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import gradio as gr
from transformers import pipeline
from PIL import Image
# Initialize the pipeline with the image captioning model
caption_pipeline = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
# Initialize the pipeline for emotion classification
emotion_pipeline = pipeline("image-classification", model="RickyIG/emotion_face_image_classification_v3")
def generate_caption_and_emotion(image):
# Process the image for captioning
caption_result = caption_pipeline(image)
caption = caption_result[0]["generated_text"]
# Process the image for emotion classification
emotion_result = emotion_pipeline(image)
emotions = ", ".join([f"{res['label']}: {res['score']:.2f}" for res in emotion_result])
# Combine results
combined_result = f"Caption: {caption}\nEmotions: {emotions}"
return combined_result
# Setup the Gradio interface
interface = gr.Interface(fn=generate_caption_and_emotion,
inputs=gr.components.Image(type="pil", label="Upload an Image"),
outputs=gr.components.Textbox(label="Generated Caption and Emotions"))
interface.launch()
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