Update app.py

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 import cv2
 import numpy as np
 import os
+from PIL import Image
 
 # Load YOLO model
 net = cv2.dnn.readNet('yolov3.weights', 'yolov3.cfg')
@@ -51,12 +52,26 @@ def count_people_in_frame(frame):
 
     return len(indexes)
 
-def count_people_video(video_path):
+def analyze_image(image):
+    """
+    Processes an image and detects people.
     """
-    Process video and count people per frame.
+    if isinstance(image, np.ndarray):  
+        image_cv = image  # Already a NumPy array
+    else:  
+        image_cv = np.array(image)  # Convert PIL image to NumPy array
+
+    people_count = count_people_in_frame(image_cv)
+    return image, f"People in Image: {people_count}"
+
+def analyze_video(video_file):
     """
+    Processes a video and detects people in each frame.
+    """
+    video_path = video_file.name  
+
     if not os.path.exists(video_path):
-        return "Error: Video file not found."
+        return "Error: Video file could not be loaded."
 
     cap = cv2.VideoCapture(video_path)
     if not cap.isOpened():
@@ -78,47 +93,30 @@ def count_people_video(video_path):
 
     cap.release()
 
-    # Generate analytics
     return f"Max People Detected in Video: {max(people_per_frame) if people_per_frame else 0}"
 
-def analyze_video(video_file):
-    # Extract video path from uploaded file
-    video_path = video_file.name  # No need for `type="file"`
-
-    # Ensure path exists
-    if not os.path.exists(video_path):
-        return "Error: Video file could not be loaded."
-
-    result = count_people_video(video_path)
-    return result
-
-def analyze_image(image):
-    image_cv = np.array(image)  # Convert PIL image to NumPy array
-    people_count = count_people_in_frame(image_cv)
-    return image, f"People in Image: {people_count}"
-
-# Gradio Interface for Image Processing
-image_interface = gr.Interface(
-    fn=analyze_image,
-    inputs=gr.Image(label="Upload Image"),
-    outputs=[gr.Image(label="Processed Image"), gr.Textbox(label="People Counting Results")],
-    title="YOLO People Counter (Image)",
-    description="Upload an image to detect and count people using YOLOv3."
-)
-
-# Gradio Interface for Video Processing
-video_interface = gr.Interface(
-    fn=analyze_video,
-    inputs=gr.Video(label="Upload Video"),  # Remove `type="file"`
-    outputs=gr.Textbox(label="People Counting Results"),
-    title="YOLO People Counter (Video)",
-    description="Upload a video to detect and count people using YOLOv3."
-)
-
-# Combine both interfaces into tabs
-app = gr.TabbedInterface(
-    [image_interface, video_interface],
-    tab_names=["Image Mode", "Video Mode"]
+def process_input(input_file):
+    """
+    Determines if the input is an image or a video and calls the appropriate function.
+    """
+    file_path = input_file.name
+    file_extension = os.path.splitext(file_path)[1].lower()
+
+    if file_extension in [".jpg", ".jpeg", ".png", ".bmp"]:
+        image = Image.open(file_path)
+        return analyze_image(image)
+    elif file_extension in [".mp4", ".avi", ".mov", ".mkv"]:
+        return analyze_video(input_file)
+    else:
+        return "Error: Unsupported file format."
+
+# Gradio Interface for Image and Video Processing
+app = gr.Interface(
+    fn=process_input,
+    inputs=gr.File(label="Upload Image or Video"),  # Use File to handle both types
+    outputs=[gr.Textbox(label="People Counting Results")],
+    title="YOLO People Counter (Image & Video)",
+    description="Upload an image or video to detect and count people using YOLOv3."
 )
 
 # Launch app