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Update app.py
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app.py
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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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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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def respond(
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message,
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history: list[tuple[str, str]],
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messages.append({"role": "user", "content": message})
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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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],
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)
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if __name__ == "__main__":
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demo.launch()
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import gradio as gr
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from huggingface_hub import InferenceClient
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client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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def respond(
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message,
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history: list[tuple[str, str]],
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messages.append({"role": "user", "content": message})
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# Check for financial-related keywords
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financial_keywords = ["bank", "investment", "financial", "insurance", "savings", "interest", "loan", "mortgage", "credit"]
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if any(keyword in message.lower() for keyword in financial_keywords):
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response = ""
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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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response += token
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return response
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else:
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return "This is a financial FAQ chatbot. Please ask me about banking, investments, insurance, or savings."
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# Initialize Gradio Chat Interface
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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 financial FAQ Chatbot. Only answer questions about finance.", 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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],
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)
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if __name__ == "__main__":
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demo.launch()
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