Update app.py

app.py

@@ -1,4 +1,4 @@
-# Install required packages
+# Required package setup
 import os
 import subprocess
 import sys
@@ -7,7 +7,6 @@ import pkg_resources
 
 def install_package(package, version=None):
     package_spec = f"{package}=={version}" if version else package
-    print(f"Installing {package_spec}...")
     try:
         subprocess.check_call([sys.executable, "-m", "pip", "install", "--no-cache-dir", package_spec])
     except subprocess.CalledProcessError as e:
@@ -20,28 +19,18 @@ def ensure_package(package, version=None):
             pkg_resources.require(f"{package}=={version}")
         else:
             importlib.import_module(package)
-        print(f"{package} is already installed with the correct version.")
-    except (ImportError, pkg_resources.VersionConflict, pkg_resources.DistributionNotFound) as e:
-        print(f"Package requirement failed: {e}")
+    except (ImportError, pkg_resources.VersionConflict, pkg_resources.DistributionNotFound):
         install_package(package, version)
 
-# Check if running in a standard environment (not Colab/Jupyter)
-if not os.path.exists("/.dockerenv") and not os.path.exists("/kaggle"):
-    print("Setting up environment...")
-    
-    # Install packages in the correct order with compatible versions
-    ensure_package("numpy", "1.23.5")  # Compatible with TensorFlow 2.10
-    ensure_package("protobuf", "3.20.3")  # Critical for TensorFlow compatibility
-    ensure_package("tensorflow", "2.10.0")  # Stable version with good compatibility
-    
-    # Install core dependencies
-    for pkg in ["gradio", "opencv-python-headless", "matplotlib", "pillow", "pandas"]:
-        ensure_package(pkg)
-    
-    # Install deepface last after all dependencies are set up
-    ensure_package("deepface")
-
-# Now import the required modules
+# Install packages
+ensure_package("numpy", "1.23.5")
+ensure_package("protobuf", "3.20.3")
+ensure_package("tensorflow", "2.10.0")
+for pkg in ["gradio", "opencv-python-headless", "matplotlib", "pillow", "pandas"]:
+    ensure_package(pkg)
+ensure_package("deepface")
+
+# Imports
 import gradio as gr
 import json
 import cv2
@@ -51,125 +40,71 @@ import tempfile
 import pandas as pd
 import shutil
 import matplotlib.pyplot as plt
-
-# Import DeepFace after ensuring dependencies are properly installed
 from deepface import DeepFace
 
+# --- VERIFY FACES ---
 def verify_faces(img1, img2, threshold=0.70, model="VGG-Face"):
     temp_dir = tempfile.mkdtemp()
     img1_path = os.path.join(temp_dir, "image1.jpg")
     img2_path = os.path.join(temp_dir, "image2.jpg")
 
-    if isinstance(img1, np.ndarray):
-        Image.fromarray(img1).save(img1_path)
-    else:
-        img1.save(img1_path)
-
-    if isinstance(img2, np.ndarray):
-        Image.fromarray(img2).save(img2_path)
-    else:
-        img2.save(img2_path)
+    Image.fromarray(img1).save(img1_path)
+    Image.fromarray(img2).save(img2_path)
 
