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import gradio as gr |
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from huggingface_hub import InferenceClient |
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def respond( |
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message, |
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history, |
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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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hf_token: gr.OAuthToken, |
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): |
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""" |
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Função de resposta usando Hugging Face Inference API. |
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""" |
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client = InferenceClient(token=hf_token.token, model="apple/FastVLM-7B") |
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messages = [{"role": "system", "content": system_message}] |
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if history: |
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for h in history: |
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if isinstance(h, tuple) and len(h) == 2: |
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user_msg, bot_msg = h |
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messages.append({"role": "user", "content": user_msg}) |
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messages.append({"role": "assistant", "content": bot_msg}) |
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messages.append({"role": "user", "content": message}) |
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response = "" |
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try: |
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for message_chunk 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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if hasattr(message_chunk, "choices") and message_chunk.choices: |
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delta = message_chunk.choices[0].delta |
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if delta and hasattr(delta, "content"): |
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response += delta.content |
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yield response |
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except Exception as e: |
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yield f"Erro durante a execução: {str(e)}" |
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chatbot = gr.ChatInterface( |
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respond, |
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type="messages", |
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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(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"), |
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], |
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) |
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with gr.Blocks() as demo: |
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with gr.Sidebar(): |
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gr.LoginButton() |
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chatbot.render() |
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if __name__ == "__main__": |
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demo.launch() |
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