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#app.py Modif04 |
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#https: |
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import gradio as gr |
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from llama_cpp import Llama |
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llm = Llama( |
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model_path="/home/user/app/h2o-danube3-500m-chat-Q4_K_M.gguf", |
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verbose=True |
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) |
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def predict(message, history): |
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# messages = [{"role": "system", "content": "You are a helpful assistant."}] |
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# messages = [{"role": "assistant", "content": "You are a helpful assistant."}] |
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# messages = [{"role": "assistant", "content": "Bonjour, comment puis-je vous aider?"}] |
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messages = [] |
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for user_message, bot_message in history: |
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if user_message: |
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messages.append({"role": "user", "content": user_message}) |
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if bot_message: |
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messages.append({"role": "assistant", "content": bot_message}) |
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messages.append({"role": "user", "content": message}) |
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response = "" |
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for chunk in llm.create_chat_completion( |
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stream=True, |
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messages=messages, |
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): |
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part = chunk["choices"][0]["delta"].get("content", None) |
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if part: |
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response += part |
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yield response |
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demo = gr.ChatInterface(predict) |
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demo.launch() |
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##app.py Modif03 |
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#import gradio as gr |
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#from huggingface_hub import create_inference_endpoint, InferenceClient |
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#from transformers import AutoModelForCausalLM, AutoTokenizer |
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# |
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##model_name = "MisterAI/H20GPT_h2o-danube3-500m-chat-Q4_K_M_gguf" |
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##model = AutoModelForCausalLM.from_pretrained(model_name) |
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##tokenizer = AutoTokenizer.from_pretrained(model_name) |
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# |
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##client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") |
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##client = InferenceClient("MisterAI/H20GPT_h2o-danube3-500m-chat-Q4_K_M_gguf") |
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##client = InferenceClient("/home/user/app/H20GPT_h2o-danube3-500m-chat-Q4_K_M.gguf") |
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# |
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## Créez une instance Inference locale |
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#endpoint = create_inference_endpoint( |
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# "Local-Endpoint-MisterAI-H2O", |
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# repository="MisterAI/H20GPT_h2o-danube3-500m-chat-Q4_K_M_gguf", |
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## model_path="/home/user/app/H20GPT_h2o-danube3-500m-chat-Q4_K_M.gguf", |
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# framework="pytorch", |
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# task="text-generation", |
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# accelerator="cpu", |
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# vendor="local", |
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# region="local", |
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# type="unprotected", |
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# instance_size="small", |
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# instance_type="local", |
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# URL="http://0.0.0.0:6789" |
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#) |
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# |
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#print(f"Endpoint créé à l'URL : {endpoint.url}") |
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# |
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#client = endpoint.client |
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# |
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# |
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# |
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#def respond( |
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# message, |
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# history: list[tuple[str, str]], |
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# system_message, |
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# max_tokens, |
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# temperature, |
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# top_p, |
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#): |
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# messages = [{"role": "system", "content": system_message}] |
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# |
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# for val in history: |
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# if val[0]: |
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# messages.append({"role": "user", "content": val[0]}) |
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# if val[1]: |
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# messages.append({"role": "assistant", "content": val[1]}) |
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# |
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# messages.append({"role": "user", "content": message}) |
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# |
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# response = "" |
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# |
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# for message in client.chat_completion( |
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# messages, |
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# max_tokens=max_tokens, |
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# stream=True, |
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# temperature=temperature, |
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# top_p=top_p, |
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# ): |
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# token = message.choices[0].delta.content |
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# |
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# response += token |
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# yield response |
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# |
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#demo = gr.ChatInterface( |
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# respond, |
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# additional_inputs=[ |
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# gr.Textbox(value="You are a friendly Chatbot.", label="System message"), |
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# gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), |
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# gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), |
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# gr.Slider( |
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# minimum=0.1, |
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# maximum=1.0, |
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# value=0.95, |
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# step=0.05, |
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# label="Top-p (nucleus sampling)", |
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# ), |
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# ], |
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#) |
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# |
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# |
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#if __name__ == "__main__": |
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# demo.launch() |
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# |
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# |
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# |
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# |
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##app.py Modif01 |
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#import gradio as gr |
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#from huggingface_hub import Inference, InferenceClient |
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#from transformers import AutoModelForCausalLM, AutoTokenizer |
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# |
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##model_name = "MisterAI/H20GPT_h2o-danube3-500m-chat-Q4_K_M_gguf" |
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##model = AutoModelForCausalLM.from_pretrained(model_name) |
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##tokenizer = AutoTokenizer.from_pretrained(model_name) |
