Create app.py

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+import pickle
+import numpy as np
+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)
+
+    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)}"
+
+# Load model
+model_path = 'model/crop_yield_model.pkl'  # Adjust the path if needed
+predictor = CropYieldModel(model_path)
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=predictor.predict,
+    inputs=[
+        gr.Number(label="Feature 1"),
+        gr.Number(label="Feature 2"),
+        gr.Number(label="Feature 3")
+    ],
+    outputs=gr.Textbox(label="Prediction Result"),
+    title="Crop Yield Prediction",
+    description="Enter the values for the features to predict crop yield."
+)
+
+if __name__ == "__main__":
+    demo.launch()