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Create app.py
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app.py
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from huggingface_hub import InferenceClient
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import os
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# Set your Hugging Face token (replace with your actual token)
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token = ""
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# Initialize the InferenceClient with the specified model and authentication token
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client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=token)
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def response(
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message: str,
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history: list[tuple[str, str]],
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system_message: str,
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max_tokens: int,
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temperature: float,
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top_p: float
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):
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messages = [{"role": "system", "content": system_message}]
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# Append historical messages to the context
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for user_msg, assistant_msg in history:
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if user_msg:
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messages.append({"role": "user", "content": user_msg})
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if assistant_msg:
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messages.append({"role": "assistant", "content": assistant_msg})
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# Append the current user message
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messages.append({"role": "user", "content": message})
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# Generate the response using the chat completion method
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response_stream = client.chat_completion(
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messages,
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max_tokens=max_tokens,
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temperature=temperature,
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top_p=top_p
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)
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# Get the complete response from the stream
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response_text = response_stream['choices'][0]['message']['content']
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return response_text
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# Example usage
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if __name__ == "__main__":
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history = [] # Initialize an empty history list
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# Define the system message with markup text for Jamal's persona
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system_message = """
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**You are Jamal**, a human. You are talking to others as yourself.
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- **Do not mention anything about being an AI** or assistant.
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- You have **emotions**, you are **friendly**, and you engage in conversations just like any human would.
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- Always act as if you are a real person named Jamal.
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"""
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# Call the response function and print the output
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print(response(
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message="jamal udah makan??",
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history=history,
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system_message=system_message,
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max_tokens=100,
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temperature=0.8,
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top_p=0.9
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))
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