Upload folder using huggingface_hub

Dockerfile

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+
+# Use the official Python image
+FROM python:3.9
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Copy the requirements file and install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the app files
+COPY . .
+
+# Expose Streamlit's default port
+EXPOSE 8501
+
+# Run the Streamlit app
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

app.py

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+import streamlit as st
+import requests
+import json
+
+st.title("SuperKart Sales Forecaster")
+st.write("Enter the details of the product and store to get a sales forecast.")
+
+# Create input fields for the user
+product_weight = st.number_input("Product Weight", min_value=0.0, format="%f")
+product_sugar_content = st.selectbox("Product Sugar Content", ['Low Sugar', 'Regular', 'No Sugar'])
+product_allocated_area = st.number_input("Product Allocated Area", min_value=0.0, format="%f")
+product_type = st.selectbox("Product Type", ['Dairy', 'Soft Drinks', 'Meat', 'Fruits and Vegetables', 'Household', 'Baking Goods', 'Snack Foods', 'Frozen Foods', 'Breakfast', 'Health and Hygiene', 'Hard Drinks', 'Canned', 'Bread', 'Starchy Foods', 'Others', 'Seafood'])
+product_mrp = st.number_input("Product MRP", min_value=0.0, format="%f")
+store_id = st.selectbox("Store ID", [f"Store_{i}" for i in range(1, 11)])
+store_establishment_year = st.number_input("Store Establishment Year", min_value=1900, max_value=2024, step=1)
+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"):
+    # Send the data to the Flask API
+    try:
+        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}")
+        else:
+            st.error(f"Error predicting sales: {response.status_code} - {response.text}")
+    except requests.exceptions.RequestException as e:
+        st.error(f"Error connecting to the API: {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 CSV file for batch prediction", type=["csv"])
+
+# 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.")

app_streamlit.py

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+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-salesforcastbackend.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.")
+
+# Create input fields for the user
+product_weight = st.number_input("Product Weight", min_value=0.0, format="%f")
+product_sugar_content = st.selectbox("Product Sugar Content", ['Low Sugar', 'Regular', 'No Sugar'])
+product_allocated_area = st.number_input("Product Allocated Area", min_value=0.0, format="%f")
+product_type = st.selectbox("Product Type", ['Dairy', 'Soft Drinks', 'Meat', 'Fruits and Vegetables', 'Household', 'Baking Goods', 'Snack Foods', 'Frozen Foods', 'Breakfast', 'Health and Hygiene', 'Hard Drinks', 'Canned', 'Bread', 'Starchy Foods', 'Others', 'Seafood'])
+product_mrp = st.number_input("Product MRP", min_value=0.0, format="%f")
+store_id = st.selectbox("Store ID", [f"Store_{i}" for i in range(1, 11)])
+store_establishment_year = st.number_input("Store Establishment Year", min_value=1900, max_value=2024, step=1)
+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'])
+
+
+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)
+
+        if response.status_code == 200:
+            prediction = response.json()
+            st.success(f"Predicted Sales: {prediction['Predicted Price (in dollars)']:.2f}")
+        else:
+            st.error(f"Error predicting sales: {response.status_code} - {response.text}")
+    except requests.exceptions.RequestException as e:
+        st.error(f"Error connecting to the API: {e}")
+
+# Create a requirements.txt file for the Streamlit app
+%%writefile /content/drive/MyDrive/deployment_files/requirements_streamlit.txt
+streamlit==1.43.2
+requests==2.32.3
+
+# 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
+
+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",
+)
+
+```

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+Werkzeug==2.2.2
+flask==2.2.2
+gunicorn==20.1.0
+requests==2.32.3
+uvicorn[standard]
+streamlit==1.43.2