     try:
-        result = DeepFace.verify(
-            img1_path=img1_path,
-            img2_path=img2_path,
-            model_name=model,
-            distance_metric="cosine",
-            threshold=threshold
-        )
-
+        result = DeepFace.verify(img1_path, img2_path, model_name=model, distance_metric="cosine", threshold=threshold)
         fig, ax = plt.subplots(1, 2, figsize=(10, 5))
-
-        img1_display = cv2.imread(img1_path)
-        img1_display = cv2.cvtColor(img1_display, cv2.COLOR_BGR2RGB)
-        img2_display = cv2.imread(img2_path)
-        img2_display = cv2.cvtColor(img2_display, cv2.COLOR_BGR2RGB)
-
-        ax[0].imshow(img1_display)
-        ax[0].set_title("Image 1")
-        ax[0].axis("off")
-
-        ax[1].imshow(img2_display)
-        ax[1].set_title("Image 2")
-        ax[1].axis("off")
-
-        verification_result = "✅ FACE MATCHED" if result["verified"] else "❌ FACE NOT MATCHED"
-        confidence = round((1 - result["distance"]) * 100, 2)
-
-        plt.suptitle(f"{verification_result}\nConfidence: {confidence}%\nDistance: {result['distance']:.4f}",
-                    fontsize=16, fontweight='bold',
-                    color='green' if result["verified"] else 'red')
-
+        ax[0].imshow(cv2.cvtColor(cv2.imread(img1_path), cv2.COLOR_BGR2RGB))
+        ax[0].set_title("Image 1"); ax[0].axis("off")
+        ax[1].imshow(cv2.cvtColor(cv2.imread(img2_path), cv2.COLOR_BGR2RGB))
+        ax[1].set_title("Image 2"); ax[1].axis("off")
+
+        verified = result["verified"]
+        conf = round((1 - result["distance"]) * 100, 2)
+        plt.suptitle(f"{'✅ MATCHED' if verified else '❌ NOT MATCHED'}\nConfidence: {conf}%",
+                     fontsize=16, fontweight='bold',
+                     color='green' if verified else 'red')
         plt.tight_layout()
 
-        os.remove(img1_path)
-        os.remove(img2_path)
-        os.rmdir(temp_dir)
-
+        shutil.rmtree(temp_dir)
         return fig, json.dumps(result, indent=2)
-
     except Exception as e:
-        if os.path.exists(img1_path):
-            os.remove(img1_path)
-        if os.path.exists(img2_path):
-            os.remove(img2_path)
-        if os.path.exists(temp_dir):
-            os.rmdir(temp_dir)
-
-        error_msg = f"Error: {str(e)}"
-        if "No face detected" in str(e):
-            error_msg = "No face detected in one or both images. Please try different images."
-
-        return None, error_msg
+        shutil.rmtree(temp_dir)
+        return None, str(e)
 
-def find_faces(query_img, db_folder, threshold=0.70, model="VGG-Face"):
+# --- FIND FACES ---
+def find_faces(query_img, db_folder_path, 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}"))
+    Image.fromarray(query_img).save(query_path)
 
     try:
+        if not os.path.isdir(db_folder_path):
+            return None, "Invalid database folder path. Please provide a valid local path."
+
         dfs = DeepFace.find(
             img_path=query_path,
-            db_path=db_path,
+            db_path=db_folder_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]
+            df = dfs[0] if dfs else pd.DataFrame()
         else:
             df = dfs
 
         if df.empty:
-            return None, "No matching faces found in the database."
+            return None, "No matching faces found."
 
         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)
+        query_display = cv2.cvtColor(cv2.imread(query_path), cv2.COLOR_BGR2RGB)
         axes[0].imshow(query_display)
-        axes[0].set_title("Query Image")
+        axes[0].set_title("Query")
         axes[0].axis("off")
 
         for i in range(num_matches):
@@ -177,235 +112,95 @@ def find_faces(query_img, db_folder, threshold=0.70, model="VGG-Face"):
             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)
-
+            match_img = cv2.cvtColor(cv2.imread(match_path), 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')
+        df["confidence"] = ((1 - df["distance"]) * 100).round(2)
+        results = df[["identity", "distance", "confidence"]].to_dict("records")
 
+        shutil.rmtree(temp_dir)
+        return fig, results
     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
+        shutil.rmtree(temp_dir)
+        return None, str(e)
 
+# --- ANALYZE FACE ---
 def analyze_face(img, actions=['age', 'gender', 'race', 'emotion']):
     temp_dir = tempfile.mkdtemp()
     img_path = os.path.join(temp_dir, "analyze.jpg")
-
-    if isinstance(img, np.ndarray):
-        Image.fromarray(img).save(img_path)
-    else:
-        img.save(img_path)
+    Image.fromarray(img).save(img_path)
 
     try:
-        results = DeepFace.analyze(
-            img_path=img_path,
-            actions=actions,
-            enforce_detection=True,
-            detector_backend='opencv'
-        )
-
-        if isinstance(results, list):
-            num_faces = len(results)
-        else:
-            num_faces = 1
-            results = [results]
+        results = DeepFace.analyze(img_path=img_path, actions=actions, enforce_detection=True)
+        results = results if isinstance(results, list) else [results]
 
