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| # import gradio as gr | |
| # from huggingface_hub import InferenceClient | |
| # """ | |
| # For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference | |
| # """ | |
| # client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| # def respond( | |
| # message, | |
| # history: list[tuple[str, str]], | |
| # system_message, | |
| # max_tokens, | |
| # temperature, | |
| # top_p, | |
| # ): | |
| # messages = [{"role": "system", "content": system_message}] | |
| # for val in history: | |
| # if val[0]: | |
| # messages.append({"role": "user", "content": val[0]}) | |
| # if val[1]: | |
| # messages.append({"role": "assistant", "content": val[1]}) | |
| # messages.append({"role": "user", "content": message}) | |
| # response = "" | |
| # for message in client.chat_completion( | |
| # messages, | |
| # max_tokens=max_tokens, | |
| # stream=True, | |
| # temperature=temperature, | |
| # top_p=top_p, | |
| # ): | |
| # token = message.choices[0].delta.content | |
| # response += token | |
| # yield response | |
| # """ | |
| # For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface | |
| # """ | |
| # demo = gr.ChatInterface( | |
| # respond, | |
| # additional_inputs=[ | |
| # gr.Textbox(value="You are a friendly Chatbot.", label="System message"), | |
| # gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| # gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| # gr.Slider( | |
| # minimum=0.1, | |
| # maximum=1.0, | |
| # value=0.95, | |
| # step=0.05, | |
| # label="Top-p (nucleus sampling)", | |
| # ), | |
| # ], | |
| # ) | |
| # if __name__ == "__main__": | |
| # demo.launch() | |
| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| from PIL import Image | |
| import io | |
| import base64 | |
| client = InferenceClient("meta-llama/Llama-3.2-11B-Vision-Instruct") | |
| def respond( | |
| message, | |
| history: list[tuple[str, str]], | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| image: Image, # Add image input to the function | |
| ): | |
| # Prepare the system message and history for the conversation | |
| messages = [{"role": "system", "content": system_message}] | |
| for val in history: | |
| if val[0]: | |
| messages.append({"role": "user", "content": val[0]}) | |
| if val[1]: | |
| messages.append({"role": "assistant", "content": val[1]}) | |
| # Add the current user message | |
| messages.append({"role": "user", "content": message}) | |
| # Convert the image to a base64-encoded string | |
| image_bytes = io.BytesIO() | |
| image.save(image_bytes, format='PNG') | |
| image_bytes.seek(0) | |
| image_base64 = base64.b64encode(image_bytes.getvalue()).decode('utf-8') | |
| # Use InferenceClient to handle the image and text input to the model | |
| # Pass the base64-encoded image as the input | |
| response_data = client.text_to_image(images=image_base64, prompt=message) # Pass the base64 string as 'images' | |
| # Check if the response is in the correct format (e.g., image) | |
| try: | |
| # Assuming the response is an image in base64 format | |
| if 'image' in response_data: | |
| image_response = response_data['image'] | |
| print("Image Res:") | |
| print(image_response) | |
| # Decode the base64 image back into an image object | |
| image_bytes = base64.b64decode(image_response) | |
| image = Image.open(io.BytesIO(image_bytes)) | |
| image.show() # Or return the image in Gradio | |
| return "Image processed successfully" # You can return some confirmation or process the image further | |
| else: | |
| return "Error: No valid image returned from the model." | |
| except Exception as e: | |
| return f"Error processing image: {e}" | |
| # Create the Gradio interface with an image input | |
| demo = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Textbox(value="You are a friendly Chatbot.", label="System message"), | |
| gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"), | |
| gr.Image(type="pil", label="Upload an Image"), # Image input for vision tasks | |
| ], | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(share=True) # Set share=True to create a public link | |