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import gradio as gr
from langchain.llms import HuggingFacePipeline
from langchain import LLMChain, PromptTemplate
from langchain.memory import ConversationBufferMemory
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
# Load a free model from Hugging Face
model_name = "microsoft/DialoGPT-medium" # Or try "tiiuae/falcon-rw-1b" or "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Create pipeline
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_length=1000, do_sample=True)
# Wrap with HuggingFacePipeline
llm = HuggingFacePipeline(pipeline=pipe)
template = """You are a helpful assistant to answer user queries.
{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=llm,
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,
examples=["How are you doing?", "What are your interests?", "Which places do you like to visit?"]
)
if __name__ == "__main__":
demo.queue().launch(share=True, debug=True)
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