app.py

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()