app.py

import gradio as gr
from transformers import BlipProcessor, BlipForConditionalGeneration, RobertaTokenizer, RobertaForSequenceClassification

# 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)

# Load the emotion analysis model and tokenizer
emotion_model_name = "SamLowe/roberta-base-go_emotions"
emotion_tokenizer = RobertaTokenizer.from_pretrained(emotion_model_name)
emotion_model = RobertaForSequenceClassification.from_pretrained(emotion_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)

    # Analyze emotions of the generated caption
    # Preprocess the caption for emotion analysis
    emotion_inputs = emotion_tokenizer(decoded_caption, return_tensors="pt")
    emotion_outputs = emotion_model(**emotion_inputs)

    # Get the predicted emotion label
    emotion_label = emotion_tokenizer.decode(emotion_outputs.logits.argmax())

    # Prepare the final output with sentiment information
    final_output = f"The sentiment in the provided image shows: {emotion_label}.\n\nGenerated Caption: {decoded_caption}"
    return final_output

# Define the Gradio interface
inputs = gr.inputs.Image(label="Upload an image")
outputs = gr.outputs.Textbox(label="Generated Caption and Sentiment Analysis")

# Create the Gradio app
gr.Interface(fn=generate_caption_and_analyze_emotions, inputs=inputs, outputs=outputs).launch()