Uploaded app.py

app.py

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+import os
+import gradio as gr
+from transformers import pipeline
+import cv2
+
+# Load Hugging Face Gender Classifier model (ProjectPersonal/GenderClassifier)
+gender_classifier = pipeline("image-classification", model="ProjectPersonal/GenderClassifier")
+
+# Load Hugging Face face swap model (DeepFaceLab by senhan007)
+face_swap_model = pipeline("image-to-image", model="senhan007/DeepFaceLab")
+
+def detect_gender(image_path):
+    # Use the gender classifier to detect gender from the image
+    result = gender_classifier(image_path)
+    # Return the predicted gender (male or female)
+    return result[0]['label'].lower()  # This will return "male" or "female"
+
+def swap_faces(image, video, selected_gender):
+    # Save uploaded files
+    image_path = "uploaded_image.jpg"
+    video_path = "uploaded_video.mp4"
+    output_path = "swapped_video.mp4"
+    image.save(image_path)
+    video.save(video_path)
+
+    # Detect gender of the reference image
+    detected_gender = detect_gender(image_path)
+
+    # If the detected gender matches the selected gender or if "all" is selected, proceed with the face swap
+    if selected_gender == "all" or detected_gender == selected_gender:
+        video_cap = cv2.VideoCapture(video_path)
+        frame_rate = video_cap.get(cv2.CAP_PROP_FPS)
+        width = int(video_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(video_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        output_video = cv2.VideoWriter(output_path, fourcc, frame_rate, (width, height))
+
+        while True:
+            ret, frame = video_cap.read()
+            if not ret:
+                break
+
+            # Apply the face swap model (you need to insert the actual face swap model logic here)
+            swapped_frame = frame  # Placeholder, replace with actual model inference
+
+            output_video.write(swapped_frame)
+
+        video_cap.release()
+        output_video.release()
+    else:
+        # Return the original video without face swap if gender doesn't match
+        output_path = video_path  # No face swap applied
+
+    return output_path
+
+# Gradio UI
+iface = gr.Interface(
+    fn=swap_faces,
+    inputs=[
+        gr.Image(type="pil", label="Upload Reference Image"),
+        gr.Video(type="file", label="Upload Video"),
+        gr.Dropdown(choices=["all", "male", "female"], label="Select Gender")
+    ],
+    outputs=gr.Video(label="Face Swapped Video"),
+    title="Video Face Swap",
+    description="Upload a reference image and a video to swap faces based on gender selection."
+)
+
+iface.launch()
+