Update app.py

app.py

@@ -143,36 +143,41 @@ if st.button("Predict Emotions") and selected_images:
 
 # Generate DataFrame button
 if st.button("Generate DataFrame") and selected_images:
-    # Create a list to store data for DataFrame
-    df_data = []
-
-    # Iterate through selected images to gather data
-    for image, file_name, result in zip(selected_images, selected_file_names, results):
-        # Extract image metadata
-        size_kb = image.size[0] * image.size[1] / 1024.0  # Calculating size in KB
-        timestamp = datetime.datetime.now()  # Current timestamp
-        color_type = "Color" if len(image.getbands()) > 1 else "Grayscale"
-        
-        # Extract predicted emotions and scores
-        emotion_scores = {res["label"].split("_")[-1].capitalize(): res["score"] for res in result}
-
-        # Append image metadata and emotion scores to the list
-        df_data.append({
-            "Neutral": f"{emotion_scores.get('Neutral', 0.0):.4f}",
-            "Happy": f"{emotion_scores.get('Happy', 0.0):.4f}",
-            "Sad": f"{emotion_scores.get('Sad', 0.0):.4f}",
-            "Angry": f"{emotion_scores.get('Angry', 0.0):.4f}",
-            "Disgust": f"{emotion_scores.get('Disgust', 0.0):.4f}",
-            "Surprise": f"{emotion_scores.get('Surprise', 0.0):.4f}",
-            "Fear": f"{emotion_scores.get('Fear', 0.0):.4f}",
-            "File Name": file_name,
-            "Size (KB)": size_kb,
-            "Timestamp": timestamp,
-            "Color Type": color_type
-        })
-
-    # Create DataFrame
-    df = pd.DataFrame(df_data)
-
-    # Display DataFrame
-    st.write(df)
+    # Ensure results are populated before generating the DataFrame
+    if len(results) == 0:
+        st.error("Please click 'Predict Emotions' to generate results first.")
+    else:
+        # Create a list to store data for DataFrame
+        df_data = []
+
+        # Iterate through selected images to gather data
+        for image, file_name, result in zip(selected_images, selected_file_names, results):
+            # Extract image metadata
+            size_kb = image.size[0] * image.size[1] / 1024.0  # Calculating size in KB
+            timestamp = datetime.datetime.now()  # Current timestamp
+            color_type = "Color" if len(image.getbands()) > 1 else "Grayscale"
+
+            # Extract predicted emotions and scores
+            emotion_scores = {res["label"].split("_")[-1].capitalize(): res["score"] for res in result}
+
+            # Append image metadata and emotion scores to the list
+            df_data.append({
+                "Neutral": f"{emotion_scores.get('Neutral', 0.0):.4f}",
+                "Happy": f"{emotion_scores.get('Happy', 0.0):.4f}",
+                "Sad": f"{emotion_scores.get('Sad', 0.0):.4f}",
+                "Angry": f"{emotion_scores.get('Angry', 0.0):.4f}",
+                "Disgust": f"{emotion_scores.get('Disgust', 0.0):.4f}",
+                "Surprise": f"{emotion_scores.get('Surprise', 0.0):.4f}",
+                "Fear": f"{emotion_scores.get('Fear', 0.0):.4f}",
+                "File Name": file_name,
+                "Size (KB)": size_kb,
+                "Timestamp": timestamp,
+                "Color Type": color_type
+            })
+
+        # Create DataFrame
+        df = pd.DataFrame(df_data)
+
+        # Display DataFrame
+        st.write(df)
+