Update app.py

app.py

@@ -1,57 +1,57 @@
-import torch
-import torchvision
-from torchvision import transforms
-import gradio as gr
-import os
-import cv2
-from PIL import Image
-from model import create_model
-model,transform=create_model(num_of classes=3)
-model.eval()
-def classify_video(video):
-    cap = cv2.VideoCapture(video)
-    predictions = []
-    Fire=[]
-    Smoke=[]
-    Default=[]
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-        img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-        img_pil = Image.fromarray(img)
-        img_tensor = transform(img_pil).unsqueeze(0)
-        with torch.no_grad():
-            output = model(img_tensor)
-            pred = output.argmax().item()
-            predictions.append(pred)
-    cap.release()
-    class_names=['DEFAULT', 'FIRE', 'SMOKE']
-    for i in predictions:
-        if i == 1:
-            Fire.append(i)
-        elif i == 2:
-            Smoke.append(i)
-        else:
-            Default.append(i)
-    if Fire!=[] and Smoke!=[]:
-        return f"Spotted {class_names[1]} and {class_names[2]}"
-    elif Fire!=[]:
-        return f"Spotted {class_names[1]}"
-    elif Smoke!=[]:
-        return f"Spotted {class_names[2]}"
-    else:
-        return f"Spotted {class_names[0]}"
-Description="An MobileNET model trained to classify Fire and Smoke through Videos"
-Article="Created at jupyter NoteBook with GPU NVIDIA_GeForce_MX350"
-example_list=[["Examples/"+ example] for example in os.listdir("Examples")if example.endswith((".mp4", ".avi", ".mov"))]
-gr.Interface(
-    fn=classify_video,
-    inputs=gr.Video(streaming=True),
-    outputs="text",
-    title="Fire and Smoke Classifier",
-    examples=example_list,
-    description=description,
-    article=article,
-    live="True"
-).launch()
+import torch
+import torchvision
+from torchvision import transforms
+import gradio as gr
+import os
+import cv2
+from PIL import Image
+from model import create_model
+model,transform=create_model(num_of_classes=3)
+model.eval()
+def classify_video(video):
+    cap = cv2.VideoCapture(video)
+    predictions = []
+    Fire=[]
+    Smoke=[]
+    Default=[]
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        img_pil = Image.fromarray(img)
+        img_tensor = transform(img_pil).unsqueeze(0)
+        with torch.no_grad():
+            output = model(img_tensor)
+            pred = output.argmax().item()
+            predictions.append(pred)
+    cap.release()
+    class_names=['DEFAULT', 'FIRE', 'SMOKE']
+    for i in predictions:
+        if i == 1:
+            Fire.append(i)
+        elif i == 2:
+            Smoke.append(i)
+        else:
+            Default.append(i)
+    if Fire!=[] and Smoke!=[]:
+        return f"Spotted {class_names[1]} and {class_names[2]}"
+    elif Fire!=[]:
+        return f"Spotted {class_names[1]}"
+    elif Smoke!=[]:
+        return f"Spotted {class_names[2]}"
+    else:
+        return f"Spotted {class_names[0]}"
+Description="An MobileNET model trained to classify Fire and Smoke through Videos"
+Article="Created at jupyter NoteBook with GPU NVIDIA_GeForce_MX350"
+example_list=[["Examples/"+ example] for example in os.listdir("Examples")if example.endswith((".mp4", ".avi", ".mov"))]
+gr.Interface(
+    fn=classify_video,
+    inputs=gr.Video(streaming=True),
+    outputs="text",
+    title="Fire and Smoke Classifier",
+    examples=example_list,
+    description=description,
+    article=article,
+    live="True"
+).launch()