Update app.py

app.py

@@ -123,91 +123,6 @@ def verify_faces(img1, img2, threshold=0.70, model="VGG-Face"):
 
         return None, error_msg
 
-def find_faces(query_img, db_folder, threshold=0.70, model="VGG-Face"):
-    temp_dir = tempfile.mkdtemp()
-    query_path = os.path.join(temp_dir, "query.jpg")
-
-    if isinstance(query_img, np.ndarray):
-        Image.fromarray(query_img).save(query_path)
-    else:
-        query_img.save(query_path)
-
-    if isinstance(db_folder, str):
-        db_path = db_folder
-    else:
-        db_path = os.path.join(temp_dir, "db")
-        os.makedirs(db_path, exist_ok=True)
-
-        for i, file in enumerate(db_folder):
-            file_ext = os.path.splitext(file.name)[1]
-            shutil.copy(file.name, os.path.join(db_path, f"image_{i}{file_ext}"))
-
-    try:
-        dfs = DeepFace.find(
-            img_path=query_path,
-            db_path=db_path,
-            model_name=model,
-            distance_metric="cosine",
-            threshold=threshold
-        )
-
-        if isinstance(dfs, list):
-            if len(dfs) == 0:
-                return None, "No matching faces found in the database."
-            df = dfs[0]
-        else:
-            df = dfs
-
-        if df.empty:
-            return None, "No matching faces found in the database."
-
-        df = df.sort_values(by=["distance"])
-
-        num_matches = min(4, len(df))
-        fig, axes = plt.subplots(1, num_matches + 1, figsize=(15, 5))
-
-        query_display = cv2.imread(query_path)
-        query_display = cv2.cvtColor(query_display, cv2.COLOR_BGR2RGB)
-        axes[0].imshow(query_display)
-        axes[0].set_title("Query Image")
-        axes[0].axis("off")
-
-        for i in range(num_matches):
-            match_path = df.iloc[i]["identity"]
-            distance = df.iloc[i]["distance"]
-            confidence = round((1 - distance) * 100, 2)
-
-            match_img = cv2.imread(match_path)
-            match_img = cv2.cvtColor(match_img, cv2.COLOR_BGR2RGB)
-
-            axes[i+1].imshow(match_img)
-            axes[i+1].set_title(f"Match #{i+1}\nConfidence: {confidence}%")
-            axes[i+1].axis("off")
-
-        plt.suptitle(f"Found {len(df)} matching faces", fontsize=16, fontweight='bold')
-        plt.tight_layout()
-
-        results = df[["identity", "distance"]].copy()
-        results["confidence"] = (1 - results["distance"]) * 100
-        results["confidence"] = results["confidence"].round(2)
-        results = results.rename(columns={"identity": "Image Path"})
-
-        os.remove(query_path)
-        if not isinstance(db_folder, str):
-            shutil.rmtree(db_path)
-
-        return fig, results.to_dict('records')
-
-    except Exception as e:
-        if os.path.exists(query_path):
-            os.remove(query_path)
-
-        error_msg = f"Error: {str(e)}"
-        if "No face detected" in str(e):
-            error_msg = "No face detected in the query image. Please try a different image."
-
-        return None, error_msg
-
 def analyze_face(img, actions=['age', 'gender', 'race', 'emotion']):
     temp_dir = tempfile.mkdtemp()
     img_path = os.path.join(temp_dir, "analyze.jpg")
@@ -323,12 +238,11 @@ def analyze_face(img, actions=['age', 'gender', 'race', 'emotion']):
 
         return None, error_msg
 
-with gr.Blocks(title="Complete Face Recognition Tool", theme=gr.themes.Soft()) as demo:
+with gr.Blocks(title="Face Recognition Tool", theme=gr.themes.Soft()) as demo:
     gr.Markdown("""
-    # 🔍 Complete Face Recognition Tool
-    This tool provides three face recognition features:
+    # 🔍 Face Recognition Tool
+    This tool provides two main features:
     - **Verify Faces**: Compare two specific images to check if they contain the same person
-    - **Find Faces**: Search for matching faces in a database/folder
     - **Analyze Face**: Determine age, gender, race, and emotion from a facial image
     """)
 
@@ -358,37 +272,6 @@ with gr.Blocks(title="Complete Face Recognition Tool", theme=gr.themes.Soft()) a
                 outputs=[verify_result_plot, verify_json]
             )
 
-        with gr.TabItem("Find Faces"):
-            query_img = gr.Image(label="Query Image (Face to find)", type="pil")
-            db_path_input = gr.Textbox(label="Database Path (folder containing images to search in)")
-            db_files_input = gr.File(label="Or upload images for database", file_count="multiple")
-
-            with gr.Row():
-                find_threshold = gr.Slider(minimum=0.1, maximum=0.9, value=0.6, step=0.05,
-                                     label="Similarity Threshold (lower = stricter matching)")
-                find_model = gr.Dropdown(
-                    choices=["VGG-Face", "Facenet", "OpenFace", "DeepFace", "ArcFace"],
-                    value="VGG-Face",
-                    label="Face Recognition Model"
-                )
-
-            find_button = gr.Button("Find Matching Faces", variant="primary")
-
-            find_result_plot = gr.Plot(label="Search Results")
-            find_results_table = gr.JSON(label="Detailed Results")
-
-            find_button.click(
-                find_faces,
-                inputs=[query_img, db_path_input, find_threshold, find_model],
-                outputs=[find_result_plot, find_results_table]
-            )
-
-            db_files_input.change(
-                lambda x: "",
-                inputs=db_files_input,
-                outputs=db_path_input
-            )
-
         with gr.TabItem("Analyze Face"):
             analyze_img = gr.Image(label="Upload Image for Analysis", type="pil")
             actions_checkboxes = gr.CheckboxGroup(