Update app.py

app.py

@@ -143,41 +143,36 @@ if st.button("Predict Emotions") and selected_images:
 
 # Generate DataFrame button
 if st.button("Generate DataFrame") and selected_images:
-    # 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)
-
+    # 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)