in the soil"), gr.Number(label="Temperature - In degrees Celsius"), gr.Number(label="Humidity - Relative humidity in %"), gr.Number(label="pH Value - pH value of the soil"), gr.Number(label="Rainfall - Rainfall in mm")],
-    outputs=[gr.Textbox(label="Predicted Output"), gr.Textbox(label="Confidence Score")],
-    title="Acres - PPDC",
-    description="Acres PPDC, is our Potato Plant Disease Classification vision model, capable of accurately classifying potato plant disease, based on a single image."
-)
+    fn=recommend_crop,
+    inputs=[
+        gr.Number(label="Nitrogen - Ratio of Nitrogen in the soil"),
+        gr.Number(label="Phosphorus - Ratio of Phosphorus in the soil"),
+        gr.Number(label="Potassium - Ratio of Potassium in the soil"),
+        gr.Number(label="Temperature - In degrees Celsius"),
+        gr.Number(label="Humidity - Relative humidity in %"),
+        gr.Number(label="pH Value - pH value of the soil"),
+        gr.Number(label="Rainfall - Rainfall in mm")
+    ],
+    outputs=gr.Textbox(label="Recommended Crop"),
+    title="Acres - CR",
+    description="Acres Crop Recommendation recommends the best crop to plant based on soil and climate conditions."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()