Update app.py

app.py

@@ -1,36 +1,32 @@
-import gradio as gr
 from transformers import DetrImageProcessor, DetrForObjectDetection, pipeline
 from PIL import Image
+import gradio as gr
 
-# Load pre-trained image recognition model
+# Load pre-trained models
 detection_model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
 detection_processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
-
-# Load text generation model
 description_generator = pipeline("text-generation", model="gpt-2")
 
-# Function for recognizing product
+# Function to recognize and describe product
 def recognize_and_describe(image):
-    # Recognize product
     inputs = detection_processor(images=image, return_tensors="pt")
     outputs = detection_model(**inputs)
     logits = outputs.logits.argmax(-1).item()
-    product_label = f"Product Class: {logits}"  # Replace with class-to-label mapping if needed
+    product_label = f"Product Class: {logits}"
 
     # Generate description
     prompt = f"Describe the product: {product_label}"
     description = description_generator(prompt, max_length=50, num_return_sequences=1)
     return product_label, description[0]["generated_text"]
 
-# Create Gradio Interface
+# Gradio Interface
 interface = gr.Interface(
     fn=recognize_and_describe,
     inputs="image",
     outputs=["text", "text"],
     title="SETA: Product Description App",
-    description="Upload a product image to get its description."
+    description="Upload an image of a product to get its description."
 )
 
-# Launch the app
 if __name__ == "__main__":
     interface.launch()