Update app.py

app.py

@@ -18,16 +18,12 @@ import gradio as gr
 from deepface import DeepFace
 import cv2
 
-# ---------------------------
-# Device setup
-# ---------------------------
+# Device
 if not torch.cuda.is_available():
     os.environ["CUDA_VISIBLE_DEVICES"] = ""
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-# ---------------------------
-# Load ResNet50
-# ---------------------------
+# Model
 weights = ResNet50_Weights.DEFAULT
 model = resnet50(weights=weights).to(device)
 model.eval()
@@ -68,9 +64,7 @@ def get_dominant_color(image,num_colors=5):
     hex_color = f"#{dominant_color[0]:02x}{dominant_color[1]:02x}{dominant_color[2]:02x}"
     return dominant_color, hex_color
 
-# ---------------------------
 # Core analysis
-# ---------------------------
 def classify_zip_and_analyze_color(zip_file):
     results = []
     images_dict = {}  # store images for preview
@@ -125,13 +119,87 @@ def classify_zip_and_analyze_color(zip_file):
                 ))
 
     df = pd.DataFrame(results, columns=["Filename","Top 3 Predictions","Confidence","Dominant Color","Basic Color","Face Info"])
-
     out_xlsx = os.path.join(tempfile.gettempdir(), f"{zip_name}_{date_str}_results.xlsx")
     df.to_excel(out_xlsx,index=False)
 
-    return df, images_dict, out_xlsx
-
-# Callback for preview
+    # ---------------------------
+    # Plot 1: Basic color frequency
+    # ---------------------------
+    fig1, ax1 = plt.subplots()
+    color_counts = df["Basic Color"].value_counts()
+    ax1.bar(color_counts.index, color_counts.values, color="skyblue")
+    ax1.set_title("Basic Color Frequency")
+    ax1.set_ylabel("Count")
+    buf1 = io.BytesIO()
+    plt.savefig(buf1, format="png")
+    plt.close(fig1)
+    buf1.seek(0)
+    plot1_img = Image.open(buf1)
+
+    # ---------------------------
+    # Plot 2: Top prediction distribution
+    # ---------------------------
+    fig2, ax2 = plt.subplots()
+    preds_flat = []
+    for p in df["Top 3 Predictions"]:
+        preds_flat.extend(p.split(", "))
+    pred_counts = pd.Series(preds_flat).value_counts().head(20)
+    ax2.barh(pred_counts.index[::-1], pred_counts.values[::-1], color="salmon")
+    ax2.set_title("Top Prediction Distribution")
+    ax2.set_xlabel("Count")
+    buf2 = io.BytesIO()
+    plt.savefig(buf2, format="png", bbox_inches="tight")
+    plt.close(fig2)
+    buf2.seek(0)
+    plot2_img = Image.open(buf2)
+
+    # ---------------------------
+    # Gender & Age
+    # ---------------------------
+    ages_male, ages_female = [], []
+    gender_confidence = {"Homme":0, "Femme":0}
+    for face_list in df["Face Info"]:
+        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
+            weight = min(conf,0.9)
+            gender_trans = "Homme" if gender=="Man" else "Femme"
+            gender_confidence[gender_trans] += weight
+            if gender_trans=="Homme":
+                ages_male.append(age)
+            else:
+                ages_female.append(age)
+
+    # Gender distribution
+    fig3, ax3 = plt.subplots()
+    ax3.bar(gender_confidence.keys(), gender_confidence.values(), color=["lightblue","pink"])
+    ax3.set_title("Gender Distribution (Weighted ≤90%)")
+    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)
+
+    # Age distribution
+    fig4, ax4 = plt.subplots()
+    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)
+    buf4.seek(0)
+    plot4_img = Image.open(buf4)
+
+    return df, list(images_dict.keys()), images_dict, out_xlsx, plot1_img, plot2_img, plot3_img, plot4_img
+
+# Show selected image preview
 def show_preview(filename, images_dict):
     return images_dict.get(filename, None)
 
@@ -141,17 +209,28 @@ def show_preview(filename, images_dict):
 with gr.Blocks() as demo:
     uploaded_zip = gr.File(label="Upload ZIP of images", file_types=[".zip"])
     output_df = gr.Dataframe(headers=["Filename","Top 3 Predictions","Confidence","Dominant Color","Basic Color","Face Info"])
+    image_dropdown = gr.Dropdown(label="Select image to preview")
     image_preview = gr.Image(label="Image Preview")
     download_file = gr.File(label="Download XLSX")
-    images_state = gr.State()  # store images dict
+    images_state = gr.State()
+
+    plot1 = gr.Image(label="Basic Color Frequency")
+    plot2 = gr.Image(label="Top Prediction Distribution")
+    plot3 = gr.Image(label="Gender Distribution")
+    plot4 = gr.Image(label="Age Distribution by Gender")
 
     analyze_btn = gr.Button("Run Analysis")
 
     def run_analysis(zip_file):
-        df, images_dict, out_xlsx = classify_zip_and_analyze_color(zip_file)
-        return df, images_dict, out_xlsx
+        df, filenames, images_dict, out_xlsx, p1, p2, p3, p4 = classify_zip_and_analyze_color(zip_file)
+        return df, filenames, images_dict, out_xlsx, p1, p2, p3, p4
+
+    analyze_btn.click(
+        run_analysis,
+        inputs=uploaded_zip,
+        outputs=[output_df, image_dropdown, images_state, download_file, plot1, plot2, plot3, plot4]
+    )
 
-    analyze_btn.click(run_analysis, inputs=uploaded_zip, outputs=[output_df, images_state, download_file])
-    output_df.select(show_preview, inputs=[output_df, images_state], outputs=image_preview)
+    image_dropdown.change(show_preview, inputs=[image_dropdown, images_state], outputs=image_preview)
 
 demo.launch(server_name="0.0.0.0", server_port=7860)