commit

app.py

@@ -1,9 +1,8 @@
 import gradio as gr
-from transformers import BlipProcessor, BlipForConditionalGeneration, RobertaTokenizer, RobertaForSequenceClassification
+from transformers import BlipProcessor, BlipForConditionalGeneration, AutoTokenizer, AutoModelForSequenceClassification
 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)
@@ -39,7 +38,6 @@ def generate_caption_and_analyze_emotions(image):
     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