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()

import os
import gradio as gr
from langchain.chat_models import ChatOpenAI
from langchain.schema import AIMessage, HumanMessage

# Set OpenAI API Key
os.environ["OPENAI_API_KEY"] = "sk-3_mJiR5z9Q3XN-D33cgrAIYGffmMvHfu5Je1U0CW1ZT3BlbkFJA2vfSvDqZAVUyHo2JIcU91XPiAq424OSS8ci29tWMA"  # Replace with your key

# Initialize the ChatOpenAI model
llm = ChatOpenAI(temperature=1.0, model="gpt-3.5-turbo-0613")

# Function to predict response
def get_text_response(message, history=None):
    # Ensure history is a list
    if history is None:
        history = []
    
    # Convert the Gradio history format to LangChain message format
    history_langchain_format = []
    for human, ai in history:
        history_langchain_format.append(HumanMessage(content=human))
        history_langchain_format.append(AIMessage(content=ai))
    
    # Add the new user message to the history
    history_langchain_format.append(HumanMessage(content=message))

    # Get the model's response
    gpt_response = llm(history_langchain_format)

    # Append AI response to history
    history.append((message, gpt_response.content))

    # Return the response and updated history
    return gpt_response.content, history

# Create a Gradio chat interface
demo = gr.Interface(
    fn=get_text_response, 
    inputs=["text", "state"], 
    outputs=["text", "state"]
)

if __name__ == "__main__":
    demo.launch()