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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
model_id = "GannaEslam38/Pegasus-Arxiv-Generator"
print("🔄 Loading Model...")
try:
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
print("✅ Model Loaded!")
except Exception as e:
print(f"❌ Error loading model: {e}")
def generate_text(prompt):
print(f"📩 Input received: {prompt}")
if len(prompt.split()) < 3:
return "⚠️ text is too short, please write a full sentence."
try:
inputs = tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True)
summary_ids = model.generate(
inputs["input_ids"],
max_length=120,
min_length=10,
num_beams=1,
early_stopping=True
)
decoded = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
cleaned_text = decoded.replace("<n>", " ").replace(" .", ".").strip()
return cleaned_text
except Exception as e:
return f"Error: {str(e)}"
interface = gr.Interface(
fn=generate_text,
inputs=gr.Textbox(lines=5, label="Input Text", placeholder="Write your topic here..."),
outputs=gr.Textbox(lines=10, label="Generated Content"),
title="Generative AI Project",
description="Fine-tuned Pegasus Model.",
cache_examples=False
)
if __name__ == "__main__":
interface.launch() |