Update app.py

app.py

@@ -1,4 +1,4 @@
-# Required package setup
+# Install required packages
 import os
 import subprocess
 import sys
@@ -7,6 +7,7 @@ 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:
@@ -19,18 +20,28 @@ def ensure_package(package, version=None):
             pkg_resources.require(f"{package}=={version}")
         else:
             importlib.import_module(package)
-    except (ImportError, pkg_resources.VersionConflict, pkg_resources.DistributionNotFound):
+        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}")
         install_package(package, version)
 
-# 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
+# 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
 import gradio as gr
 import json
 import cv2
@@ -40,71 +51,125 @@ 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")
 
-    Image.fromarray(img1).save(img1_path)
-    Image.fromarray(img2).save(img2_path)
+    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)
 
     try:
-        result = DeepFace.verify(img1_path, img2_path, model_name=model, distance_metric="cosine", threshold=threshold)
+        result = DeepFace.verify(
+            img1_path=img1_path,
+            img2_path=img2_path,
+            model_name=model,
+            distance_metric="cosine",
+            threshold=threshold
+        )
+
         fig, ax = plt.subplots(1, 2, figsize=(10, 5))
-        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')
+
+        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')
+
         plt.tight_layout()
 
-        shutil.rmtree(temp_dir)
+        os.remove(img1_path)
+        os.remove(img2_path)
+        os.rmdir(temp_dir)
+
         return fig, json.dumps(result, indent=2)
+
     except Exception as e:
-        shutil.rmtree(temp_dir)
-        return None, str(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
 
-# --- FIND FACES ---
-def find_faces(query_img, db_folder_path, threshold=0.70, model="VGG-Face"):
+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")
-    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."
+    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_folder_path,
+            db_path=db_path,
             model_name=model,
             distance_metric="cosine",
             threshold=threshold
         )
 
         if isinstance(dfs, list):
-            df = dfs[0] if dfs else pd.DataFrame()
+            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."
+            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.cvtColor(cv2.imread(query_path), cv2.COLOR_BGR2RGB)
+        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")
+        axes[0].set_title("Query Image")
         axes[0].axis("off")
 
         for i in range(num_matches):
@@ -112,95 +177,235 @@ def find_faces(query_img, db_folder_path, threshold=0.70, model="VGG-Face"):
             distance = df.iloc[i]["distance"]
             confidence = round((1 - distance) * 100, 2)
 
-            match_img = cv2.cvtColor(cv2.imread(match_path), cv2.COLOR_BGR2RGB)
+            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()
 
-        df["confidence"] = ((1 - df["distance"]) * 100).round(2)
-        results = df[["identity", "distance", "confidence"]].to_dict("records")
+        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')
 
-        shutil.rmtree(temp_dir)
-        return fig, results
     except Exception as e:
-        shutil.rmtree(temp_dir)
-        return None, str(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
 
-# --- ANALYZE FACE ---
 def analyze_face(img, actions=['age', 'gender', 'race', 'emotion']):
     temp_dir = tempfile.mkdtemp()
     img_path = os.path.join(temp_dir, "analyze.jpg")
-    Image.fromarray(img).save(img_path)
+
+    if isinstance(img, np.ndarray):
+        Image.fromarray(img).save(img_path)
+    else:
+        img.save(img_path)
 
     try:
-        results = DeepFace.analyze(img_path=img_path, actions=actions, enforce_detection=True)
-        results = results if isinstance(results, list) else [results]
+        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]
 
         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(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))
+        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})"
+            )
+
             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)
 
-        return fig, results
+        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
+
     except Exception as e:
-        shutil.rmtree(temp_dir)
-        return None, str(e)
+        if os.path.exists(img_path):
+            os.remove(img_path)
+        if os.path.exists(temp_dir):
+            os.rmdir(temp_dir)
 
-# --- GRADIO INTERFACE ---
-with gr.Blocks(title="Face Recognition Tool", theme=gr.themes.Soft()) as demo:
+        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:
     gr.Markdown("""
-    # 👤 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.
+    # 🔍 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
     """)
 
     with gr.Tabs():
         with gr.TabItem("Verify Faces"):
             with gr.Row():
-                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])
+                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]
+            )
 
         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():
-                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])
+                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"):
-            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()
+            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()