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import gradio as gr
from transformers import BlipProcessor, BlipForConditionalGeneration, RobertaTokenizer, RobertaForSequenceClassification
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# 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 = AutoTokenizer.from_pretrained(emotion_model_name)
emotion_model = AutoModelForSequenceClassification.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.encode_plus(
decoded_caption,
max_length=128,
padding="max_length",
truncation=True,
return_tensors="pt"
)
emotion_outputs = emotion_model(**emotion_inputs)
# Get the predicted emotion label
emotion_label_id = emotion_outputs.logits.argmax().item()
emotion_label = emotion_tokenizer.decode(emotion_label_id)
# 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
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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