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| import os | |
| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| import markdown | |
| client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| # Function to read and process Markdown files from the 'data' directory and its subfolders | |
| def load_markdown_files(data_folder='data'): | |
| documents = [] | |
| for root, dirs, files in os.walk(data_folder): | |
| for filename in files: | |
| if filename.endswith('.md'): | |
| file_path = os.path.join(root, filename) | |
| try: | |
| with open(file_path, 'r', encoding='utf-8') as file: | |
| content = file.read() | |
| # Convert Markdown to plain text if needed | |
| html_content = markdown.markdown(content) | |
| documents.append(html_content) # Store HTML content or plain text | |
| except Exception as e: | |
| print(f"Error reading {file_path}: {e}") | |
| return documents | |
| # Load documents at startup | |
| documents = load_markdown_files() | |
| def respond( | |
| message, | |
| history: list[tuple[str, str]], | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| ): | |
| messages = [{"role": "system", "content": system_message}] | |
| # Retrieve relevant context from loaded documents based on the user message | |
| relevant_contexts = retrieve_relevant_context(message, documents) | |
| # Add retrieved contexts to the messages for better responses | |
| messages.append({"role": "context", "content": " ".join(relevant_contexts)}) | |
| 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 retrieve_relevant_context(query, documents): | |
| # Simple keyword matching to find relevant documents | |
| relevant_contexts = [] | |
| for doc in documents: | |
| if query.lower() in doc.lower(): # Basic keyword search | |
| relevant_contexts.append(doc) | |
| return relevant_contexts[:3] # Return top 3 relevant contexts | |
| demo = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Textbox(value="You are a friendly Chatbot. Only answer questions that you have knowledge of, in the language of Traditional Chinese.", 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)", | |
| ), | |
| ], | |
| ) | |
| #To create a public link, set `share=True` in `launch()`. | |
| if __name__ == "__main__": | |
| demo.launch(share=True) |