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Dockerfile

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+FROM python:3.9-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+# Explicitly add Python's script directory to PATH
+ENV PATH="/usr/local/bin:$PATH"
+
+# Create the .streamlit directory if it doesn't exist and grant write permissions
+RUN mkdir -p /.streamlit && chmod -R 777 /.streamlit
+
+EXPOSE 8501
+
+CMD ["streamlit", "run", "app.py"]

app.py

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+import streamlit as st
+import pandas as pd
+import requests
+
+# Set the title of the Streamlit app
+st.title("SuperKart Sales Predictor")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect business input for features
+Product_Weight = st.number_input("Product Weight", min_value=0.0, max_value=100.0, step=0.1)
+Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "No Sugar", "Regular"])
+Product_Type = st.selectbox("Product Type", ["Perishable", "Non Perishable"])
+Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.000, max_value=0.300, step=0.1)
+Product_MRP = st.number_input("Product MRP", min_value=00.00, max_value=1000.00, step=0.1)
+Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
+Store_Type = st.selectbox("Store Type", ["Departmental Store", "Food Mart", "Supermarket Type1", "Supermarket Type2"])
+Store_Current_Age = st.number_input("Store Current Age", min_value=0, max_value=100, step=1)
+
+# Convert user input into a DataFrame
+business_df = pd.DataFrame({
+    'Product_Weight': [Product_Weight],
+    'Product_Sugar_Content': [Product_Sugar_Content],
+    'Product_Type': [Product_Type],
+    'Product_Allocated_Area': [Product_Allocated_Area],
+    'Product_MRP': [Product_MRP],
+    'Store_Size': [Store_Size],
+    'Store_Location_City_Type': [Store_Location_City_Type],
+    'Store_Type': [Store_Type],
+    'Store_Current_Age': [Store_Current_Age]  # Changed key name
+})
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    backend_url = "https://vrs1503-superkart-backend.hf.space/v1/predict"  # Ensure correct URL
+    try:
+        response = requests.post(backend_url, json=business_df.to_dict(orient="records")[0])
+        response.raise_for_status()  # Raise an exception for bad status codes
+        data = response.json()
+        if 'prediction' in data:
+            prediction = data['prediction'][0]  # Access the first element of the list
+            st.success(f"Predicted Sales (in dollars): {prediction}")
+        else:
+            st.error(f"Error: 'prediction' key not found in response. Response: {data}")
+    except requests.exceptions.RequestException as e:
+        st.error(f"Error making prediction: {e}")
+
+# Section for batch prediction
+st.subheader("Batch Prediction")
+
+# Allow users to upload a CSV file for batch prediction
+uploaded_file = st.file_uploader("Upload a CSV file", type=["csv"])
+
+# Make predictions when the "Predict" button is clicked
+if uploaded_file is not None:
+    if st.button("Predict Batch"):  # Changed button name to avoid duplication
+        backend_url = "https://vrs1503-superkart-backend.hf.space/v1/batch_predict" # Ensure correct URL
+        try:
+            response = requests.post(backend_url, files={"file": uploaded_file})
+            response.raise_for_status()
+            predictions = response.json()
+            st.success("Batch predictions completed!")
+            st.write(predictions)  # Display the predictions
+        except requests.exceptions.RequestException as e:
+            st.error(f"Error making batch prediction: {e}")

requirements.txt

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+Pandas==2.2.2
+requests==2.32.3
+streamlit==1.45.0
+joblib==1.4.2
+transformers
+tensorflow[and-cuda]==2.18.0