Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+import os
+import cv2
+import pandas as pd
+from deepface import DeepFace
+
+# --- Setup: Create known_faces directory ---
+known_faces_dir = "known_faces"
+if not os.path.exists(known_faces_dir):
+    os.makedirs(known_faces_dir)
+# DeepFace creates a .pkl file for representations. We can ignore it.
+if os.path.exists(os.path.join(known_faces_dir, "representations_vgg_face.pkl")):
+    os.remove(os.path.join(known_faces_dir, "representations_vgg_face.pkl"))
+
+
+# --- The Recognition Function using DeepFace ---
+def recognize_face_deepface(uploaded_image_pil):
+    # Check if there are any known faces to compare against
+    if not os.listdir(known_faces_dir):
+        return "No known faces found. Please upload images to the 'known_faces' directory in your Space."
+    
+    # DeepFace works with file paths, so we save the uploaded image temporarily
+    temp_image_path = "temp_uploaded_image.jpg"
+    uploaded_image_pil.save(temp_image_path)
+
+    try:
+        # Use DeepFace.find() to search for the face in our database directory
+        # It will return a list of pandas DataFrames. If the list is empty, no match was found.
+        # model_name='VGG-Face' is the default and works well.
+        # distance_metric='cosine' is also a standard choice.
+        dfs = DeepFace.find(
+            img_path=temp_image_path,
+            db_path=known_faces_dir,
+            model_name='VGG-Face',
+            enforce_detection=True # Set to False if you want it to work even if the face alignment is poor
+        )
+
+        # The result 'dfs' is a list of DataFrames. We check the first one.
+        if not dfs or dfs[0].empty:
+            return "Unknown (No similar face found in the database)"
+
+        # If we have a result, extract the path of the most similar face
+        most_similar_face_path = dfs[0].iloc[0]['identity']
+        
+        # Get the name from the filename
+        # e.g., 'known_faces/susan.jpg' -> 'susan'
+        filename = os.path.basename(most_similar_face_path)
+        name = os.path.splitext(filename)[0]
+        
+        return f"Recognized: {name}"
+
+    except ValueError as e:
+        # This error is often thrown by DeepFace if no face is detected in the uploaded image
+        return "No face detected in the uploaded image. Please try another one."
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+    finally:
+        # Clean up the temporary image file
+        if os.path.exists(temp_image_path):
+            os.remove(temp_image_path)
+
+
+# --- Create the Gradio Interface ---
+demo = gr.Interface(
+    fn=recognize_face_deepface,
+    inputs=gr.Image(type="pil", label="Upload an image to recognize"),
+    outputs=gr.Textbox(label="Recognition Result", api_name="recognize")
+)
+
+# Launch the app
+demo.launch()