app.py

import gradio as gr
from transformers import pipeline, GPT2LMHeadModel, GPT2Tokenizer
import re

# Load the fine-tuned GPT-2 model and tokenizer
model_dir = "Manasa1/finetuned_GPT23"
fine_tuned_model = GPT2LMHeadModel.from_pretrained(model_dir)
fine_tuned_tokenizer = GPT2Tokenizer.from_pretrained(model_dir)

# Create a text-generation pipeline
generator = pipeline('text-generation', model=fine_tuned_model, tokenizer=fine_tuned_tokenizer)

# Function to intelligently add relevant hashtags and emojis
def add_relevant_tags(tweet, input_question):
    # Pre-defined mappings of topics to hashtags and emojis
    topic_to_hashtags = {
        "startup": ["#Startups", "#Innovation", "#Entrepreneurship"],
        "AI": ["#AI", "#ArtificialIntelligence", "#Tech"],
        "technology": ["#Technology", "#Future", "#Tech"],
        "future": ["#Future", "#Vision", "#Tech"],
    }
    topic_to_emojis = {
        "startup": "🚀",
        "AI": "🤖",
        "technology": "💻",
        "future": "🌟",
    }

    # Determine topic from input question (using keywords)
    topic = None
    for key in topic_to_hashtags.keys():
        if key.lower() in input_question.lower():
            topic = key
            break

    # Add relevant hashtags and emoji if a topic is detected
    if topic:
        hashtags = " ".join(topic_to_hashtags[topic][:2])  # Take up to 2 hashtags
        emoji = topic_to_emojis[topic]
        tweet = f"{tweet} {emoji} {hashtags}"
    return tweet.strip()

def generate_tweet(input_question):
    # Format the input without "Question:" and "Answer:"
    prompt = input_question.strip()
    
    # Generate the output with a max length of around 200 to 280 characters
    output = generator(prompt, max_length=300, num_return_sequences=1, temperature=0.7, top_p=0.9)
    
    # Extract the generated text
    tweet = output[0]['generated_text']
    
    # Ensure the tweet is between 200 and 280 characters
    tweet_length = len(tweet)
    if tweet_length > 280:
        tweet = tweet[:280]
        last_period = tweet.rfind(".")
        if last_period != -1:
            tweet = tweet[:last_period + 1]
    elif tweet_length < 200:
        tweet = tweet.ljust(200)

    # Add relevant hashtags and emojis
    tweet = add_relevant_tags(tweet, input_question) 

    return tweet

# Create the Gradio interface
interface = gr.Interface(
    fn=generate_tweet,
    inputs=gr.Textbox(label="Enter a prompt/question", placeholder="Write a tweet about AI."),
    outputs=gr.Textbox(label="Generated Tweet"),
    title="Tweet Generator",
    description="Generate concise, relevant tweets enriched with appropriate emojis and hashtags using a fine-tuned GPT-2 model."
)

# Launch the interface
interface.launch()