Update app.py

app.py

@@ -1,90 +1,90 @@
-import gradio as gr
-import cv2
-import os
-import boto3
-
-s3_client = boto3.client(
-    's3',
-    aws_access_key_id='AKIAY5HVHYWVXRTEU6CB',
-    aws_secret_access_key='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',
-    region_name='eu-central-1' 
-)
-
-def upload_to_s3(bucket_name, folder_name):
-    # Upload files in the folder to S3 bucket
-    for filename in os.listdir(folder_name):
-        if filename.endswith('.png'):
-            file_path = os.path.join(folder_name, filename)
-            s3_client.upload_file(file_path, bucket_name, f"{folder_name}/{filename}")
-
-def process_video(uploaded_video, name, surname, interval_ms):
-    try:
-        if uploaded_video is None:
-            return "No video file uploaded."
-
-        folder_name = f"{name}_{surname}"
-        os.makedirs(folder_name, exist_ok=True)
-
-        # The uploaded_video is a NamedString object, extract the file path
-        temp_video_path = uploaded_video.name
-
-        # Initialize face detector
-        face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
-
-        # Open and process the video
-        vidcap = cv2.VideoCapture(temp_video_path)
-        if not vidcap.isOpened():
-            raise Exception("Failed to open video file.")
-
-        fps = vidcap.get(cv2.CAP_PROP_FPS)
-        frame_interval = int(fps * (interval_ms / 10000))
-
-        frame_count = 0
-        saved_image_count = 0
-        success, image = vidcap.read()
-        while success and saved_image_count < 86:
-            if frame_count % frame_interval == 0:
-                # Apply face detection
-                gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-                faces = face_cascade.detectMultiScale(gray, 1.1, 4)
-                for (x, y, w, h) in faces:
-                    # Crop and resize face
-                    face = image[y:y+h, x:x+w]
-                    face_resized = cv2.resize(face, (160, 160))
-                    cv2.imwrite(os.path.join(folder_name, f"{name}_{surname}_{saved_image_count:04d}.png"), face_resized)
-                    saved_image_count += 1
-                    if saved_image_count >= 86:
-                        break
-
-            success, image = vidcap.read()
-            frame_count += 1
-
-        vidcap.release()
-
-        bucket_name = 'imagefilessml'  # Replace with your bucket name
-
-        upload_to_s3(bucket_name, folder_name)
-
-        return f"Saved and uploaded {saved_image_count} face images"
-
-
-        return f"Saved {saved_image_count} face images in the folder: {folder_name}"
-
-    except Exception as e:
-        return f"An error occurred: {e}"
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        video = gr.File(label="Upload Your Video")
-        name = gr.Textbox(label="Name")
-        surname = gr.Textbox(label="Surname")
-        interval = gr.Number(label="Interval in milliseconds", value=1000)
-        submit_button = gr.Button("Submit")
-
-    submit_button.click(
-        fn=process_video,
-        inputs=[video, name, surname, interval],
-        outputs=[gr.Text(label="Result")]
-    )
-
-demo.launch()
+import gradio as gr
+import cv2
+import os
+import boto3
+
+s3_client = boto3.client(
+    's3',
+    aws_access_key_id='AWS_ACCESS_KEY_ID',
+    aws_secret_access_key='CKxcJhYPNQHBmnVKrcK6wjxD3QV0gdj7HvVw7JWl',
+    region_name='eu-central-1' 
+)
+
+def upload_to_s3(bucket_name, folder_name):
+    # Upload files in the folder to S3 bucket
+    for filename in os.listdir(folder_name):
+        if filename.endswith('.png'):
+            file_path = os.path.join(folder_name, filename)
+            s3_client.upload_file(file_path, bucket_name, f"{folder_name}/{filename}")
+
+def process_video(uploaded_video, name, surname, interval_ms):
+    try:
+        if uploaded_video is None:
+            return "No video file uploaded."
+
+        folder_name = f"{name}_{surname}"
+        os.makedirs(folder_name, exist_ok=True)
+
+        # The uploaded_video is a NamedString object, extract the file path
+        temp_video_path = uploaded_video.name
+
+        # Initialize face detector
+        face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+        # Open and process the video
+        vidcap = cv2.VideoCapture(temp_video_path)
+        if not vidcap.isOpened():
+            raise Exception("Failed to open video file.")
+
+        fps = vidcap.get(cv2.CAP_PROP_FPS)
+        frame_interval = int(fps * (interval_ms / 10000))
+
+        frame_count = 0
+        saved_image_count = 0
+        success, image = vidcap.read()
+        while success and saved_image_count < 86:
+            if frame_count % frame_interval == 0:
+                # Apply face detection
+                gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+                faces = face_cascade.detectMultiScale(gray, 1.1, 4)
+                for (x, y, w, h) in faces:
+                    # Crop and resize face
+                    face = image[y:y+h, x:x+w]
+                    face_resized = cv2.resize(face, (160, 160))
+                    cv2.imwrite(os.path.join(folder_name, f"{name}_{surname}_{saved_image_count:04d}.png"), face_resized)
+                    saved_image_count += 1
+                    if saved_image_count >= 86:
+                        break
+
+            success, image = vidcap.read()
+            frame_count += 1
+
+        vidcap.release()
+
+        bucket_name = 'imagefilessml'  # Replace with your bucket name
+
+        upload_to_s3(bucket_name, folder_name)
+
+        return f"Saved and uploaded {saved_image_count} face images"
+
+
+        return f"Saved {saved_image_count} face images in the folder: {folder_name}"
+
+    except Exception as e:
+        return f"An error occurred: {e}"
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        video = gr.File(label="Upload Your Video")
+        name = gr.Textbox(label="Name")
+        surname = gr.Textbox(label="Surname")
+        interval = gr.Number(label="Interval in milliseconds", value=1000)
+        submit_button = gr.Button("Submit")
+
+    submit_button.click(
+        fn=process_video,
+        inputs=[video, name, surname, interval],
+        outputs=[gr.Text(label="Result")]
+    )
+
+demo.launch()