import gradio as gr
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM

# Initialize the image classification pipeline
classifier = pipeline("image-classification", model="google/vit-base-patch16-224")

# Initialize the tokenizer and model for the generative text
model_name = "EleutherAI/gpt-neo-2.7B"  # Using GPT-Neo for demonstration
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def generate_tweet(label):
    # Generate a tweet about the label
    prompt = f"write a tweet about {label}"

    inputs = tokenizer.encode(prompt, return_tensors="pt", add_special_tokens=True)
    outputs = model.generate(inputs, max_length=280, num_return_sequences=1, no_repeat_ngram_size=2)

    tweet = tokenizer.decode(outputs[0], skip_special_tokens=True)

    return tweet

def predict(image):
    predictions = classifier(image)
    # Sort predictions based on confidence and select the top one
    top_prediction = sorted(predictions, key=lambda x: x['score'], reverse=True)[0]
    label = top_prediction['label'].split(',')[0]  # Clean up label if necessary
    
    # Generate the tweet
    tweet = generate_tweet(label)
    return tweet

title = "Image Classifier to Generative Tweet"
description = "This demo recognizes and classifies images using the 'google/vit-base-patch16-224' model and generates a tweet about the top prediction using the GPT-Neo model for generating creative and engaging content."
input_component = gr.Image(type="pil", label="Upload an image here")
output_component = gr.Textbox(label="Generated Promotional Tweet")

gr.Interface(fn=predict, inputs=input_component, outputs=output_component, title=title, description=description).launch()