app.py

# -*- coding: utf-8 -*-
"""Untitled14.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1qm9JqCY6CGVTqzvw3GEAI8BsI_-w1rwP
"""

# !pip install -q -U gradio
# !pip install -q -U torch transformers accelerate einops
# !pip install -q peft

examples = [["Which organelle carries out the synthesis and packaging of digestive enzymes?"],
            ["What is the change in speed of a moving object per unit time?"] ,
            ["What is the formula of carbon tetrafluoride?"]]

import gradio as gr
import torch
from transformers import (
    AutoTokenizer,
    AutoModelForCausalLM,
    TextIteratorStreamer,
    pipeline,
)
from peft import PeftModel, PeftConfig

# Load the tokenizer and models
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True)
config = PeftConfig.from_pretrained("mudogruer/phi-2-SciQ")
base_model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2")
model = PeftModel.from_pretrained(base_model, "mudogruer/phi-2-SciQ")

# Text generation pipeline
phi2 = pipeline(
    "text-generation",
    tokenizer=tokenizer,
    model=model,
    pad_token_id=tokenizer.eos_token_id,
    eos_token_id=tokenizer.eos_token_id,
    device_map="cpu",
)

def generate(message, max_new_tokens):

  # Provide a default value if max_new_tokens is None
    if max_new_tokens is None:
        max_new_tokens = 50  # Default value; adjust as needed based on your typical use case

    instruction = "You are a helpful assistant to 'User'. You do not respond as 'User' or pretend to be 'User'. You only respond once as 'Assistant'."
    final_prompt = f"Instruction: {instruction}\nUser: {message}\nOutput:"

    # Calculate the total length allowed for the model and adjust max_new_tokens
    input_length = len(tokenizer.encode(final_prompt))
    total_max_length = 512  # Adjust based on your model's max length capability
    if input_length + max_new_tokens > total_max_length:
        max_new_tokens = total_max_length - input_length  # Adjust to not exceed total max length

    # Generate text synchronously
    response = phi2(final_prompt, max_new_tokens=max_new_tokens)
    generated_text = response[0]['generated_text']

    # Extract the response
    last_response = generated_text.split('Output:')[-1].strip()
    return last_response

# Gradio interface setup
with gr.Blocks() as demo:
    gr.Markdown("""### Phi-2 Scientific Question Chatbot (Fine-tuned from SciQ dataset)""")
    tokens_slider = gr.Slider(8, 128, value=21, label="Maximum new tokens")
    chatbot = gr.Interface(fn=generate, inputs=["text", tokens_slider], outputs="text", examples=examples)
    demo.launch(share=True)