Upload folder using huggingface_hub

Dockerfile

@@ -1,16 +1,19 @@
-FROM python:3.9-slim
+
+# Use the official Python image
+FROM python:3.9
 
 # Set the working directory inside the container
 WORKDIR /app
 
-# Copy all files from the current directory to the container's working directory
+# 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 . .
 
-# Install dependencies from the requirements file without using cache to reduce image size
-RUN pip install --no-cache-dir --upgrade -r requirements.txt
+# Expose Streamlit's default port
+EXPOSE 8501
 
-# Define the command to start the application using Gunicorn with 4 worker processes
-# - `-w 4`: Uses 4 worker processes for handling requests
-# - `-b 0.0.0.0:7860`: Binds the server to port 7860 on all network interfaces
-# - `app:app`: Runs the Flask app (assuming `app.py` contains the Flask instance named `app`)
-CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:sales_forecaster_api"]
+# Run the Streamlit app
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

app.py

@@ -1,94 +1,71 @@
-# Import necessary libraries
-import numpy as np
-import joblib  # For loading the serialized model
-import pandas as pd  # For data manipulation
-from flask import Flask, request, jsonify  # For creating the Flask API
-
-# Initialize the Flask application
-sales_forecaster_api = Flask("SuperKart Sales Forecast")
-
-# Load the trained machine learning model
-model = joblib.load("/content/drive/MyDrive/Python/Project7/deployment_files/sales_prediction_v1_0.joblib")
-
-# Define a route for the home page (GET request)
-@sales_forecaster_api.get('/')
-def home():
-    """
-    This function handles GET requests to the root URL ('/') of the API.
-    It returns a simple welcome message.
-    """
-    return "Welcome to the SuperKart Sales Forecast API!"
-
-# Define an endpoint for single property prediction (POST request)
-@sales_forecaster_api.post('/v1/sales')
-def predict_sales_price():
-    """
-    This function handles POST requests to the '/v1/sales' endpoint.
-    It expects a JSON payload containing property details and returns
-    the predicted rental price as a JSON response.
-    """
-    # Get the JSON data from the request body
-    property_data = request.get_json()
-
-    # Extract relevant features from the JSON data
-    sample = {
-        'Product_Weight': property_data['Product_Weight'],
-        'Product_Sugar_Content': property_data['Product_Sugar_Content'],
-        'Product_Allocated_Area': property_data['Product_Allocated_Area'],
-        'Product_Type': property_data['Product_Type'],
-        'Product_MRP': property_data['Product_MRP'],
-        'Store_Id': property_data['Store_Id'],
-        'Store_Establishment_Year': property_data['Store_Establishment_Year'],
-        'Store_Size': property_data['Store_Size'],
-        'Store_Location_City_Type': property_data['Store_Location_City_Type'],
-        'Store_Type': property_data['Store_Type'],
+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-SalesForcasterFrontend.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,
     }
 
-    # Convert the extracted data into a Pandas DataFrame
-    input_data = pd.DataFrame([sample])
-
-    # Make prediction (get log_price)
-    predicted_sales_price = model.predict(input_data)[0]
-
-    # Calculate actual price
-    predicted_price = np.exp(predicted_sales_price)
-
-    # Convert predicted_price to Python float
-    predicted_price = round(float(predicted_price), 2)
-    # The conversion above is needed as we convert the model prediction (log price) to actual price using np.exp, which returns predictions as NumPy float32 values.
-    # When we send this value directly within a JSON response, Flask's jsonify function encounters a datatype error
-
-    # Return the actual price
-    return jsonify({'Predicted Price (in dollars)': predicted_price})
-
+    # Send the data to the Flask API
+    try:
+        response = requests.post(API_URL, json=input_data)
 
-# Define an endpoint for batch prediction (POST request)
-@sales_forecaster_api.post('/v1/salesbatch')
-def predict_sales_price_batch():
-    """
-    This function handles POST requests to the '/v1/salesbatch' endpoint.
-    It expects a CSV file containing property details for multiple properties
-    and returns the predicted rental prices as a dictionary in the JSON response.
-    """
-    # Get the uploaded CSV file from the request
-    file = request.files['file']
+        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}")
 
-    # Read the CSV file into a Pandas DataFrame
-    input_data = pd.read_csv(file)
 
-    # Make predictions for all properties in the DataFrame (get log_prices)
-    predicted_log_prices = model.predict(input_data).tolist()
+# Upload the Streamlit app file and requirements file to Hugging Face Space
+from huggingface_hub import upload_file
 
-    # Calculate actual prices
-    predicted_prices = [round(float(np.exp(log_price)), 2) for log_price in predicted_log_prices]
+repo_id_frontend = "pkulkar/SalesForcasterFrontend" # Replace with your Hugging Face Space ID for the frontend
 
-    # Create a dictionary of predictions with property IDs as keys
-    property_ids = input_data['id'].tolist()  # Assuming 'id' is the property ID column
-    output_dict = dict(zip(property_ids, predicted_prices))  # Use actual prices
+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",
+)
 
-    # Return the predictions dictionary as a JSON response
-    return output_dict
+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",
+)
 
-# Run the Flask application in debug mode if this script is executed directly
-if __name__ == '__main__':
-    sales_forecaster_api.run(debug=True)
+```

app_streamlit.py

@@ -0,0 +1,75 @@
+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

@@ -3,3 +3,9 @@ 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