Upload folder using huggingface_hub

Dockerfile

@@ -0,0 +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
+
+# 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
+
+# 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"]
+
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

@@ -0,0 +1,57 @@
+
+import streamlit as st
+
+st.title("Super Kart Sales Prediction App")
+st.write("This tool predicts sales forecast of a product based on its details. Enter the required information below.")
+
+sugar_contents = ["Low Sugar", "No Sugar", "Regular"]
+product_types = ['Baking Goods', 'Breads', 'Breakfast', 'Canned', 'Dairy', 'Frozen Foods', 'Fruits and Vegetables', 'Hard Drinks',
+                 'Health and Hygiene', 'Household', 'Meat', 'Others', 'Seafood', 'Snack Foods', 'Soft Drinks', 'Starchy Foods']
+establishment_years = [2004, 2009]
+store_sizes = ['Small', 'Medium', 'High']
+city_types = ['Tier 1', 'Tier 2', 'Tier 3']
+store_types = ['Departmental Store','Food Mart','Supermarket Type1', 'Supermarket Type2']
+
+# ProductId = st.text_input("Product Id")
+# ProductWeight = st.number_input("Weight", min_value=0.0, value=50.0)
+# ProductSugarContent = st.selectbox("Sugar Content?",  sugar_contents)
+# ProductAllocatedArea = st.number_input("Allocated Area, e.g 0.056 ", min_value=0.000, value=1.000)
+# ProductType = st.selectbox("Type / Category", product_types)
+# ProductMRP = st.number_input("MRP", min_value=0.0, value=500.0)
+
+# StoreId = st.text_input("Store Id")
+# StoreEstablishmentYear = st.selectbox("Year of Establishment",  establishment_years)
+# StoreSize = st.selectbox("Size", store_sizes)
+# StoreLocationCityType = st.selectbox("Location", store_sizes)
+# StoreType = st.selectbox("Store Type", store_types)
+
+
+# input_data = {
+#     'ProductId': ProductId,
+#     'ProductWeight': ProductWeight,
+#     'ProductSugarContent': ProductSugarContent,
+#     'ProductAllocatedArea': ProductAllocatedArea,
+#     'ProductType': ProductType,
+#     'ProductMRP': ProductMRP,
+
+#     'StoreId': StoreId,
+#     'StoreEstablishmentYear': StoreEstablishmentYear,
+#     'StoreSize': StoreSize,
+#     'StoreLocationCityType': StoreLocationCityType,
+#     'StoreType': StoreType
+# }
+
+# base_url = os.getenv("API_BASE")
+# api_key = os.getenv("API_KEY")
+
+# # base_url = 'https://balakishan77-skart.hf.space'
+# forecast_url = base_url + '/forecast'
+
+# if st.button("Predict", type='primary'):
+#     response = requests.post(forecast_url, json=input_data)
+#     if response.status_code == 200:
+#         result = response.json()
+#         sales_forecast_prediction = result["Prediction"]
+#         st.write(f"Based on the information provided, the sales forecast for product having ID {ProductId} is likely to {sales_forecast_prediction}.")
+#     else:
+#         st.error("Error in API request")

requirements.txt

@@ -0,0 +1,3 @@
+pandas==2.2.2
+requests==2.28.1
+streamlit==1.43.2