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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| from transformers import AutoModelForCausalLM, pipeline | |
| # Use a pipeline as a high-level helper | |
| pipe = pipeline("visual-question-answering", model="dandelin/vilt-b32-finetuned-vqa", trust_remote_code=True) | |
| # Load model directly | |
| model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large", trust_remote_code=True) | |
| """ | |
| 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, system_message, max_tokens, temperature, top_p): | |
| """ | |
| Generates a response based on the user message and chat history. | |
| Args: | |
| message (str): The user message. | |
| history (list): A list of tuples containing user and assistant messages. | |
| system_message (str): The system message. | |
| max_tokens (int): Maximum number of tokens for the response. | |
| temperature (float): Temperature for the response generation. | |
| top_p (float): Top-p for nucleus sampling. | |
| Yields: | |
| str: The generated response. | |
| """ | |
| 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 | |
| def process_video(video): | |
| """ | |
| Processes the uploaded video file. | |
| Args: | |
| video (gr.Video): The uploaded video file. | |
| Returns: | |
| str: Confirmation message for the uploaded video. | |
| """ | |
| return f"Processing video: {video.name}" | |
| def process_pdf(pdf): | |
| """ | |
| Processes the uploaded PDF file. | |
| Args: | |
| pdf (gr.File): The uploaded PDF file. | |
| Returns: | |
| str: Confirmation message for the uploaded PDF. | |
| """ | |
| return f"Processing PDF: {pdf.name}" | |
| def process_image(image): | |
| """ | |
| Processes the uploaded image file. | |
| Args: | |
| image (gr.Image): The uploaded image file. | |
| Returns: | |
| str: Confirmation message for the uploaded image. | |
| """ | |
| return f"Processing image: {image.name}" | |
| # Define upload interfaces | |
| video_upload = gr.Interface(fn=process_video, inputs=gr.Video(), outputs="text", title="Upload a Video") | |
| pdf_upload = gr.Interface(fn=process_pdf, inputs=gr.File(file_types=['.pdf']), outputs="text", title="Upload a PDF") | |
| image_upload = gr.Interface(fn=process_image, inputs=gr.Image(), outputs="text", title="Upload an Image") | |
| # Combine upload interfaces into tabs | |
| tabbed_interface = gr.TabbedInterface([video_upload, pdf_upload, image_upload], ["Video", "PDF", "Image"]) | |
| # Main Gradio interface | |
| demo = gr.Blocks() | |
| with demo: | |
| with gr.Tab("Chat Interface"): | |
| 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)", | |
| ), | |
| ], | |
| ) | |
| with gr.Tab("Upload Files"): | |
| tabbed_interface | |
| if __name__ == "__main__": | |
| demo.launch() | |