Upload folder using huggingface_hub

Dockerfile

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+# 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

Superkart_model_v1.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bf225ec14e320dcfd1c67cd06d5482d3a3df311957bdb91935a5aa2b80d6caf0
+size 222714

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import requests
+
+
+# Streamlit UI for Boston Housing Price Prediction
+st.title("Super Kart sales revenue Predictor App")
+st.write("This app predicts the Revenue of the superkart product .")
+
+st.subheader("Enter the below listing details")
+
+# Collect user input using sliders
+PRODUCT_ID = st.text_input(" Enter product ID (PRODUCT ID)")
+st.write(f"{PRODUCT_ID}")
+PRODUCT_WGT = st.text_input(" Enter product Weight (PRODUCT WGT)")
+st.write(f"{PRODUCT_WGT}")
+PRODUCT_SGR_CNT = st.text_input(" Enter product Weight (PRODUCT SGR CNT)")
+st.write(f"{PRODUCT_SGR_CNT}")
+PRODUCT_ALOC_AREA = st.text_input(" Enter product Allocated Area (PRODUCT ALOC AREA)")
+st.write(f"{PRODUCT_ALOC_AREA}")
+PRODUCT_TYPE = st.text_input(" Enter product Type (PRODUCT TYPE)")
+st.write(f"{PRODUCT_TYPE}")
+PRODUCT_MRP = st.text_input(" Enter product MRP (PRODUCT MRP)")
+st.write(f"{PRODUCT_MRP}")
+STORE_EST_YEAR = st.text_input(" Enter Established Year (STORE EST YEAR)")
+st.write(f"{STORE_EST_YEAR}")
+STORE_SIZE = st.text_input(" Enter Store Size (STORE SIZE)")
+st.write(f"{STORE_SIZE}")
+STORE_LOC = st.text_input(" Enter Store Location (STORE LOC)")
+st.write(f"{STORE_LOC}")
+STORE_TYPE = st.text_input(" Enter Store type (STORE TYPE)")
+st.write(f"{STORE_TYPE}")
+
+
+# Create input DataFrame
+input_data = pd.DataFrame([{
+    'PRODUCT_ID': PRODUCT_ID,
+    'PRODUCT_WGT': PRODUCT_WGT,
+    'PRODUCT_SGR_CNT': PRODUCT_SGR_CNT,
+    'PRODUCT_ALOC_AREA': PRODUCT_ALOC_AREA,
+    'PRODUCT_TYPE': PRODUCT_TYPE,
+    'PRODUCT_MRP': PRODUCT_MRP,
+    'STORE_EST_YEAR': STORE_EST_YEAR,
+    'STORE_SIZE': STORE_SIZE,
+    'STORE_LOC': STORE_LOC,
+    'STORE_TYPE': STORE_TYPE
+}])
+
+if st.button("Predict", type='primary'):
+    response = requests.post("https://ravikmrg6-SuperKartFrontEnd.hf.space/v1/house", json=input_data.to_dict(orient='records')[0])    # enter user name and space name before running the cell
+    if response.status_code == 200:
+        result = response.json()
+        predicted_price = result[""]
+        st.success(f"🏡 Predicted Product Revenu Value: **${predicted_price * 1000:.2f}**")
+    else:
+        st.error("Error in API request")
+
+# Batch Prediction
+st.subheader("Batch Prediction")
+
+file = st.file_uploader("Upload CSV file", type=["csv"])
+if file is not None:
+    if st.button("Predict for Batch", type='primary'):
+        response = requests.post("https://ravikmrg6-SuperKartFrontEnd.hf.space/v1/housebatch", files={"file": file})    # enter user name and space name before running the cell
+        if response.status_code == 200:
+            result = response.json()
+            st.header("Batch Prediction Results")
+            st.write(result)
+        else:
+            st.error("Error in API request")

requirements.txt

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+pandas==2.2.2
+requests==2.32.4
+streamlit==1.43.2
+huggingface_hub>=0.20.0