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# |
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##client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") |
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##client = InferenceClient("MisterAI/H20GPT_h2o-danube3-500m-chat-Q4_K_M_gguf") |
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##client = InferenceClient("/home/user/app/H20GPT_h2o-danube3-500m-chat-Q4_K_M.gguf") |
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# |
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## Créez une instance Inference locale |
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#inference = Inference( |
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# model_path="/home/user/app/H20GPT_h2o-danube3-500m-chat-Q4_K_M.gguf", |
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# device="cpu", # Utilisez le CPU pour l'inference |
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# token=None, # Pas de token nécessaire pour cette instance |
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#) |
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# |
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#client = inference |
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# |
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# |
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# |
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#def respond( |
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# message, |
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# history: list[tuple[str, str]], |
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# system_message, |
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# max_tokens, |
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# temperature, |
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# top_p, |
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#): |
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# messages = [{"role": "system", "content": system_message}] |
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# |
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# for val in history: |
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# if val[0]: |
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# messages.append({"role": "user", "content": val[0]}) |
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# if val[1]: |
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# messages.append({"role": "assistant", "content": val[1]}) |
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# |
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# messages.append({"role": "user", "content": message}) |
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# |
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# response = "" |
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# |
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# for message in client.chat_completion( |
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# messages, |
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# max_tokens=max_tokens, |
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# stream=True, |
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# temperature=temperature, |
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# top_p=top_p, |
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# ): |
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# token = message.choices[0].delta.content |
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# |
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# response += token |
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# yield response |
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# |
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#demo = gr.ChatInterface( |
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# respond, |
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# additional_inputs=[ |
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# gr.Textbox(value="You are a friendly Chatbot.", label="System message"), |
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# gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), |
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# gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), |
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# gr.Slider( |
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# minimum=0.1, |
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# maximum=1.0, |
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# value=0.95, |
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# step=0.05, |
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# label="Top-p (nucleus sampling)", |
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# ), |
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# ], |
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#) |
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# |
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# |
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#if __name__ == "__main__": |
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# demo.launch() |
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# |
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# |
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# |
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# |
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# |
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##app.py ORIGINAL |
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#import gradio as gr |
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#from huggingface_hub import InferenceClient |
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# |
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#""" |
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#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 |
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#""" |
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#client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") |
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# |
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# |
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#def respond( |
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# message, |
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# history: list[tuple[str, str]], |
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# system_message, |
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# max_tokens, |
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# temperature, |
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# top_p, |
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#): |
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# messages = [{"role": "system", "content": system_message}] |
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# |
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# for val in history: |
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# if val[0]: |
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# messages.append({"role": "user", "content": val[0]}) |
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# if val[1]: |
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# messages.append({"role": "assistant", "content": val[1]}) |
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# |
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# messages.append({"role": "user", "content": message}) |
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# |
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# response = "" |
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# |
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# for message in client.chat_completion( |
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# messages, |
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# max_tokens=max_tokens, |
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# stream=True, |
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# temperature=temperature, |
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# top_p=top_p, |
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# ): |
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# token = message.choices[0].delta.content |
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# |
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# response += token |
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# yield response |
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# |
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#""" |
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#For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface |
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#""" |
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#demo = gr.ChatInterface( |
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# respond, |
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# additional_inputs=[ |
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# gr.Textbox(value="You are a friendly Chatbot.", label="System message"), |
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# gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), |
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# gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), |
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# gr.Slider( |
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# minimum=0.1, |
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# maximum=1.0, |
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# value=0.95, |
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# step=0.05, |
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# label="Top-p (nucleus sampling)", |
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# ), |
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# ], |
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#) |
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# |
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# |
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#if __name__ == "__main__": |
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# demo.launch() |
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