Update app.py

app.py

@@ -12,7 +12,6 @@ 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
 
@@ -94,9 +93,6 @@ 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)
@@ -122,7 +118,9 @@ 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)
@@ -133,13 +131,13 @@ def classify_zip_and_analyze_color(zip_file):
                         for f in detected_faces:
                             faces_data.append({
                                 "age": f["age"],
-                                "gender": "Homme" if f["gender"]=="Man" else "Femme",
+                                "gender": f["gender"],  # dict of probabilities
                                 "emotion": f["dominant_emotion"]
                             })
                     else:
                         faces_data.append({
                             "age": detected_faces["age"],
-                            "gender": "Homme" if detected_faces["gender"]=="Man" else "Femme",
+                            "gender": detected_faces["gender"],  # dict
                             "emotion": detected_faces["dominant_emotion"]
                         })
                 except Exception:
@@ -155,13 +153,10 @@ def classify_zip_and_analyze_color(zip_file):
                 ))
 
     # 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"])
 
-    # Save XLSX with zip name + date
-    out_xlsx = os.path.join(tempfile.gettempdir(), f"{zip_name}_{date_str}_results.xlsx")
+    # Save XLSX
+    out_xlsx = os.path.join(tempfile.gettempdir(), "results.xlsx")
     df.to_excel(out_xlsx, index=False)
 
     # ---------------------------
@@ -196,24 +191,51 @@ def classify_zip_and_analyze_color(zip_file):
     plot2_img = Image.open(buf2)
 
     # ---------------------------
-    # Plot 3: Gender distribution
+    # Extract age and weighted gender (confidence ≤ 0.9)
     # ---------------------------
-    gender_counts = {"Homme":0, "Femme":0}
+    ages = []
+    gender_confidence = {"Man": 0, "Woman": 0}
+
     for face_list in df["Face Info"]:
-        for face in face_list:
-            gender_counts[face["gender"]] += 1
+        for face in face_list:  # each face is a dict
+            ages.append(face["age"])
+            gender_dict = face["gender"]  # dict of probabilities
+            gender = max(gender_dict, key=gender_dict.get)
+            conf = float(gender_dict[gender]) / 100  # convert % to 0-1
+            weight = min(conf, 0.9)
+            if gender in gender_confidence:
+                gender_confidence[gender] += weight
+            else:
+                gender_confidence[gender] = weight
 
+    # ---------------------------
+    # Plot 3: Gender distribution (weighted ≤ 0.9)
+    # ---------------------------
     fig3, ax3 = plt.subplots()
-    ax3.bar(gender_counts.keys(), gender_counts.values(), color=["lightblue","pink"])
-    ax3.set_title("Gender Distribution")
-    ax3.set_ylabel("Count")
+    ax3.bar(gender_confidence.keys(), gender_confidence.values(), color=["lightblue", "pink"])
+    ax3.set_title("Gender Distribution (Weighted ≤90% Confidence)")
+    ax3.set_ylabel("Sum of Confidence")
     buf3 = io.BytesIO()
     plt.savefig(buf3, format="png")
     plt.close(fig3)
     buf3.seek(0)
     plot3_img = Image.open(buf3)
 
-    return df, out_xlsx, plot1_img, plot2_img, plot3_img
+    # ---------------------------
+    # Plot 4: Age distribution
+    # ---------------------------
+    fig4, ax4 = plt.subplots()
+    ax4.hist(ages, bins=range(0, 101, 5), color="lightgreen", edgecolor="black")
+    ax4.set_title("Age Distribution")
+    ax4.set_xlabel("Age")
+    ax4.set_ylabel("Count")
+    buf4 = io.BytesIO()
+    plt.savefig(buf4, format="png")
+    plt.close(fig4)
+    buf4.seek(0)
+    plot4_img = Image.open(buf4)
+
+    return df, out_xlsx, plot1_img, plot2_img, plot3_img, plot4_img
 
 # ---------------------------
 # Gradio Interface
@@ -222,19 +244,16 @@ 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"],
-            datatype=["str","str","str","str","str","str"]
-        ),
+        gr.Dataframe(headers=["Filename", "Top 3 Predictions", "Confidence", "Dominant Color", "Basic Color", "Face Info"]),
         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"),
+        gr.Image(type="pil", label="Gender Distribution (Weighted ≤90%)"),
+        gr.Image(type="pil", label="Age Distribution"),
     ],
     title="Image Classifier with Color & Face Analysis",
-    description="Upload a ZIP of images. Classifies images, analyzes dominant color, detects/characterizes faces (age, gender, emotion).",
+    description="Upload a ZIP of images. Classifies images, analyzes dominant color, and detects/characterizes faces (age, gender, emotion).",
 )
 
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+    demo.launch(server_name="0.0.0.0", server_port=7860)