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74 lines
2.6 KiB
Python
74 lines
2.6 KiB
Python
import gradio as gr
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from langchain_openai import ChatOpenAI
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from langchain.memory import ConversationBufferMemory
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from langchain_core.output_parsers import StrOutputParser
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from langchain.prompts.chat import (
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ChatPromptTemplate,
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MessagesPlaceholder,
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HumanMessagePromptTemplate,
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)
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# Initialize LangChain chat model
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llm = ChatOpenAI(
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temperature=0.7,
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model_name="llama-3.2-3B-instruct", # Replace with your specific model or endpoint if required
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openai_api_base="https://hub.societyai.com/models/llama-3-2-3b/openai/v1",
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)
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# Set up the memory for the chatbot
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memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
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# Create a prompt template that includes conversation history
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prompt = ChatPromptTemplate.from_messages([
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MessagesPlaceholder(variable_name="chat_history"),
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HumanMessagePromptTemplate.from_template("{input}")
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])
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# Create the chain using RunnableSequence
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chain = prompt | llm | StrOutputParser()
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# Define the Gradio interface
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with gr.Blocks(css="footer {visibility: hidden}") as demo:
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chatbot = gr.Chatbot()
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msg = gr.Textbox()
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clear = gr.Button("Clear")
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def user(user_message, history):
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"""Appends the user message to the conversation history."""
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history = history or []
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history.append((user_message, None))
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return "", history
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def bot(history):
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"""Processes the conversation history with LangChain."""
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try:
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user_message = history[-1][0]
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# Update the memory with the user's message
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memory.chat_memory.add_user_message(user_message)
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# Get the chat history from memory
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chat_history = memory.chat_memory.messages
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# Generate a response using LangChain
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response = chain.invoke({"input": user_message, "chat_history": chat_history})
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# Update the memory with the assistant's response
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memory.chat_memory.add_ai_message(response)
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# Update the last entry with the assistant's response
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history[-1] = (user_message, response)
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return history
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except Exception as e:
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# Handle exceptions and display an error message
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history[-1] = (user_message, f"[Error]: {str(e)}")
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return history
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def clear_history():
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memory.clear()
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return [], ""
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# Set up the Gradio interface components
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msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
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bot, chatbot, chatbot
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)
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clear.click(fn=clear_history, inputs=None, outputs=[chatbot, msg], queue=False)
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if __name__ == "__main__":
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demo.launch()
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