         fig = plt.figure(figsize=(14, 7))
-
-        img_display = cv2.imread(img_path)
-        img_display = cv2.cvtColor(img_display, cv2.COLOR_BGR2RGB)
-
         main_ax = plt.subplot2grid((2, 4), (0, 0), colspan=2, rowspan=2)
-        main_ax.imshow(img_display)
-        main_ax.set_title(f"Analyzed Image ({num_faces} face{'s' if num_faces > 1 else ''} detected)")
-        main_ax.axis('off')
-
-        for i, face_result in enumerate(results):
-            if i >= 4:
-                break
-
-            age = face_result.get('age', 'N/A')
-            gender = face_result.get('dominant_gender', 'N/A')
-            race = face_result.get('dominant_race', 'N/A')
-            emotion = face_result.get('dominant_emotion', 'N/A')
-
-            gender_conf = 'N/A'
-            if 'gender' in face_result and isinstance(face_result['gender'], dict):
-                for g, conf in face_result['gender'].items():
-                    if g.lower() == gender.lower():
-                        gender_conf = f"{conf:.1f}%"
-                        break
-
-            race_conf = 'N/A'
-            if 'race' in face_result and isinstance(face_result['race'], dict):
-                for r, conf in face_result['race'].items():
-                    if r.lower() == race.lower():
-                        race_conf = f"{conf:.1f}%"
-                        break
-
-            emotion_conf = 'N/A'
-            if 'emotion' in face_result and isinstance(face_result['emotion'], dict):
-                for e, conf in face_result['emotion'].items():
-                    if e.lower() == emotion.lower():
-                        emotion_conf = f"{conf:.1f}%"
-                        break
-
-            ax = plt.subplot2grid((2, 4), (0 if i < 2 else 1, 2 + (i % 2)))
-
-            text = (
-                f"Face #{i+1}\n\n"
-                f"Age: {age}\n\n"
-                f"Gender: {gender} ({gender_conf})\n\n"
-                f"Race: {race} ({race_conf})\n\n"
-                f"Emotion: {emotion} ({emotion_conf})"
-            )
-
+        main_ax.imshow(cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB))
+        main_ax.set_title(f"Detected {len(results)} Face(s)")
+        main_ax.axis("off")
+
+        for i, res in enumerate(results[:4]):
+            age = res.get("age", "N/A")
+            gender = res.get("dominant_gender", "N/A")
+            race = res.get("dominant_race", "N/A")
+            emotion = res.get("dominant_emotion", "N/A")
+
+            text = f"Face #{i+1}\n\nAge: {age}\nGender: {gender}\nRace: {race}\nEmotion: {emotion}"
+            ax = plt.subplot2grid((2, 4), (0 if i < 2 else 1, 2 + i % 2))
             ax.text(0.5, 0.5, text, ha='center', va='center', fontsize=11)
-            ax.axis('off')
+            ax.axis("off")
 
         plt.tight_layout()
+        shutil.rmtree(temp_dir)
 
-        os.remove(img_path)
-        os.rmdir(temp_dir)
-
-        formatted_results = []
-        for i, res in enumerate(results[:8]):
-            face_data = {
-                "face_number": i+1,
-                "age": res.get("age", "N/A"),
-                "gender": {
-                    "dominant": res.get("dominant_gender", "N/A"),
-                    "confidence": res.get("gender", {})
-                },
-                "race": {
-                    "dominant": res.get("dominant_race", "N/A"),
-                    "confidence": res.get("race", {})
-                },
-                "emotion": {
-                    "dominant": res.get("dominant_emotion", "N/A"),
-                    "confidence": res.get("emotion", {})
-                }
-            }
-            formatted_results.append(face_data)
-
-        return fig, formatted_results
-
+        return fig, results
     except Exception as e:
-        if os.path.exists(img_path):
-            os.remove(img_path)
-        if os.path.exists(temp_dir):
-            os.rmdir(temp_dir)
+        shutil.rmtree(temp_dir)
+        return None, str(e)
 
