Update app.py

app.py

@@ -1,32 +1,31 @@
 import gradio as gr
 import pickle
 import numpy as np
+import pandas as pd
 import os
 
-# Class for loading model and predicting
-class CropYieldModel:
-    def __init__(self, model_path):
-        if not os.path.exists(model_path):
-            raise FileNotFoundError(f"Model file not found at {model_path}")
-        with open(model_path, 'rb') as file:
-            self.model = pickle.load(file)
+# Load Model
+model_path = "crop_yield_model.pkl"
 
-    def predict(self, feature1, feature2, feature3):
-        try:
-            # Prepare input features
-            features = np.array([[feature1, feature2, feature3]])
-            prediction = self.model.predict(features)
-            return f"Predicted Crop Yield: {float(prediction[0]):.2f}"
-        except Exception as e:
-            return f"Prediction failed: {str(e)}"
+if not os.path.exists(model_path):
+    raise FileNotFoundError(f"Model file not found at {model_path}")
 
-# Load model
-model_path = 'crop_yield_model.pkl'  # Adjust the path if needed
-predictor = CropYieldModel(model_path)
+with open(model_path, "rb") as file:
+    model = pickle.load(file)
+
+# Prediction Function
+def predict_yield(feature1, feature2, feature3):
+    try:
+        # Prepare input
+        features = np.array([[feature1, feature2, feature3]])
+        prediction = model.predict(features)
+        return f"Predicted Crop Yield: {float(prediction[0]):.2f}"
+    except Exception as e:
+        return f"Error in prediction: {str(e)}"
 
 # Gradio Interface
 demo = gr.Interface(
-    fn=predictor.predict,
+    fn=predict_yield,
     inputs=[
         gr.Number(label="Feature 1"),
         gr.Number(label="Feature 2"),
@@ -34,8 +33,8 @@ demo = gr.Interface(
     ],
     outputs=gr.Textbox(label="Prediction Result"),
     title="Crop Yield Prediction",
-    description="Enter the values for the features to predict crop yield."
+    description="Enter the feature values to predict crop yield.",
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()