Upload folder using huggingface_hub

Dockerfile

@@ -1,21 +1,16 @@
+# Use a minimal base image with Python 3.9 installed
 FROM python:3.9-slim
 
+# Set the working directory inside the container to /app
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    software-properties-common \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
 
+# Install Python dependencies listed in requirements.txt
 RUN pip3 install -r requirements.txt
 
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+# Define the command to run the Streamlit app on port 8501 and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

@@ -0,0 +1,101 @@
+
+import streamlit as st
+import requests
+import pandas as pd
+
+# Title and description
+st.set_page_config(page_title="SuperKart Store Sales Forecast", layout="centered")
+st.title('📈 SuperKart Store Sales Forecast')
+st.write('Enter product and store details to predict the sales total or upload a CSV for batch forecasting.')
+
+# --- Online Prediction ---
+st.header('🛒 Product and Store Details (Single Forecast)')
+
+col1, col2 = st.columns(2)
+
+with col1:
+    product_weight = st.number_input('Product Weight (kg)', min_value=0.0, format="%.2f")
+    product_mrp = st.number_input('Product MRP (₹)', min_value=0.0, format="%.2f")
+    product_sugar_content = st.selectbox('Product Sugar Content', ['Low Sugar', 'Regular', 'No Sugar'])
+    product_allocated_area = st.number_input('Product Allocated Area (0.0 - 1.0)', min_value=0.0, max_value=1.0, format="%.4f")
+    product_type = st.selectbox('Product Type', [
+        'Meat', 'Snack Foods', 'Hard Drinks', 'Dairy', 'Canned', 'Soft Drinks',
+        'Health and Hygiene', 'Baking Goods', 'Bread', 'Breakfast', 'Frozen Foods',
+        'Fruits and Vegetables', 'Household', 'Seafood', 'Starchy Foods', 'Others'
+    ])
+
+with col2:
+    store_id = st.selectbox('Store ID', ['OUT001', 'OUT002', 'OUT003', 'OUT004'])
+    store_establishment_year = st.number_input('Store Establishment Year', min_value=1980, max_value=2025, step=1)
+    store_size = st.selectbox('Store Size', ['High', 'Medium', 'Small'])
+    store_location_city_type = st.selectbox('Store Location City Type', ['Tier 1', 'Tier 2', 'Tier 3'])
+    store_type = st.selectbox('Store Type', ['Departmental Store', 'Supermarket Type1', 'Supermarket Type2', 'Food Mart'])
+
+# Predict Button
+if st.button('📊 Predict Sales Forecast'):
+    input_data = {
+        'Product_Weight': product_weight,
+        'Product_MRP': product_mrp,
+        'Product_Sugar_Content': product_sugar_content,
+        'Product_Allocated_Area': product_allocated_area,
+        'Product_Type': product_type,
+        '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
+    }
+
+    api_url = 'https://Yash0204-API-SuperKart-Backend.hf.space/v1/sales'
+
+    try:
+        response = requests.post(api_url, json=input_data)
+
+        if response.status_code == 200:
+            prediction_result = response.json()
+            predicted_sales = prediction_result.get('predicted_product_store_sales_total')
+
+            if predicted_sales is not None:
+                st.success(f'✅ Predicted Product Store Sales Total: ₹{predicted_sales:.2f}')
+            else:
+                st.error('❌ Prediction not found in the response.')
+
+        elif response.status_code == 400:
+            st.error(f'❌ API Error: Invalid input data. Details: {response.json().get("error", "Unknown error")}')
+        else:
+            st.error(f'❌ API Error: Status Code {response.status_code}. Details: {response.text}')
+
+    except requests.exceptions.RequestException as e:
+        st.error(f'❌ Connection Error: {e}')
+    except Exception as e:
+        st.error(f'❌ Unexpected Error: {e}')
+
+st.markdown("---")
+
+# --- Batch Forecast ---
+st.header("📁 Batch Forecast using CSV Upload")
+uploaded_file = st.file_uploader("Upload a CSV file containing product/store data", type=["csv"])
+
+if uploaded_file is not None:
+    if st.button("📥 Predict Batch Sales Forecast"):
+        api_batch_url = "https://Yash0204-API-SuperKart-Backend.hf.space/v1/salesbatch"
+
+        try:
+            response = requests.post(api_batch_url, files={"file": uploaded_file})
+
+            if response.status_code == 200:
+                result = response.json()
+                df_result = pd.DataFrame(result)
+                st.success("✅ Batch predictions completed.")
+                st.dataframe(df_result)
+            else:
+                st.error(f'❌ Batch Prediction Error: {response.status_code} - {response.text}')
+
+        except requests.exceptions.RequestException as e:
+            st.error(f'❌ Connection Error: {e}')
+        except Exception as e:
+            st.error(f'❌ Unexpected Error: {e}')
+
+st.info("ℹ️ Please ensure your backend API supports `/v1/sales` and `/v1/salesbatch` endpoints.")
+
+

requirements.txt

@@ -1,3 +1,3 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+requests==2.28.1
+streamlit==1.43.2