-        error_msg = f"Error: {str(e)}"
-        if "No face detected" in str(e):
-            error_msg = "No face detected in the image. Please try a different image."
-
-        return None, error_msg
-
-with gr.Blocks(title="Complete Face Recognition Tool", theme=gr.themes.Soft()) as demo:
+# --- GRADIO INTERFACE ---
+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:
-    - **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
+    # 👤 Face Recognition Tool
+    - **Verify Faces**: Are two images of the same person?
+    - **Find Faces**: Search for matches from a folder.
+    - **Analyze Face**: Detect age, gender, race, emotion.
     """)
 
     with gr.Tabs():
         with gr.TabItem("Verify Faces"):
             with gr.Row():
-                img1_input = gr.Image(label="First Image", type="pil")
-                img2_input = gr.Image(label="Second Image", type="pil")
-
-            with gr.Row():
-                verify_threshold = gr.Slider(minimum=0.1, maximum=0.9, value=0.6, step=0.05,
-                                       label="Similarity Threshold (lower = stricter matching)")
-                verify_model = gr.Dropdown(
-                    choices=["VGG-Face", "Facenet", "OpenFace", "DeepFace", "ArcFace"],
-                    value="VGG-Face",
-                    label="Face Recognition Model"
-                )
-
-            verify_button = gr.Button("Verify Faces", variant="primary")
-
-            verify_result_plot = gr.Plot(label="Verification Result")
-            verify_json = gr.JSON(label="Technical Details")
-
-            verify_button.click(
-                verify_faces,
-                inputs=[img1_input, img2_input, verify_threshold, verify_model],
-                outputs=[verify_result_plot, verify_json]
-            )
+                img1 = gr.Image(label="Image 1", type="numpy")
+                img2 = gr.Image(label="Image 2", type="numpy")
+            threshold = gr.Slider(0.1, 0.9, value=0.7, step=0.05, label="Threshold")
+            model = gr.Dropdown(["VGG-Face", "Facenet", "OpenFace", "DeepFace", "ArcFace"], value="VGG-Face", label="Model")
+            btn = gr.Button("Verify")
+            out_plot = gr.Plot()
+            out_json = gr.JSON()
+            btn.click(verify_faces, inputs=[img1, img2, threshold, model], outputs=[out_plot, out_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
-            )
+                query_img = gr.Image(label="Query Face", type="numpy")
+                db_path = gr.Textbox(label="Folder Path for Face Database")
+            threshold_find = gr.Slider(0.1, 0.9, value=0.7, step=0.05, label="Threshold")
+            model_find = gr.Dropdown(["VGG-Face", "Facenet", "OpenFace", "DeepFace", "ArcFace"], value="VGG-Face", label="Model")
+            btn_find = gr.Button("Find Matches")
+            out_plot_find = gr.Plot()
+            out_json_find = gr.JSON()
+            btn_find.click(find_faces, inputs=[query_img, db_path, threshold_find, model_find], outputs=[out_plot_find, out_json_find])
 
         with gr.TabItem("Analyze Face"):
-            analyze_img = gr.Image(label="Upload Image for Analysis", type="pil")
-            actions_checkboxes = gr.CheckboxGroup(
-                choices=["age", "gender", "race", "emotion"],
-                value=["age", "gender", "race", "emotion"],
-                label="Select Attributes to Analyze"
-            )
-
-            analyze_button = gr.Button("Analyze Face", variant="primary")
-
-            analyze_result_plot = gr.Plot(label="Analysis Results")
-            analyze_json = gr.JSON(label="Detailed Analysis")
-
-            analyze_button.click(
-                analyze_face,
-                inputs=[analyze_img, actions_checkboxes],
-                outputs=[analyze_result_plot, analyze_json]
-            )
-
-demo.launch()
+            img = gr.Image(label="Upload Image", type="numpy")
+            actions = gr.CheckboxGroup(["age", "gender", "race", "emotion"], value=["age", "gender", "race", "emotion"], label="Attributes")
+            btn_analyze = gr.Button("Analyze")
+            out_plot_analyze = gr.Plot()
+            out_json_analyze = gr.JSON()
+            btn_analyze.click(analyze_face, inputs=[img, actions], outputs=[out_plot_analyze, out_json_analyze])
+
+demo.launch()