Upload folder using huggingface_hub

app.py

@@ -2,9 +2,6 @@ import streamlit as st
 import requests
 import json
 
-# Define the URL of your Flask API (replace with your Hugging Face Space URL)
-API_URL = "https://pkulkar-SalesForcasterFrontend.hf.space/v1/sales" # Replace with your Hugging Face Space URL
-
 st.title("SuperKart Sales Forecaster")
 st.write("Enter the details of the product and store to get a sales forecast.")
 
@@ -20,26 +17,24 @@ store_size = st.selectbox("Store Size", ['Medium', 'High', 'Low'])
 store_location_city_type = st.selectbox("Store Location City Type", ['Tier 1', 'Tier 3', 'Tier 2'])
 store_type = st.selectbox("Store Type", ['Supermarket Type 1', 'Supermarket Type 2', 'Departmental Store', 'Food Mart'])
 
+# Prepare the data to be sent to the API
+input_data = {
+    'Product_Weight': product_weight,
+    'Product_Sugar_Content': product_sugar_content,
+    'Product_Allocated_Area': product_allocated_area,
+    'Product_Type': product_type,
+    'Product_MRP': product_mrp,
+    'Store_Id': store_id,
+    'Store_Establishment_Year': store_establishment_year,
+    'Store_Size': store_size,
+    'Store_Location_City_Type': store_location_city_type,
+    'Store_Type': store_type,
+}
 
 if st.button("Predict Sales"):
-    # Prepare the data to be sent to the API
-    input_data = {
-        'Product_Weight': product_weight,
-        'Product_Sugar_Content': product_sugar_content,
-        'Product_Allocated_Area': product_allocated_area,
-        'Product_Type': product_type,
-        'Product_MRP': product_mrp,
-        'Store_Id': store_id,
-        'Store_Establishment_Year': store_establishment_year,
-        'Store_Size': store_size,
-        'Store_Location_City_Type': store_location_city_type,
-        'Store_Type': store_type,
-    }
-
     # Send the data to the Flask API
     try:
-        response = requests.post(API_URL, json=input_data)
-
+        response = requests.post("https://pkulkar-SalesForcasterFrontend.hf.space/v1/sales", json=input_data)
         if response.status_code == 200:
             prediction = response.json()
             st.success(f"Predicted Sales: {prediction['Predicted Price (in dollars)']:.2f}")
@@ -48,24 +43,19 @@ if st.button("Predict Sales"):
     except requests.exceptions.RequestException as e:
         st.error(f"Error connecting to the API: {e}")
 
+# Section for batch prediction
+st.subheader("Batch Prediction")
 
-# Upload the Streamlit app file and requirements file to Hugging Face Space
-from huggingface_hub import upload_file
-
-repo_id_frontend = "pkulkar/SalesForcasterFrontend" # Replace with your Hugging Face Space ID for the frontend
+# Allow users to upload a CSV file for batch prediction
+uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])
 
-upload_file(
-    path_or_fileobj="/content/drive/MyDrive/deployment_files/app_streamlit.py",
-    path_in_repo="app.py",
-    repo_id=repo_id_frontend,
-    repo_type="space",
-)
-
-upload_file(
-    path_or_fileobj="/content/drive/MyDrive/deployment_files/requirements_streamlit.txt",
-    path_in_repo="requirements.txt",
-    repo_id=repo_id_frontend,
-    repo_type="space",
-)
-
-```
+# Make batch prediction when the "Predict Batch" button is clicked
+if uploaded_file is not None:
+    if st.button("Predict Sales Batch"):
+        response = requests.post("https://pkulkar-SalesForcasterFrontend.hf.space/v1/salesbatch", files={"file": uploaded_file})  # Send file to Flask 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.")