| import os |
| from io import BytesIO |
|
|
| import gradio as gr |
| import requests |
| from PIL import Image |
|
|
| from lang_sam import SAM_MODELS |
| from lang_sam.server import PORT, server |
|
|
|
|
| def inference(sam_type, box_threshold, text_threshold, image, text_prompt): |
| """Gradio function that makes a request to the /predict LitServe endpoint.""" |
| url = f"http://localhost:{PORT}/predict" |
|
|
| |
| with open(image, "rb") as img_file: |
| files = { |
| "image": img_file, |
| } |
| data = { |
| "sam_type": sam_type, |
| "box_threshold": str(box_threshold), |
| "text_threshold": str(text_threshold), |
| "text_prompt": text_prompt, |
| } |
|
|
| try: |
| response = requests.post(url, files=files, data=data) |
| except Exception as e: |
| print(f"Request failed: {e}") |
| return None |
|
|
| if response.status_code == 200: |
| try: |
| output_image = Image.open(BytesIO(response.content)).convert("RGB") |
| return output_image |
| except Exception as e: |
| print(f"Failed to process response image: {e}") |
| return None |
| else: |
| print(f"Request failed with status code {response.status_code}: {response.text}") |
| return None |
|
|
|
|
| with gr.Blocks(title="lang-sam") as blocks: |
| with gr.Row(): |
| sam_model_choices = gr.Dropdown(choices=list(SAM_MODELS.keys()), label="SAM Model", value="sam2.1_hiera_small") |
| box_threshold = gr.Slider(minimum=0.0, maximum=1.0, value=0.3, label="Box Threshold") |
| text_threshold = gr.Slider(minimum=0.0, maximum=1.0, value=0.25, label="Text Threshold") |
| with gr.Row(): |
| image_input = gr.Image(type="filepath", label="Input Image") |
| output_image = gr.Image(type="pil", label="Output Image") |
| text_prompt = gr.Textbox(lines=1, label="Text Prompt") |
|
|
| submit_btn = gr.Button("Run Prediction") |
|
|
| submit_btn.click( |
| fn=inference, |
| inputs=[sam_model_choices, box_threshold, text_threshold, image_input, text_prompt], |
| outputs=output_image, |
| ) |
|
|
| examples = [ |
| [ |
| "sam2.1_hiera_small", |
| 0.32, |
| 0.25, |
| os.path.join(os.path.dirname(__file__), "assets", "fruits.jpg"), |
| "kiwi. watermelon. blueberry.", |
| ], |
| [ |
| "sam2.1_hiera_small", |
| 0.3, |
| 0.25, |
| os.path.join(os.path.dirname(__file__), "assets", "car.jpeg"), |
| "wheel.", |
| ], |
| [ |
| "sam2.1_hiera_small", |
| 0.3, |
| 0.25, |
| os.path.join(os.path.dirname(__file__), "assets", "food.jpg"), |
| "food.", |
| ], |
| ] |
|
|
| gr.Examples( |
| examples=examples, |
| inputs=[sam_model_choices, box_threshold, text_threshold, image_input, text_prompt], |
| outputs=output_image, |
| ) |
|
|
| server.app = gr.mount_gradio_app(server.app, blocks, path="/gradio") |
|
|
| if __name__ == "__main__": |
| print(f"Starting LitServe and Gradio server on port {PORT}...") |
| server.run(port=PORT) |
|
|
