Upload folder using huggingface_hub

Dockerfile

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+# Use python 3.9 as base image
+FROM python:3.9
+
+# Set working directory
+WORKDIR /code
+
+# Copy requirements and install
+COPY requirements.txt /code/requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Copy the rest of the app code
+COPY . /code
+
+# Command to run the app
+CMD ["python", "app.py"]

app.py

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+import gradio as gr
+import pandas as pd
+import joblib
+from huggingface_hub import hf_hub_download
+import os
+
+# 1. Load the model from Hugging Face Hub
+REPO_ID = "your_username/superkart-sales-predictor"
+model_path = hf_hub_download(repo_id=REPO_ID, filename="model.joblib")
+model = joblib.load(model_path)
+
+def predict_sales(Product_Weight, Product_Sugar_Content, Product_Allocated_Area, 
+                  Product_Type, Product_MRP, Store_Establishment_Year, 
+                  Store_Size, Store_Location_City_Type, Store_Type):
+    
+    # 2. Get inputs and save into a dataframe
+    input_data = pd.DataFrame([{
+        '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_Establishment_Year': Store_Establishment_Year,
+        'Store_Size': Store_Size,
+        'Store_Location_City_Type': Store_Location_City_Type,
+        'Store_Type': Store_Type
+    }])
+    
+    prediction = model.predict(input_data)[0]
+    return f"Estimated Total Sales: ${round(prediction, 2)}"
+
+# 3. Define the Gradio Interface
+interface = gr.Interface(
+    fn=predict_sales,
+    inputs=[
+        gr.Number(label="Product Weight"),
+        gr.Dropdown(["Low Sugar", "Regular", "No Sugar"], label="Sugar Content"),
+        gr.Slider(0, 1, label="Allocated Area Ratio"),
+        gr.Dropdown(['Frozen Foods', 'Dairy', 'Canned', 'Baking Goods', 'Health and Hygiene', 'Others'], label="Product Type"),
+        gr.Number(label="Product MRP"),
+        gr.Number(label="Store Establishment Year"),
+        gr.Dropdown(["Small", "Medium", "High"], label="Store Size"),
+        gr.Dropdown(["Tier 1", "Tier 2", "Tier 3"], label="City Type"),
+        gr.Dropdown(["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"], label="Store Type")
+    ],
+    outputs="text",
+    title="SuperKart Sales Forecast",
+    description="Enter product and store details to predict total sales."
+)
+
+if __name__ == "__main__":
+    interface.launch()

models/sales_rf_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e129dc8d0cc1b80e6597ae5ec6bbe180608917fc2e591a7bdda6588fa5ad09d8
+size 49992634

requirements.txt

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+pandas
+joblib
+scikit-learn
+huggingface_hub
+gradio