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app.py

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+import streamlit as st
+import requests
+import pandas as pd
+import json
+
+st.title("SuperKart Sales Forecasting")
+
+st.write("Enter the details of the product and store to get a sales forecast.")
+
+# Input fields for the user to provide data
+product_id = st.text_input("Product ID")
+product_weight = st.number_input("Product Weight", value=10.0, format="%.2f")
+product_sugar_content = st.selectbox("Product Sugar Content", ['Low Sugar', 'Regular', 'No Sugar', 'reg'])
+product_allocated_area = st.number_input("Product Allocated Area", value=0.1, format="%.3f")
+product_type = st.selectbox("Product Type", ['Frozen Foods', 'Dairy', 'Canned', 'Baking Goods', 'Health and Hygiene', 'Snack Foods', 'Meat', 'Household', 'Hard Drinks', 'Fruits and Vegetables', 'Breads', 'Soft Drinks', 'Breakfast', 'Others', 'Starchy Foods', 'Seafood'])
+product_mrp = st.number_input("Product MRP", value=150.0, format="%.2f")
+store_id = st.selectbox("Store ID", ['OUT004', 'OUT003', 'OUT001', 'OUT002'])
+store_establishment_year = st.number_input("Store Establishment Year", value=2000, format="%d")
+store_size = st.selectbox("Store Size", ['Medium', 'High', 'Small'])
+store_location_city_type = st.selectbox("Store Location City Type", ['Tier 2', 'Tier 1', 'Tier 3'])
+store_type = st.selectbox("Store Type", ['Supermarket Type2', 'Departmental Store', 'Supermarket Type1', 'Food Mart'])
+
+# Create a dictionary with the input data
+input_data = {
+    'Product_Id': product_id,
+    '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
+}
+
+# Button to trigger prediction
+if st.button("Predict Sales"):
+    # Hugging Face proxy URL for Flask backend
+    backend_url = "https://huggingface.co/spaces/bhumitps/md-be/predict"
+
+    try:
+        # Send a POST request to the backend API
+        response = requests.post(backend_url, json=[input_data])  # Send as a list of one data point
+
+        # Check if the request was successful
+        if response.status_code == 200:
+            predictions = response.json().get('predictions')
+            if predictions:
+                st.success(f"Predicted Sales: {predictions[0]:.2f}")
+            else:
+                st.error("Error: Could not retrieve predictions from the backend.")
+        else:
+            st.error(f"Error: Received status code {response.status_code} from the backend.")
+            st.error(f"Response: {response.text}")
+
+    except requests.exceptions.RequestException as e:
+        st.error(f"Error connecting to the backend API: {e}")