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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| import pandas as pd | |
| import json | |
| import os | |
| import re | |
| import uuid | |
| client = InferenceClient("tiiuae/falcon-7b-instruct") # HuggingFaceH4/zephyr-7b-beta | |
| def trigger_example(example): | |
| chat, updated_history = generate_response(example) | |
| return chat, updated_history | |
| def respond( | |
| message, | |
| history: list[tuple[str, str]], | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| uploaded_file, | |
| ): | |
| 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]}) | |
| if uploaded_file is not None: | |
| with open(uploaded_file.name, "r") as f: | |
| file_content = f.read() | |
| messages.append({"role": "user", "content": f"{message}\n\nFile content:\n{file_content}"}) | |
| else: | |
| 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 | |
| if uploaded_file is not None: | |
| print(f"Uploaded file: {uploaded_file.name}") | |
| if uploaded_file.name.endswith(".csv"): | |
| try: | |
| df = pd.read_csv(uploaded_file.name) | |
| print(f"CSV file loaded with {len(df)} rows and {len(df.columns)} columns.") | |
| json_data = df.to_json(orient="records") | |
| with open(f"{uploaded_file.name.split('.')[0]}.json", "w") as json_file: | |
| json_file.write(json_data) | |
| print(f"JSON file created: {uploaded_file.name.split('.')[0]}.json") | |
| except Exception as e: | |
| print(f"Error loading CSV file: {e}") | |
| elif uploaded_file.name.endswith(".txt"): | |
| try: | |
| with open(uploaded_file.name, "r") as f: | |
| text = f.read() | |
| print(f"Text file loaded with {len(text)} characters.") | |
| json_data = json.dumps({"text": text}) | |
| with open(f"{uploaded_file.name.split('.')[0]}.json", "w") as json_file: | |
| json_file.write(json_data) | |
| print(f"JSON file created: {uploaded_file.name.split('.')[0]}.json") | |
| except Exception as e: | |
| print(f"Error loading text file: {e}") | |
| def clear_chat(): | |
| return [], [], str(uuid.uuid4()) | |
| examples = [ | |
| "Explain the relativity theory in French", | |
| "Como sair de um helicóptero que caiu na água?", | |
| "¿Cómo le explicarías el aprendizaje automático a un extraterrestre?", | |
| "Explain gravity to a chicken.", | |
| "Give me an example of an endangered species and let me know what I can do to help preserve it", | |
| "Formally introduce the transformer architecture with notation.", | |
| ] | |
| demo = gr.ChatInterface( | |
| respond, | |
| title="Nixie Steamcore, a hotbot!", | |
| additional_inputs=[ | |
| gr.Textbox(value="Nixie Steamcore, a hotbot!", label="System message"), | |
| gr.Slider(minimum=1, maximum=2048, value=2048, step=1, label="Max new tokens"), | |
| gr.Slider(minimum=0.1, maximum=4.0, value=1.2, 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.File(label="Upload a document"), | |
| ], | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(debug=True) | |
| """ | |
| if __name__ == "__main__": | |
| # demo.launch(debug=True) | |
| try: | |
| demo.queue(api_open=False, max_size=40).launch(show_api=False) | |
| except Exception as e: | |
| print(f"Error: {e}") | |
| """ | |
| """ | |
| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| 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 | |
| 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() | |
| """ |