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app.py

@@ -41,3 +41,42 @@ if st.button("Predict"):
     prediction = (prediction_proba >= classification_threshold).astype(int)
     result = "Fali" if prediction == 1 else "Not Fail"
     st.write(f"Based on the information provided, the engine is likely to {result}.")
+    
+
+# Define an endpoint for batch prediction (POST request)
+def predict_store_sales_batch(csv_file):
+    """
+    This function expects a CSV file containing property details for multiple properties
+    and returns the predicted sales as a dictionary in the JSON response.
+    """
+
+    # Read the CSV file into a Pandas DataFrame
+    input_data = pd.read_csv(csv_file)
+
+    # Make predictions for all properties in the DataFrame (get store_saless)
+    predicted_sales = model.predict(input_data.drop("Engine Condition",axis=1)).tolist()
+
+    # Create a dictionary of predictions with property IDs as keys
+    property_ids = input_data['Engine Condition'].tolist()  # Assuming 'id' is the property ID column
+    output_dict = dict(zip(property_ids, predicted_sales))  # Use actual prices
+
+    # Return the predictions dictionary as a JSON response
+    return output_dict
+    
+
+
+
+# Section for batch prediction
+st.subheader("Batch Prediction")
+
+# Allow users to upload a CSV file for batch prediction
+uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])
+
+
+# Make batch prediction when the "Predict Batch" button is clicked
+if uploaded_file is not None:
+    if st.button("Predict Batch"):
+        response = predict_store_sales_batch(uploaded_file)
+        predictions = response.json()
+        st.success("Batch predictions completed!")
+        st.write(predictions)  # Display the predictions