app.py

import gradio as gr
import os
import torch
import pickle
import gzip

from model import create_flan_T5_model
from timeit import default_timer as timer
from typing import Tuple, Dict

device = "cuda" if torch.cuda.is_available() else "cpu"

### Load example texts ###
questions_texts = []
with open("questions_texts.txt", "r") as file:
  questions_texts = [line.strip() for line in file.readlines()]

system_prompts = []
with open("system_prompts.txt", "r") as file:
  system_prompts = [line.strip() for line in file.readlines()]

response_texts = []
with open("response_texts.txt", "r") as file:
  response_texts = [line.strip() for line in file.readlines()]

### Model and transforms preparation ###
# Create model and tokenizer
model, tokenizer = create_flan_T5_model()

# Load saved weights
model.load_state_dict(
    torch.load(f="flan-t5-small.pth",
               map_location=torch.device("cpu")) # load to CPU
)

### Predict function ###
def predict(selection: str) -> Tuple[Dict, str, float]:
  start_time = timer()

  model.eval()

  # Extract the question part from the selection
  # Assuming the format "Prompt: {prompt} | Question: {question}"
  question = selection.split("| Question: ")[1]

  # Find the index of the question
  idx = questions_texts.index(question)

  # Now, use the index to get the system prompt and actual response
  system_prompt = system_prompts[idx]
  response = response_texts[idx]
#
  input_text = f"context: {system_prompt} question: {question}"

  model_inputs = tokenizer(input_text, return_tensors="pt", max_length=512, padding='max_length', truncation=True).to(device)

  with torch.inference_mode():
    predicted_token_ids = model.generate(input_ids=model_inputs['input_ids'], attention_mask=model_inputs['attention_mask'], max_length=128)

  result = tokenizer.decode(predicted_token_ids[0], skip_special_tokens=True)

  end_time = timer()
  pred_time = round(end_time - start_time, 4)

  return {"Predicted Answer": result}, {"Actual Answer": response}, pred_time

### 4. Gradio app ###
# Create title, description and article
title = "Prompt Answering with Google's flan-t5-small"
description = "[google/flan-t5-small based model](https://huggingface.co/google/flan-t5-small) LLM model trained to take prompts and tasks on the  [HuggingFace 🤗 Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca). [Source Code Found Here](https://colab.research.google.com/drive/1sIScjt_hyNegHC15Y76JVXEOUvdD_2dh?usp=sharing)"
article = "Built with [Gradio](https://github.com/gradio-app/gradio) and [PyTorch](https://pytorch.org/). [Source Code Found Here](https://colab.research.google.com/drive/1sIScjt_hyNegHC15Y76JVXEOUvdD_2dh?usp=sharing)"

dropdown_choices = [f"Prompt: {prompt} | Question: {question}" for prompt, question in zip(system_prompts, questions_texts)]

# Create the Gradio demo
demo = gr.Interface(fn=predict,
    inputs=gr.Dropdown(choices=dropdown_choices, label="Select a Question and Prompt"),
    outputs=[
        gr.JSON(label="Predicted Answer"),
        gr.Textbox(label="Actual Answer"),
        gr.Number(label="Prediction time (s)")
    ],
    title=title,
    description=description,
    article=article)

# Launch the demo
demo.launch()