Update app.py

app.py

@@ -20,60 +20,60 @@ def save_image():
     st.title("Hand Sign Detection")
     col1, col2 = st.columns(2)  # 2 for two col
     
-    with col1:
-    genre = st.radio(
-        "Upload Your Hand Sign",
-        ('Browse', 'Camera'))
-
-    if genre == 'Camera':
-        uploaded_image = st.camera_input("Take a picture")
-        
-    else:
-        uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
-
-    if uploaded_image is not None:
-        
-        # Convert the image to a format compatible with PIL and OpenCV
-        pil_image = Image.open(uploaded_image)
-        opencv_image = np.array(pil_image)
-        opencv_image = cv2.cvtColor(opencv_image, cv2.COLOR_BGR2RGB)
-
-        # Provide a file path to save the image
-        upload_image_path = "processed_image.jpg"
-        cv2.imwrite(upload_image_path, opencv_image)
-
-        # calling 
-        model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt')  # local model
-        results = model(upload_image_path)
-        st.success(f"Image saved as {upload_image_path} ")
-
-        command = [
-            "python",
-            "yolov5/detect.py",
-            "--weights", "best.pt",
-            "--img", "416",
-            "--conf", "0.56",
-            "--source", upload_image_path
-        ]
-
-        process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
-        std_out, std_err = process.communicate()
-        if process.returncode != 0:
-            error_message = f"Error: {std_err}"
-            st.text(error_message)
-
-        
-    with col2:
-        detect_image_pred = "yolov5/runs/detect/exp/processed_image.jpg"
-        if os.path.exists(detect_image_pred):
-            st.text("Operation in progress. Please wait....")
-            st.image(detect_image_pred, caption="Detected Image", use_column_width=True)
-
-            st.text("Detection Output:")
-            st.text(results.pandas().xyxy[0])
+        with col1:
+        genre = st.radio(
+            "Upload Your Hand Sign",
+            ('Browse', 'Camera'))
+    
+        if genre == 'Camera':
+            uploaded_image = st.camera_input("Take a picture")
+            
         else:
-            st.text("Detection Threshold is 60")
-       
+            uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+    
+        if uploaded_image is not None:
+            
+            # Convert the image to a format compatible with PIL and OpenCV
+            pil_image = Image.open(uploaded_image)
+            opencv_image = np.array(pil_image)
+            opencv_image = cv2.cvtColor(opencv_image, cv2.COLOR_BGR2RGB)
+    
+            # Provide a file path to save the image
+            upload_image_path = "processed_image.jpg"
+            cv2.imwrite(upload_image_path, opencv_image)
+    
+            # calling 
+            model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt')  # local model
+            results = model(upload_image_path)
+            st.success(f"Image saved as {upload_image_path} ")
+    
+            command = [
+                "python",
+                "yolov5/detect.py",
+                "--weights", "best.pt",
+                "--img", "416",
+                "--conf", "0.56",
+                "--source", upload_image_path
+            ]
+    
+            process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+            std_out, std_err = process.communicate()
+            if process.returncode != 0:
+                error_message = f"Error: {std_err}"
+                st.text(error_message)
+    
+            
+        with col2:
+            detect_image_pred = "yolov5/runs/detect/exp/processed_image.jpg"
+            if os.path.exists(detect_image_pred):
+                st.text("Operation in progress. Please wait....")
+                st.image(detect_image_pred, caption="Detected Image", use_column_width=True)
+    
+                st.text("Detection Output:")
+                st.text(results.pandas().xyxy[0])
+            else:
+                st.text("Detection Threshold is 60")
+