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app.py

@@ -41,3 +41,37 @@ input_data = pd.DataFrame([{
     'Store_Type': Store_Type
 }])
 
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict Sales"):
+    response = requests.post(
+        "https://Anusha3-Superkart-Backend-Docker-space.hf.space/v1/sales",
+        json=input_data.to_dict(orient='records')[0]
+    )  # Send data to backend API
+
+    if response.status_code == 200:
+        prediction = response.json()['Predicted Sales']
+        st.success(f"Predicted Sales: {prediction}")
+    else:
+        st.error("Error making prediction. Please check the backend logs.")
+
+# 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 Sales"):
+        response = requests.post(
+            "https://Anusha3-Superkart-Backend-Docker-space.hf.space/v1/salesbatch",  files={"file": uploaded_file}   )  # Send file to backend API
+
+        if response.status_code == 200:
+            predictions = response.json()
+            st.success(" Batch predictions completed!")
+            st.write(predictions)  # Display the predictions
+        else:
+            st.error("Error making batch prediction.")
+
+
+