README.md

metadata

title: Trace Visualizer
emoji: 🐢
colorFrom: green
colorTo: red
sdk: gradio
sdk_version: 6.5.1
app_file: app.py
pinned: false

Trace Model Visualizer

Gradio app for visualizing trace/trajectory predictions from mihirgrao/trace-model.

Features

• Image input: Upload an image
• Trace prediction: Model predicts trajectory points from the image
• Visual overlay: Trace is overlaid on the image with gradient coloring (green start → red end)
• Coordinate output: Predicted trace points are printed below

Installation

pip install -r requirements.txt

Usage

Gradio app

python app.py

Then open the URL (default: http://localhost:7860).

1. Click Load Model to load the trace model (first run downloads from Hugging Face)
2. Upload an image and optionally enter a task instruction (e.g. "Pick up the red block")
3. Click Run Inference
4. View the overlay image and predicted trace points

Eval server

Run a FastAPI server for batch evaluation (e.g. from scripts or the Gradio app):

python eval_server.py --model-id mihirgrao/trace-model --port 8001

Endpoints:

• POST /predict – single image + instruction
• POST /predict_batch – batch of {image_path?|image_base64?, instruction} samples
• GET /health, GET /model_info

CLI script

python predict_trace.py image.png
python predict_trace.py image.png -i "Pick up the red block"
python predict_trace.py image.png -o output_trace.png -i "Stack the cube on the block"
python predict_trace.py image.png -o output.png -m mihirgrao/trace-model

• image – Path to input image
• -i, --instruction – Task / language instruction (e.g. "Pick up the red block")
• -o, --output – Where to save the overlay (default: <image>_trace.png)
• -m, --model-id – Model ID (default: mihirgrao/trace-model)
• -p, --prompt – Full prompt override (if set, ignores -i)