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| 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 dynamically truncate output while keeping it meaningful | |
| def truncate_tweet(tweet, max_length=240): | |
| # Ensure the tweet is concise, ending on a complete sentence | |
| if len(tweet) > max_length: | |
| tweet = tweet[:max_length] | |
| last_period = tweet.rfind(".") | |
| if last_period != -1: | |
| tweet = tweet[:last_period + 1] | |
| return tweet.strip() | |
| # 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 | |
| output = generator(prompt, max_length=150, num_return_sequences=1, temperature=0.7, top_p=0.9) | |
| # Extract the generated text and clean it | |
| tweet = output[0]['generated_text'] | |
| tweet = re.sub(r"(Question:|Answer:)", "", tweet).strip() # Remove "Question:" and "Answer:" | |
| tweet = truncate_tweet(tweet) # Truncate to ensure it's concise | |
| tweet = add_relevant_tags(tweet, input_question) # Add relevant hashtags and emojis | |
| 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() | |