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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| import PyPDF2 | |
| import io | |
| """ | |
| For more information n `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 | |
| def extract_text_from_pdf(pdf_file): | |
| if pdf_file is None: | |
| return "No file uploaded." | |
| try: | |
| pdf_reader = PyPDF2.PdfReader(io.BytesIO(pdf_file)) | |
| text = "" | |
| for page in pdf_reader.pages: | |
| text += page.extract_text() + "\n\n" | |
| return text.strip() | |
| except Exception as e: | |
| return f"An error occurred: {str(e)}" | |
| # Update the Chatbot component | |
| """ | |
| 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)", | |
| # ), | |
| # ], | |
| # ) | |
| pdf_interface = gr.Interface( | |
| fn=extract_text_from_pdf, | |
| inputs=gr.File(label="Upload PDF", type="binary"), | |
| outputs="text", | |
| title="PDF Text Extractor", | |
| description="Upload a PDF file to extract its text content." | |
| ) | |
| # Create the tabbed interface | |
| # demo = gr.TabbedInterface( | |
| # interface_list=[demo, pdf_interface], | |
| # tab_names=["Chat", "PDF Extractor"] | |
| # ) | |
| if __name__ == "__main__": | |
| pdf_interface.launch() | |