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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() | |
| from PIL import Image | |
| import base64 | |
| import io | |
| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| import requests | |
| # Function to convert image to base64 | |
| def image_to_base64(image: Image): | |
| buffered = io.BytesIO() | |
| image.save(buffered, format="PNG") | |
| return base64.b64encode(buffered.getvalue()).decode("utf-8") | |
| # Function to respond to input | |
| def respond(message: str, image: Image): | |
| # Convert image to base64 | |
| image_base64 = image_to_base64(image) | |
| # Initialize the Hugging Face client | |
| client = InferenceClient("your_huggingface_model") | |
| try: | |
| # Call the text-to-image method | |
| response_data = client.text_to_image(images=image_base64, prompt=message) | |
| # Convert the response data (image) into a PIL Image | |
| image_response = Image.open(io.BytesIO(response_data)) | |
| # Format the response in the required 'messages' format | |
| response_message = { | |
| 'role': 'assistant', # Assuming the response is from the assistant | |
| 'content': image_response | |
| } | |
| return response_message | |
| except Exception as e: | |
| return {"role": "assistant", "content": str(e)} | |
| # Define the Gradio interface | |
| def create_interface(): | |
| with gr.Blocks() as demo: | |
| chatbot = gr.Chatbot(type='messages') # 'messages' format for chatbot | |
| message_input = gr.Textbox() | |
| image_input = gr.Image(type='pil') # Image input as PIL image | |
| # Define the interaction | |
| message_input.submit(respond, inputs=[message_input, image_input], outputs=[chatbot]) | |
| return demo | |
| # Launch the interface | |
| if __name__ == "__main__": | |
| create_interface().launch(share=True) # Set share=True if you want to share the link publicly | |