Update app.py

app.py

@@ -12,6 +12,7 @@ from torchvision.models import resnet50, ResNet50_Weights
 from sklearn.cluster import MiniBatchKMeans
 import matplotlib.pyplot as plt
 import io
+from datetime import datetime
 
 import gradio as gr
 
@@ -93,6 +94,9 @@ def get_dominant_color(image, num_colors=5):
 def classify_zip_and_analyze_color(zip_file):
     results = []
 
+    zip_name = os.path.splitext(os.path.basename(zip_file.name))[0]
+    date_str = datetime.now().strftime("%Y%m%d")
+
     with tempfile.TemporaryDirectory() as tmpdir:
         with zipfile.ZipFile(zip_file.name, 'r') as zip_ref:
             zip_ref.extractall(tmpdir)
@@ -118,9 +122,7 @@ def classify_zip_and_analyze_color(zip_file):
                 rgb, hex_color = get_dominant_color(image)
                 basic_color = closest_basic_color(rgb)
 
-                # ---------------------------
                 # Face detection & characterization
-                # ---------------------------
                 faces_data = []
                 try:
                     img_cv2 = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
@@ -131,32 +133,40 @@ def classify_zip_and_analyze_color(zip_file):
                         for f in detected_faces:
                             faces_data.append({
                                 "age": f["age"],
-                                "gender": f["gender"],  # dict of probabilities
+                                "gender": f["gender"],
                                 "emotion": f["dominant_emotion"]
                             })
                     else:
                         faces_data.append({
                             "age": detected_faces["age"],
-                            "gender": detected_faces["gender"],  # dict
+                            "gender": detected_faces["gender"],
                             "emotion": detected_faces["dominant_emotion"]
                         })
                 except Exception:
                     faces_data = []
 
+                # Thumbnail preview
+                thumbnail = image.copy()
+                thumbnail.thumbnail((64, 64))
+
                 results.append((
                     fname,
                     ", ".join([p[0] for p in preds]),
                     ", ".join([p[1] for p in preds]),
                     hex_color,
                     basic_color,
-                    faces_data
+                    faces_data,
+                    thumbnail
                 ))
 
     # Build dataframe
-    df = pd.DataFrame(results, columns=["Filename", "Top 3 Predictions", "Confidence", "Dominant Color", "Basic Color", "Face Info"])
+    df = pd.DataFrame(results, columns=[
+        "Filename", "Top 3 Predictions", "Confidence",
+        "Dominant Color", "Basic Color", "Face Info", "Thumbnail"
+    ])
 
-    # Save XLSX
-    out_xlsx = os.path.join(tempfile.gettempdir(), "results.xlsx")
+    # Save XLSX with zip name + date
+    out_xlsx = os.path.join(tempfile.gettempdir(), f"{zip_name}_{date_str}_results.xlsx")
     df.to_excel(out_xlsx, index=False)
 
     # ---------------------------
@@ -191,29 +201,31 @@ def classify_zip_and_analyze_color(zip_file):
     plot2_img = Image.open(buf2)
 
     # ---------------------------
-    # Extract age and weighted gender (confidence ≤ 0.9)
+    # Extract ages and genders
     # ---------------------------
-    ages = []
-    gender_confidence = {"Man": 0, "Woman": 0}
+    ages_male, ages_female = [], []
+    gender_confidence = {"Homme": 0, "Femme": 0}
 
     for face_list in df["Face Info"]:
-        for face in face_list:  # each face is a dict
-            ages.append(face["age"])
-            gender_dict = face["gender"]  # dict of probabilities
+        for face in face_list:
+            age = face["age"]
+            gender_dict = face["gender"]
             gender = max(gender_dict, key=gender_dict.get)
-            conf = float(gender_dict[gender]) / 100  # convert % to 0-1
+            conf = float(gender_dict[gender]) / 100
             weight = min(conf, 0.9)
-            if gender in gender_confidence:
-                gender_confidence[gender] += weight
+            gender_trans = "Homme" if gender == "Man" else "Femme"
+            gender_confidence[gender_trans] += weight
+            if gender_trans == "Homme":
+                ages_male.append(age)
             else:
-                gender_confidence[gender] = weight
+                ages_female.append(age)
 
     # ---------------------------
-    # Plot 3: Gender distribution (weighted ≤ 0.9)
+    # Plot 3: Gender distribution
     # ---------------------------
     fig3, ax3 = plt.subplots()
     ax3.bar(gender_confidence.keys(), gender_confidence.values(), color=["lightblue", "pink"])
-    ax3.set_title("Gender Distribution (Weighted ≤90% Confidence)")
+    ax3.set_title("Gender Distribution (Weighted ≤90%)")
     ax3.set_ylabel("Sum of Confidence")
     buf3 = io.BytesIO()
     plt.savefig(buf3, format="png")
@@ -222,13 +234,15 @@ def classify_zip_and_analyze_color(zip_file):
     plot3_img = Image.open(buf3)
 
     # ---------------------------
-    # Plot 4: Age distribution
+    # Plot 4: Age distribution by gender
     # ---------------------------
     fig4, ax4 = plt.subplots()
-    ax4.hist(ages, bins=range(0, 101, 5), color="lightgreen", edgecolor="black")
-    ax4.set_title("Age Distribution")
+    bins = range(0, 101, 5)
+    ax4.hist([ages_male, ages_female], bins=bins, color=["lightblue", "pink"], label=["Homme", "Femme"], edgecolor="black")
+    ax4.set_title("Age Distribution by Gender")
     ax4.set_xlabel("Age")
     ax4.set_ylabel("Count")
+    ax4.legend()
     buf4 = io.BytesIO()
     plt.savefig(buf4, format="png")
     plt.close(fig4)
@@ -244,15 +258,19 @@ demo = gr.Interface(
     fn=classify_zip_and_analyze_color,
     inputs=gr.File(file_types=[".zip"], label="Upload ZIP of images"),
     outputs=[
-        gr.Dataframe(headers=["Filename", "Top 3 Predictions", "Confidence", "Dominant Color", "Basic Color", "Face Info"]),
+        gr.Dataframe(
+            headers=["Filename", "Top 3 Predictions", "Confidence",
+                     "Dominant Color", "Basic Color", "Face Info", "Thumbnail"],
+            datatype=["str","str","str","str","str","str","pil"]
+        ),
         gr.File(label="Download XLSX"),
         gr.Image(type="pil", label="Basic Color Frequency"),
         gr.Image(type="pil", label="Top Prediction Distribution"),
         gr.Image(type="pil", label="Gender Distribution (Weighted ≤90%)"),
-        gr.Image(type="pil", label="Age Distribution"),
+        gr.Image(type="pil", label="Age Distribution by Gender"),
     ],
     title="Image Classifier with Color & Face Analysis",
-    description="Upload a ZIP of images. Classifies images, analyzes dominant color, and detects/characterizes faces (age, gender, emotion).",
+    description="Upload a ZIP of images. Classifies images, analyzes dominant color, detects/characterizes faces (age, gender, emotion), and shows thumbnails.",
 )
 
 if __name__ == "__main__":