Update app.py

app.py

@@ -3,15 +3,16 @@ from deepface import DeepFace
 import easyocr
 import matplotlib.pyplot as plt
 from PIL import Image
+import os
 
 def convert_to_jpg(image_path):
     try:
         img = Image.open(image_path)
-        jpg_path = image_path.rsplit('.', 1)[0] + '.jpg'
+        jpg_path = os.path.splitext(image_path)[0] + '.jpg'
         img.convert('RGB').save(jpg_path, 'JPEG')
         return jpg_path
     except Exception as e:
-        return f"Error converting image: {str(e)}"
+        raise ValueError(f"Error converting image: {str(e)}")
 
 def perform_handwriting_recognition_easyocr(image_path):
     try:
@@ -29,16 +30,16 @@ def analyze_face(image_path):
     try:
         jpg_path = convert_to_jpg(image_path)
         handwriting_result = perform_handwriting_recognition_easyocr(jpg_path)
-        analysis = DeepFace.analyze(img_path=jpg_path, actions=["age", "gender", "emotion", "race"])
-        gender_scores = analysis["gender"] if not isinstance(analysis, list) else analysis[0]["gender"]
-        predicted_gender = "Male" if gender_scores["Man"] > gender_scores["Woman"] else "Female"
+        analysis = DeepFace.analyze(img_path=jpg_path, actions=["age", "gender", "emotion", "race"], enforce_detection=False)
+        gender_scores = analysis.get("gender", {"Man": 0, "Woman": 0})
+        predicted_gender = "Male" if gender_scores.get("Man", 0) > gender_scores.get("Woman", 0) else "Female"
         
         outputs = {
-            "age": analysis["age"] if not isinstance(analysis, list) else analysis[0]["age"],
+            "age": analysis.get("age", "Unknown"),
             "gender_scores": gender_scores,
             "predicted_gender": predicted_gender,
-            "emotion": analysis["dominant_emotion"] if not isinstance(analysis, list) else analysis[0]["dominant_emotion"],
-            "race": analysis["dominant_race"] if not isinstance(analysis, list) else analysis[0]["dominant_race"],
+            "emotion": analysis.get("dominant_emotion", "Unknown"),
+            "race": analysis.get("dominant_race", "Unknown"),
             "handwriting": handwriting_result,
         }
         return outputs
@@ -56,7 +57,7 @@ def verify_faces(image_path1, image_path2):
     try:
         jpg_path1 = convert_to_jpg(image_path1)
         jpg_path2 = convert_to_jpg(image_path2)
-        result = DeepFace.verify(img1_path=jpg_path1, img2_path=jpg_path2)
+        result = DeepFace.verify(img1_path=jpg_path1, img2_path=jpg_path2, enforce_detection=False)
         confidence = result["distance"]
         verified = result["verified"]
         return f"Match Confidence: {confidence:.2f}", f"Are they the same person? {'Yes' if verified else 'No'}"
@@ -65,7 +66,7 @@ def verify_faces(image_path1, image_path2):
 
 def visualize_gender(gender_scores):
     labels = ["Male", "Female"]
-    scores = [gender_scores["Man"], gender_scores["Woman"]]
+    scores = [gender_scores.get("Man", 0), gender_scores.get("Woman", 0)]
     fig, ax = plt.subplots(figsize=(4, 3))
     ax.bar(labels, scores, color=["blue", "pink"])
     ax.set_ylabel("Confidence (%)")