Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from sklearn.preprocessing import LabelEncoder
+from xgboost import XGBClassifier
+import pickle
+
+model = pickle.load('crop_recommendation_model.pkl')
+le = pickle.load('label_encoder.pkl')
+
+def recommend_crop(nitrogen, phosphorus, potassium, temperature, humidity, ph, rainfall)
+    X_sample = 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
+    crops_pred = le.inverse_transform(y_pred_sample)
+
+    return crops_pred
+
+# 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."
+)