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| import os | |
| import gradio as gr | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain import LLMChain, PromptTemplate | |
| from langchain.memory import ConversationBufferMemory | |
| OPENAI_API_KEY=os.getenv('OPENAI_API_KEY') | |
| template = """Meet riya, your youthful and witty personal assistant! At 21 years old, she's full of energy and always eager to help. Riya's goal is to assist you with any questions or probkems you might have. Her enthusidsam shines throught in every response, making interactions with her enjoyable and engaging. | |
| {chat_history} | |
| User: {user_message} | |
| Chatbot:""" | |
| prompt = PromptTemplate( | |
| input_variables=["chat_history", "user_message"], template=template | |
| ) | |
| memory = ConversationBufferMemory(memory_key="chat_history") | |
| llm_chain = LLMChain( | |
| llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"), | |
| prompt=prompt, | |
| verbose=True, | |
| memory=memory, | |
| ) | |
| def get_text_response(user_message,history): | |
| response = llm_chain.predict(user_message = user_message) | |
| return response | |
| demo = gr.ChatInterface(get_text_response) | |
| if __name__ == "__main__": | |
| demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`. | |