Update app.py

app.py

@@ -16,15 +16,14 @@ if 'models_loaded' not in st.session_state:
     st.session_state.models_loaded = True
 
 # Define image processing and classification functions
-def imageprocessing(url):
-    response = requests.get(url)
-    image = Image.open(BytesIO(response.content))
+def imageprocessing(image):
     encoding = st.session_state.image_processor(image.convert("RGB"), return_tensors="pt")
-    return encoding, image
+    return encoding
 
 def topwear(encoding):
     outputs = st.session_state.top_wear_model(**encoding)
     predicted_class_idx = outputs.logits.argmax(-1).item()
+    st.write(st.session_state.top_wear_model.config.id2label[predicted_class_idx])
     return st.session_state.top_wear_model.config.id2label[predicted_class_idx]
 
 def patterns(encoding):
@@ -43,8 +42,8 @@ def sleevelengths(encoding):
     return st.session_state.sleeve_length_model.config.id2label[predicted_class_idx]
 
 # Run all models in parallel
-def pipes(image_url):
-    encoding, image = imageprocessing(image_url)
+def pipes(image):
+    encoding = imageprocessing(image)
 
     results = [None] * 4
 
@@ -70,20 +69,26 @@ def pipes(image_url):
         "sleeve_length": results[3]
     }
 
-    return result_dict, image
+    return result_dict
 
 # Streamlit app UI
 st.title("Clothing Classification Pipeline")
 
-image_url = st.text_input("Enter Image URL")
-if image_url:
-    start_time = time.time()
-    
-    try:
-        result, img = pipes(image_url)
-        st.image(img.resize((200, 200)), caption="Uploaded Image", use_column_width=False)
-        st.write("Classification Results (JSON):")
-        st.json(result)  # Display results in JSON format
-        st.write(f"Time taken: {time.time() - start_time:.2f} seconds")
-    except Exception as e:
-        st.error(f"Error processing the image: {str(e)}")
+url = st.text_input("Paste image URL here...")
+if url:
+    response = requests.get(url)
+    if response.status_code == 200:
+        image = Image.open(BytesIO(response.content))
+        st.image(image.resize((200, 200)), caption="Uploaded Image", use_column_width=False)
+        
+        start_time = time.time()
+
+        try:
+            result = pipes(image)
+            st.write("Classification Results (JSON):")
+            st.json(result)  # Display results in JSON format
+            st.write(f"Time taken: {time.time() - start_time:.2f} seconds")
+        except Exception as e:
+            st.error(f"Error processing the image: {str(e)}")
+    else:
+        st.error("Failed to load image from URL. Please check the URL.")