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import gradio as gr
from transformers import BlipProcessor, BlipForConditionalGeneration, AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline
# Load the image captioning model and tokenizer
caption_model_name = "Salesforce/blip-image-captioning-large"
caption_processor = BlipProcessor.from_pretrained(caption_model_name)
caption_model = BlipForConditionalGeneration.from_pretrained(caption_model_name)
def generate_caption_and_analyze_emotions(image):
# Preprocess the image for caption generation
caption_inputs = caption_processor(images=image, return_tensors="pt")
# Generate caption using the caption model
caption = caption_model.generate(**caption_inputs)
# Decode the output caption
decoded_caption = caption_processor.decode(caption[0], skip_special_tokens=True)
# Load the emotion analysis model and tokenizer
emotion_model_name = "bhadresh-savani/distilbert-base-uncased-emotion"
emotion_classifier = pipeline(model=emotion_model_name)
results = emotion_classifier(decoded_caption)
sentiment_label = results[0]['label']
if sentiment_label == 'neutral':
sentiment_text = "Sentiment of the image is"
else:
sentiment_text = "Sentiment of the image shows"
final_output = f"This image shows {decoded_caption} and {sentiment_text} {sentiment_label}."
return final_output
# Define the Gradio interface
inputs = gr.inputs.Image(label="Upload an image")
outputs = gr.outputs.Textbox(label="Sentiment Analysis")
# Create the Gradio app
app = gr.Interface(fn=generate_caption_and_analyze_emotions, inputs=inputs, outputs=outputs)
# Launch the Gradio app
if __name__ == "__main__":
app.launch()
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