E-commerce Visual Assistant - AI Productivity Tool

Overview

E-commerce Visual Assistant is a Hugging Face Space that provides an interactive, Gradio-based interface for asking commerce-focused questions about product photos. Users upload a product image and type natural-language queries (for example, “What brand is this?” or “Is this suitable for winter?”). The Space routes image+text inputs to the google/paligemma-3b model to generate context-aware answers and suggestions suited to e-commerce scenarios such as brand identification, attribute extraction, and simple styling or fit guidance. The project is available on Hugging Face (see the Space commit referenced at the provided URL). The tool is intended as a lightweight visual assistant for merchants, product curators, or shoppers who want quick, conversational insight about product images. The interface focuses on ease of use via Gradio’s web UI: upload an image, ask a question, receive a textual response. The Space’s implementation and latest commit are available at the URL supplied by the author; there is no public pricing or commercial plan shown on the Space page.

Model Details

This Space uses the Hugging Face-hosted model google/paligemma-3b to perform multimodal inference on combined image and text inputs. The application leverages Gradio as the front-end to accept image uploads and text prompts, then forwards those inputs to the model to produce conversational answers tailored to commerce questions. The Space repository commit referenced in the URL demonstrates a straightforward Gradio pipeline that accepts an image file and a question string and returns the model-generated answer. Specific low-level model details (exact parameter count, training dataset composition, fine-tuning steps applied in this Space) are not published on the Space page. The model identifier includes "3b", which commonly denotes a ~3 billion parameter class in model naming conventions, but exact parameter counts and architecture details are not provided by the Space itself. The Space’s implementation focuses on multimodal prompt handling rather than exposing model training or benchmark internals. For the authoritative source code and the exact commit used, see the Space commit at the provided URL.

Key Features

• Upload product photos and ask commerce-focused natural-language questions.
• Uses google/paligemma-3b to perform multimodal (image + text) inference.
• Gradio-based web interface for quick, no-code interactions.
• Provides brand identification and product-attribute style responses.
• Single-image conversational workflow tailored to e-commerce queries.

Example Usage

Example (python):

import base64
import requests

# Example: send image + question to Hugging Face Inference API for the model used by the Space.
# NOTE: You must supply a valid HF API token in HF_API_TOKEN.
# The exact input schema for multimodal models can vary; adapt payload if the model expects a different structure.

HF_API_TOKEN = "YOUR_HF_API_TOKEN"
MODEL = "google/paligemma-3b"
API_URL = f"https://api-inference.huggingface.co/models/{MODEL}"

headers = {
    "Authorization": f"Bearer {HF_API_TOKEN}",
    "Content-Type": "application/json",
}

# Load an image and encode as base64
with open("product.jpg", "rb") as f:
    img_b64 = base64.b64encode(f.read()).decode("utf-8")

# Payload: include both image and text question. Some multimodal endpoints accept a dict with keys like "image" and "text".
payload = {
    "inputs": {
        "image": f"data:image/jpeg;base64,{img_b64}",
        "text": "What brand is this and which product category does it belong to?"
    }
}

response = requests.post(API_URL, headers=headers, json=payload, timeout=60)
response.raise_for_status()
print(response.json())

# If you want to interact directly with the Space's web UI, open it in a browser instead:
# import webbrowser
# webbrowser.open('https://huggingface.co/spaces/shravankumar147/ecommerce-visual-assistant')

Benchmarks

Hugging Face model downloads: 0 (Source: https://huggingface.co/spaces/shravankumar147/ecommerce-visual-assistant/commit/28c37121cc7f33d33af68ba77c8a5389804476d6)

Hugging Face model likes: 0 (Source: https://huggingface.co/spaces/shravankumar147/ecommerce-visual-assistant/commit/28c37121cc7f33d33af68ba77c8a5389804476d6)

Key Information

• Category: Productivity
• Type: AI Productivity Tool