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Create app.py
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app.py
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import streamlit as st
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from huggingface_hub import InferenceClient
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# Initialize the 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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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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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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messages.append({"role": "user", "content": message})
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response = ""
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response_container = st.empty() # Placeholder to update the response text
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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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response_container.text(response) # Dynamically update the response
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# Streamlit interface
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st.title("Chatbot Interface")
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# User inputs
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system_message = st.text_input("System message", value="You are a friendly Chatbot.")
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message = st.text_input("User message", key="user_message")
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max_tokens = st.slider("Max new tokens", 1, 2048, 512)
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temperature = st.slider("Temperature", 0.1, 4.0, 0.7)
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top_p = st.slider("Top-p (nucleus sampling)", 0.1, 1.0, 0.95)
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# A placeholder for chat history
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history = st.session_state.get('history', [])
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# Submit button
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if st.button("Send"):
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if message:
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# Append the new message to the history
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history.append((message, "")) # No assistant response yet
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# Call the respond function and update the history with the assistant's reply
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for response in respond(message, history, system_message, max_tokens, temperature, top_p):
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history[-1] = (message, response)
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# Save updated history in session state
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st.session_state['history'] = history
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# Display the chat history
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for user_msg, bot_msg in history:
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st.write(f"**User**: {user_msg}")
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st.write(f"**Assistant**: {bot_msg}")
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