Update app.py

app.py

@@ -73,7 +73,7 @@ def get_dominant_color(image,num_colors=5):
 # ---------------------------
 def classify_zip_and_analyze_color(zip_file):
     results = []
-    images_dict = {}
+    images_list = []  # list of (image, label) for gallery
     zip_name = os.path.splitext(os.path.basename(zip_file.name))[0]
     date_str = datetime.now().strftime("%Y%m%d")
 
@@ -86,7 +86,7 @@ def classify_zip_and_analyze_color(zip_file):
                 img_path = os.path.join(tmpdir,fname)
                 try:
                     image = Image.open(img_path).convert("RGB")
-                    images_dict[fname] = image.copy()
+                    images_list.append((image.copy(), fname))
                 except:
                     continue
 
@@ -128,7 +128,6 @@ def classify_zip_and_analyze_color(zip_file):
     # ---------------------------
     # Plots
     # ---------------------------
-    # Basic color
     fig1, ax1 = plt.subplots()
     color_counts = df["Basic Color"].value_counts()
     ax1.bar(color_counts.index, color_counts.values, color="skyblue")
@@ -136,7 +135,6 @@ def classify_zip_and_analyze_color(zip_file):
     ax1.set_ylabel("Count")
     buf1 = io.BytesIO(); plt.savefig(buf1, format="png"); plt.close(fig1); buf1.seek(0); plot1_img = Image.open(buf1)
 
-    # Top predictions
     fig2, ax2 = plt.subplots()
     preds_flat = []
     for p in df["Top 3 Predictions"]: preds_flat.extend(p.split(", "))
@@ -146,7 +144,6 @@ def classify_zip_and_analyze_color(zip_file):
     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 and age
     ages_male, ages_female = [], []
     gender_confidence = {"Homme":0, "Femme":0}
     for face_list in df["Face Info"]:
@@ -174,16 +171,7 @@ def classify_zip_and_analyze_color(zip_file):
     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, images_dict, out_xlsx, plot1_img, plot2_img, plot3_img, plot4_img
-
-# ---------------------------
-# Preview callback
-# ---------------------------
-def show_preview(selected_row, images_state):
-    if images_state is None or selected_row is None:
-        return None
-    filename = selected_row[0]  # first column is filename
-    return images_state.get(filename, None)
+    return df, images_list, out_xlsx, plot1_img, plot2_img, plot3_img, plot4_img
 
 # ---------------------------
 # Gradio interface
@@ -192,10 +180,9 @@ with gr.Blocks() as demo:
     uploaded_zip = gr.File(label="Upload ZIP of images", file_types=[".zip"])
     analyze_btn = gr.Button("Run Analysis")
 
-    output_df = gr.Dataframe(headers=["Filename","Top 3 Predictions","Confidence","Dominant Color","Basic Color","Face Info"], interactive=True)
-    image_preview = gr.Image(label="Image Preview")
+    output_df = gr.Dataframe(headers=["Filename","Top 3 Predictions","Confidence","Dominant Color","Basic Color","Face Info"])
+    image_gallery = gr.Gallery(label="Preview Images").style(grid=[4], height="auto")
     download_file = gr.File(label="Download XLSX")
-    images_state = gr.State()
 
     plot1 = gr.Image(label="Basic Color Frequency")
     plot2 = gr.Image(label="Top Prediction Distribution")
@@ -203,15 +190,13 @@ with gr.Blocks() as demo:
     plot4 = gr.Image(label="Age Distribution by Gender")
 
     def run_analysis(zip_file):
-        df, images_dict, out_xlsx, p1, p2, p3, p4 = classify_zip_and_analyze_color(zip_file)
-        return df, images_dict, out_xlsx, p1, p2, p3, p4
+        df, images_list, out_xlsx, p1, p2, p3, p4 = classify_zip_and_analyze_color(zip_file)
+        return df, images_list, out_xlsx, p1, p2, p3, p4
 
     analyze_btn.click(
         run_analysis,
         inputs=uploaded_zip,
-        outputs=[output_df, images_state, download_file, plot1, plot2, plot3, plot4]
+        outputs=[output_df, image_gallery, download_file, plot1, plot2, plot3, plot4]
     )
 
-    output_df.select(show_preview, inputs=[output_df, images_state], outputs=image_preview)
-
 demo.launch(server_name="0.0.0.0", server_port=7860)