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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load model and tokenizer directly from Hugging Face Hub
model = AutoModelForCausalLM.from_pretrained("Manasa1/Llama-2-7b-chat-finetune")
tokenizer = AutoTokenizer.from_pretrained("Manasa1/Llama-2-7b-chat-finetune")
def generate_tweet():
prompt = "Write a concise, creative tweet reflecting the style and personality in the fine-tuned dataset."
# Tokenize the input prompt
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=100, padding=True)
# Explicitly set the pad_token_id
model.config.pad_token_id = model.config.eos_token_id
# Generate the tweet with the attention mask
outputs = model.generate(
inputs["input_ids"],
attention_mask=inputs["attention_mask"], # Pass attention_mask explicitly
max_length=140,
num_return_sequences=1,
top_p=0.8,
temperature=0.6,
repetition_penalty=1.2, # Penalize repetition
)
# Decode and return the generated tweet
generated_tweet = tokenizer.decode(outputs[0], skip_special_tokens=True)
return generated_tweet.strip()
# Gradio Interface
with gr.Blocks() as app:
gr.Markdown("# AI Tweet Generator")
gr.Markdown("Click the button below to generate a tweet reflecting the fine-tuned personality.")
generate_button = gr.Button("Generate")
output_box = gr.Textbox(label="Generated Tweet")
generate_button.click(generate_tweet, inputs=None, outputs=output_box)
# Launch the app locally
app.launch()
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