app.py

import gradio as gr
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import os
import os.path 
import pickle
import torch
from openai import OpenAI

base_model_id = "meta-llama/Llama-3.2-3B-Instruct"
model_id = "HiGenius/Headline-Generation-Model"

hf_token = os.environ.get('HF_TOKEN')
openai_api_key = os.environ.get('OPENAI_API_KEY')

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

def load_model():
    base_model = AutoModelForCausalLM.from_pretrained(base_model_id, use_auth_token=hf_token)
    model = PeftModel.from_pretrained(base_model, model_id, use_auth_token=hf_token).to(device)
    tokenizer = AutoTokenizer.from_pretrained(base_model_id, use_auth_token=hf_token)
    tokenizer.pad_token = tokenizer.eos_token
    tokenizer.padding_side='left'
    tokenizer.truncation_side="left"

    return tokenizer, model

def summarize_content(content):
    client = OpenAI(api_key=openai_api_key)
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": "Summarize the following article content concisely while preserving key information:"},
            {"role": "user", "content": content}
        ],
        max_tokens=600,
        temperature=0.3
    )
    return response.choices[0].message.content

tokenizer, model = load_model()

guideline_path = "./guidelines.txt"
with open(guideline_path, 'r', encoding='utf-8') as f:
    guidelines = f.read()

def process_prompt(tokenizer, content, video_summary = '', guidelines = None):
    # Check token lengths
    content_tokens = len(tokenizer.encode(content))
    total_tokens = content_tokens
    if video_summary:
        total_tokens += len(tokenizer.encode(video_summary))

    if content_tokens > 850 or total_tokens > 900:
        content = summarize_content(content)
    
    if guidelines:
        system_prompt = "You are a helpful assistant that writes engaging headlines. To maximize engagement, you may follow these proven guidelines:\n" + guidelines
    else:
        system_prompt = "You are a helpful assistant that writes engaging headlines."

    user_prompt = (
        f"Below is an article and its accompanying video summary:\n\n"
        f"Article Content:\n{content}\n\n"
        f"Video Summary:\n{'None' if video_summary == '' else video_summary}\n\n"
        f"Write ONLY a single engaging headline that accurately reflects the article. Do not include any additional text, explanations, or options."
    )
    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_prompt},
    ]
    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    return prompt

def generate_headlines(content, video_summary):
    if not content.strip():
        return "Please enter valid article content."
        
    if not video_summary.strip():
        video_summary = ''
        
    prompt = process_prompt(tokenizer, content, video_summary, guidelines)
    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1024).to(device)
    
    headlines = []
    for i in range(5):
        outputs = model.generate(**inputs,
                               max_new_tokens=60,
                               num_return_sequences=1,
                               do_sample=True,
                               temperature=0.7)
        response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
        response = response.replace('"', '')
        headlines.append(f"Headline {i+1}: {response}")
    
    return "\n\n".join(headlines)

# Create Gradio interface
demo = gr.Interface(
    fn=generate_headlines,
    inputs=[
        gr.Textbox(label="Article Content", placeholder="Type the main content of the article here..."),
        gr.Textbox(label="Video Summary (Optional)", placeholder="Type the summary of the video related to the article...")
    ],
    outputs=gr.Textbox(label="Generated Headlines"),
    title="Article Headline Writer",
    description="Write catchy headlines from content and video summary."
)

if __name__ == "__main__":
    demo.launch()