app_functions.py

# app_functions.py
from transformers import AutoModelForCausalLM, AutoTokenizer

def Get_DialoGPT_Response(input_text, no_words, user_type):
    model_name = "Rabbiaaa/DialoGPT"
    try:
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        model = AutoModelForCausalLM.from_pretrained(model_name)

        prompt = f"Give an answer for {user_type} of the text given that is '{input_text}' within {no_words} words."
        inputs = tokenizer(prompt, return_tensors="pt")
        outputs = model.generate(inputs["input_ids"], max_new_tokens=int(no_words), do_sample=True, top_k=50)
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        return response
    except Exception as e:
        return f"Error during DialoGPT model execution: {str(e)}"

def Get_DistilGPT_Response(input_text, no_words, user_type):
    model_name = "Rabbiaaa/DistilGPT"
    try:
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        model = AutoModelForCausalLM.from_pretrained(model_name)

        prompt = f"Give an answer for {user_type} of the text given that is '{input_text}' within {no_words} words."
        inputs = tokenizer(prompt, return_tensors="pt")
        outputs = model.generate(inputs["input_ids"], max_new_tokens=int(no_words), do_sample=True, top_k=50)
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        return response
    except Exception as e:
        return f"Error during DistilGPT model execution: {str(e)}"

def Get_MedGPT_Response(input_text, no_words, user_type):
    model_name = "Rabbiaaa/MedGPT"
    try:
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        model = AutoModelForCausalLM.from_pretrained(model_name)

        prompt = f"Give an answer for {user_type} of the text given that is '{input_text}' within {no_words} words."
        inputs = tokenizer(prompt, return_tensors="pt")
        outputs = model.generate(inputs["input_ids"], max_new_tokens=int(no_words), do_sample=True, top_k=50)
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        return response
    except Exception as e:
        return f"Error during MedGPT model execution: {str(e)}"