Update app.py

app.py

@@ -1,114 +1,70 @@
 # Install required packages
-import os
-import subprocess
-import sys
-import importlib
-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:
-        print(f"Failed to install {package_spec}: {e}")
-        raise
-
-def ensure_package(package, version=None):
-    try:
-        if version:
-            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}")
-        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
 import gradio as gr
 import json
 import cv2
 import numpy as np
+from deepface import DeepFace
+import matplotlib.pyplot as plt
 from PIL import Image
 import tempfile
+import os
 import pandas as pd
 import shutil
-import matplotlib.pyplot as plt
-
-# Import DeepFace after ensuring dependencies are properly installed
-from deepface import DeepFace
 
 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)
-
+    
     try:
         result = DeepFace.verify(
-            img1_path=img1_path,
+            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))
-
+        
         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',
+        
+        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()
-
+        
         os.remove(img1_path)
         os.remove(img2_path)
         os.rmdir(temp_dir)
-
+        
         return fig, json.dumps(result, indent=2)
-
+    
     except Exception as e:
         if os.path.exists(img1_path):
             os.remove(img1_path)
@@ -116,107 +72,118 @@ def verify_faces(img1, img2, threshold=0.70, model="VGG-Face"):
             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
 
 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}"))
-
+            orig_filename = file.orig_name  # Get original filename with extension
+            file_ext = os.path.splitext(orig_filename)[1]
+            new_filename = f"image_{i}{file_ext}"
+            shutil.copy(file.name, os.path.join(db_path, new_filename))
+    
     try:
         dfs = DeepFace.find(
             img_path=query_path,
             db_path=db_path,
             model_name=model,
             distance_metric="cosine",
-            threshold=threshold
+            threshold=threshold,
+            silent=True  # Disable unnecessary logging
         )
-
+        
         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"]
+            if not os.path.exists(match_path):
+                continue  # Skip invalid paths
+                
             distance = df.iloc[i]["distance"]
             confidence = round((1 - distance) * 100, 2)
-
+            
             match_img = cv2.imread(match_path)
+            if match_img is None:
+                continue  # Skip unreadable images
+                
             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."
-
+        elif "No such file or directory" in str(e):
+            error_msg = "Invalid database path or corrupted image files"
+        
         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")
-
+    
     if isinstance(img, np.ndarray):
         Image.fromarray(img).save(img_path)
     else:
         img.save(img_path)
-
+    
     try:
         results = DeepFace.analyze(
             img_path=img_path,
@@ -224,55 +191,55 @@ def analyze_face(img, actions=['age', 'gender', 'race', 'emotion']):
             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(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"
@@ -280,15 +247,15 @@ def analyze_face(img, actions=['age', 'gender', 'race', 'emotion']):
                 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')
-
+        
         plt.tight_layout()
-
+        
         os.remove(img_path)
         os.rmdir(temp_dir)
-
+        
         formatted_results = []
         for i, res in enumerate(results[:8]):
             face_data = {
@@ -308,87 +275,93 @@ def analyze_face(img, actions=['age', 'gender', 'race', 'emotion']):
                 }
             }
             formatted_results.append(face_data)
-
+        
         return fig, formatted_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)
-
+        
         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
 
+# Create Gradio interface
 with gr.Blocks(title="Complete 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
     """)
-
+    
     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,
+                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",
+                    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],
+                verify_faces, 
+                inputs=[img1_input, img2_input, verify_threshold, verify_model], 
                 outputs=[verify_result_plot, verify_json]
             )
-
+            
+            gr.Markdown("""... (keep existing markdown) ...""")
+        
         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,
+                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",
+                    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
             )
-
+            
+            gr.Markdown("""... (keep existing markdown) ...""")
+        
         with gr.TabItem("Analyze Face"):
             analyze_img = gr.Image(label="Upload Image for Analysis", type="pil")
             actions_checkboxes = gr.CheckboxGroup(
@@ -396,16 +369,19 @@ with gr.Blocks(title="Complete Face Recognition Tool", theme=gr.themes.Soft()) a
                 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]
             )
+            
+            gr.Markdown("""... (keep existing markdown) ...""")
 
+# Launch the app
 demo.launch()