app.py

import gradio as gr
import os
from huggingface_hub import login

from transformers import AutoModelForSeq2SeqLM, T5Tokenizer
from peft import PeftModel, PeftConfig


token = os.environ.get("token")
login(token)
print("login is succesful")
max_length=512

MODEL_NAME = "google/flan-t5-base"
tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME, token=token)
config = PeftConfig.from_pretrained("Orcawise/eu_ai_act_orcawise_july12")
base_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base")
model = PeftModel.from_pretrained(base_model, "Orcawise/eu_ai_act_orcawise_july12")

#gr.Interface.from_pipeline(pipe).launch()

def generate_text(prompt):
    """Generates text using the PEFT model.
    Args:
        prompt (str): The user-provided prompt to start the generation.
           Returns:
        str: The generated text.
    """


    # Preprocess the prompt
    # inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    inputs = tokenizer(prompt, return_tensors="pt")

    # Generate text using beam search
    outputs = model.generate(
       input_ids = inputs["input_ids"], 
       max_length=max_length, 
       num_beams=5,
       repetition_penalty=1.5,
       temperature=1,
       top_p=0.5,
       early_stopping=True

       )
    print("show the output", outputs)
    # Decode the generated tokens
    generated_text = tokenizer.decode(outputs, skip_special_tokens=True)[0]
    print("show the generated text", generated_text)
    return generated_text
#############
custom_css="""
.message.pending {
    background: #A8C4D6;
}
/* Response message */
.message.bot.svelte-1s78gfg.message-bubble-border {
    /* background:  white; */
    border-color: #266B99
}
/* User message */
.message.user.svelte-1s78gfg.message-bubble-border{
    background: #9DDDF9;
    border-color: #9DDDF9
    
}   
/* For both user and response message as per the document */
span.md.svelte-8tpqd2.chatbot.prose p {
    color: #266B99; 
}
/* Chatbot comtainer */
.gradio-container{
    /* background:  #84D5F7 */
}
/* RED (Hex: #DB1616) for action buttons and links only */
.clear-btn {
  background: #DB1616;
  color: white;
}
/* #84D5F7 - Primary colours are set to be used for all sorts */
.submit-btn {
  background: #266B99; 
  color: white;
}
"""

### working correctly but  the welcoming message isnt rendering
with gr.Blocks(css=custom_css) as demo:
    chatbot = gr.Chatbot()
    msg = gr.Textbox(placeholder="Ask your question...")  # Add placeholder text
    submit_button = gr.Button("Submit", elem_classes="submit-btn")
    clear = gr.Button("Clear", elem_classes="clear-btn")


    def user(user_message, history):
        return "", history + [[user_message, None]]


    def bot(history):
      history[-1][1] = ""  # Update the last bot message (welcome message or response)
      if len(history) < 0:  # Check if it's the first interaction
          bot_message = "Hi there! How can I help you today?"
          history.append([None, bot_message])  # Add welcome message to history
          for character in bot_message:
            history[-1][1] += character
            yield history  # Yield the updated history character by character

      else:
          previous_message = history[-1][0]  # Access the previous user message
          bot_message = generate_text(previous_message)  # Generate response based on previous message
          for character in bot_message:
            history[-1][1] += character
            yield history  # Yield the updated history character by character



    # Connect submit button to user and then bot functions
    submit_button.click(user, [msg,  chatbot], [msg, chatbot], queue=False).then(
        bot, chatbot, chatbot
    )

    # Trigger user function on Enter key press (same chain as submit button)
    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
        bot, chatbot, chatbot
    )

    clear.click(lambda: None, None, chatbot, queue=False)

demo.launch()