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import os
import gradio as gr
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory
# Set OpenAI API Key
OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
# Define the template for the chatbot's response
template = """You are a helpful assistant to answer all user queries.
{chat_history}
User: {user_message}
Chatbot:"""
# Define the prompt template
prompt = PromptTemplate(
input_variables=["chat_history", "user_message"],
template=template
)
# Initialize conversation memory
memory = ConversationBufferMemory(memory_key="chat_history")
# Define the LLM chain with the ChatOpenAI model and conversation memory
llm_chain = LLMChain(
llm=ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo"), # Use 'model' instead of 'model_name'
prompt=prompt,
verbose=True,
memory=memory,
)
# Function to get chatbot response
def get_text_response(user_message, history):
response = llm_chain.predict(user_message=user_message)
return response
# Create a Gradio chat interface
demo = gr.Interface(fn=get_text_response, inputs="text", outputs="text")
if __name__ == "__main__":
demo.launch()
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