Create app.py

app.py

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+import gradio as gr
+from transformers import BlipProcessor, BlipForConditionalGeneration, 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)
+
+# Load the emotion analysis model
+emotion_model_name = "SamLowe/roberta-base-go_emotions"
+emotion_classifier = pipeline("text-classification", model=emotion_model_name)
+
+def generate_caption_and_analyze_emotions(image):
+    try:
+        # Preprocess the image for caption generation
+        caption_inputs = caption_processor(images=image, return_tensors="pt")
+
+        # Generate caption using the caption model
+        caption_ids = caption_model.generate(**caption_inputs)
+
+        # Decode the output caption
+        decoded_caption = caption_processor.decode(caption_ids[0], skip_special_tokens=True)
+
+        # Perform emotion analysis on the generated caption
+        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
+    except Exception as e:
+        return f"An error occurred: {e}"
+
+# Define the Gradio interface using the new API
+inputs = gr.Image(label="Upload an image")
+outputs = gr.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()