app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import random
from datetime import datetime
from PyPDF2 import PdfReader
import json
from transformers import AutoModelForCausalLM, AutoTokenizer

# Replace 'username/your_model_name' with your Hugging Face model name
model_dir = "Manasa1/your_model_name"
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 generate tweets
def generate_tweet(prompt):
    input_prompt = f"{prompt}\n\nTweet:"  # Format input for clarity
    output = generator(
        input_prompt,
        max_length=50,  # Limit the total length of the generated text
        num_return_sequences=1,
        temperature=0.7,  # Control creativity
        top_p=0.9,  # Use nucleus sampling
        pad_token_id=fine_tuned_tokenizer.eos_token_id,  # Avoid padding issues
    )
    # Extract the generated text and remove the input prompt from the output
    generated_tweet = output[0]['generated_text'].replace(input_prompt, "").strip()
    return generated_tweet

# Gradio Interface
interface = gr.Interface(
    fn=generate_tweet,
    inputs=gr.inputs.Textbox(label="Prompt", placeholder="Enter a topic for the tweet (e.g., AI, technology)"),
    outputs=gr.outputs.Textbox(label="Generated Tweet"),
    title="AI Tweet Generator",
    description="Enter a topic or phrase, and the AI will generate a creative tweet. Powered by a fine-tuned GPT-2 model."
)

# Launch the app
interface.launch()