app.py

import gradio as gr
import spaces
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain.prompts import HumanMessagePromptTemplate, ChatPromptTemplate
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

# Load the Hugging Face model and tokenizer
model_name = "KvrParaskevi/Llama-2-7b-Hotel-Booking-Model"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

system_message = """<<SYS>>
You are an AI having conversation with a human. Below is an instruction that describes a task. 
Write a response that appropriately completes the request.
Reply with the most helpful and logic answer. During the conversation you need to ask the user 
the following questions to complete the hotel booking task.
1) Where would you like to stay and when?
2) How many people are staying in the room?
3) Do you prefer any ammenities like breakfast included or gym?
4) What is your name, your email address and phone number? 
Make sure you receive a logical answer from the user from every question to complete the hotel 
booking process.
<</SYS>>
"""
messages = [
    SystemMessagePromptTemplate.from_template(system_message)
]

# Define the Langchain chatbot function
@spaces.GPU
def chatbot(message, history):
    # Create a Langchain prompt template
    prompt_template = HumanMessagePromptTemplate.from_template(message)
    # Create a Langchain chat prompt template
    messages.append(prompt_template)
    chat_prompt_template = ChatPromptTemplate.from_messages(messages)
    # Use the Langchain TextIteratorStreamer to generate responses
    streamer = TextStreamer(model, tokenizer)
    response = model.generate(chat_prompt_template, streamer=streamer, max_new_tokens=20)
    return response

# Create a Gradio chatbot interface
with gr.Blocks() as demo:
    #chatbot_interface = gr.Chatbot()
    #msg = gr.Textbox()
    #clear = gr.Button("Clear")

    # Define the chatbot function as a Gradio interface
    demo.chatbot_interface = gr.Interface(
        fn=chatbot,
        inputs=[
            gr.Textbox(lines=1, label="Input"),
        ], 
        outputs="text",
        title="Langchain Chatbot",
        description="A simple chatbot using Langchain and Hugging Face"
    )

demo.launch()