app.py

from transformers import AutoModelForCausalLM, AutoTokenizer
import gradio as gr
import torch

title = "EZChat"
description = "A State-of-the-Art Large-scale Pretrained Response generation model Qwen's 7B-Chat"
examples = [["How are you?"]]

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-7B-Chat", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", trust_remote_code=True).eval()

history = []  # Initialize chat history

def predict(input, history=history):
    if input is not None and tokenizer.eos_token is not None:
        combined_input = input + tokenizer.eos_token
        new_user_input_ids = tokenizer.encode(combined_input, return_tensors="pt")

        # Append the new user input tokens to the chat history
        bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)

        # Generate a response
        generated_response_ids = model.generate(
            bot_input_ids, max_length=20, pad_token_id=tokenizer.eos_token_id
        )

        # Convert the generated response tokens to text
        response = tokenizer.decode(generated_response_ids[0], skip_special_tokens=True)

        # Append the user input and generated response to the chat history
        history.extend(new_user_input_ids[0].tolist())
        history.extend(generated_response_ids[0].tolist())

        return response, history

    else:
        print("Input or eos_token is None. Cannot concatenate.")

gr.Interface(
    fn=predict,
    title=title,
    description=description,
    examples=examples,
    inputs=["text", "text"],
    outputs=["text", "text"],
    theme="ParityError/Anime",
).launch()