app.py

import gradio as gr
from transformers import GPT2Tokenizer, GPT2LMHeadModel

# Load pre-trained model and tokenizer
model_name = "gpt2"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

def generate_response(prompt):
  score1 = 0
  score2=0
  # Tokenize the prompt
  input_ids = tokenizer.encode(prompt, return_tensors="pt")

  # Generate response using beam search
  output = model.generate(input_ids, max_length=100,
                          num_return_sequences=2, no_repeat_ngram_size=2, num_beams=5)

  # Decode and store responses with basic scoring
  responses = []
  for i, out in enumerate(output):
    response = tokenizer.decode(out, skip_special_tokens=True)
    responses.append(response)

  return responses[0], score1, responses[1], score2

  

# Gradio interface
iface = gr.Interface(
  fn=generate_response,
  inputs=["text"],
  outputs=[gr.Textbox(label="Response 1"),
           gr.Slider(0,5,interactive=True,label="score 1",step=1),
           gr.Textbox(label="Response 2"),
           gr.Slider(0,5,interactive=True,label="score 2",step=1)],
  title="GROUP1_TASK1",
  description="Enter a question to generate responses from GPT-2 model.",
  
)

iface.launch()