## Add PorterStemmer support to TextProcessor

ISSR_AI4MH_Yixing_Fan/pipeline/fetch_reddit.py

@@ -31,7 +31,7 @@
     "anxiety attack", "feeling empty", "burnout", "mental exhaustion", "can't sleep",
     "constant worry", "feeling worthless", "no motivation", "social isolation", "panic disorder",
     "intrusive thoughts", "emotional pain", "mental fog", "dissociation", "feeling trapped",
-    "racing thoughts", "mood swings", "emotional breakdown", "mental health crisis", "therapy needed"
+    "racing thoughts", "mood swings", "emotional breakdown", "mental health crisis", "therapy needed" , "feel like a burden" ,"nobody cares about me"
 ]
 
 subreddits = [
@@ -41,7 +41,7 @@
     "mentalhealthawareness", "bipolar", "traumatoolbox", "mentalhealthmemes", "mentalhealthart",
     "mentalhealthvideos", "mentalhealthresources", "mentalhealthadvice", "mentalhealthstories",
     "mentalhealthchat", "mentalhealthhelp", "mentalhealthcommunity",
-    "mentalhealthmatters", "mentalhealthwarriors"
+    "mentalhealthmatters", "mentalhealthwarriors","healthanxiety" ,"KindVoice"
 ]
 
 # ========== Preprocessing function ==========

ISSR_Communication_Analysis_Tool_Samuel_Kalu/app.py

@@ -8,6 +8,7 @@
 
 import base64
 import io
+import traceback
 import warnings
 
 import gradio as gr
@@ -21,12 +22,13 @@
 
 
 def get_image_base64(path):
+    if not os.path.exists(path):
+        raise FileNotFoundError(f"Image asset not found at '{path}'")
     with open(path, "rb") as image_file:
         encoded = base64.b64encode(image_file.read()).decode("utf-8")
     return f'<img src="data:image/png;base64,{encoded}" width="300" style="display: block; margin: 0 auto;"/>'
 
 
-
 def process_multiple_videos(
     folder_path=None,
     video_files=None,
@@ -61,7 +63,6 @@ def process_multiple_videos(
 
     if folder_path and os.path.isdir(folder_path):
         logger.info(f"Processing videos from folder: {folder_path}")
-        # Pass checkbox states to process_all_videos_from_path
         process_all_videos_from_path(
             folder_path,
             output_dir,
@@ -78,6 +79,16 @@ def process_multiple_videos(
             f"Finished processing videos from folder. Found {len(processed_csvs)} CSVs."
         )
 
+    elif folder_path and not os.path.isdir(folder_path):
+        logger.warning(f"Provided folder path does not exist: '{folder_path}'")
+        return (
+            f"Error: Folder path '{folder_path}' does not exist or is not a directory. Please check the path and try again.",
+            gr.update(visible=False, value=None),
+            gr.update(visible=False, value=None),
+            gr.update(visible=False, value=None),
+            gr.update(open=False),
+        )
+
     elif video_files:
         logger.info(f"Processing uploaded video files: {video_files}")
         for idx, video_path in enumerate(video_files):
@@ -95,7 +106,6 @@ def process_multiple_videos(
                 )
 
             original_filename = os.path.basename(video_path)
-            # Ensure proper CSV filename generation from video filename
             output_csv_filename = os.path.splitext(original_filename)[0] + ".csv"
             output_csv_path = os.path.join(output_dir, output_csv_filename)
 
@@ -104,8 +114,6 @@ def process_multiple_videos(
             )
 
             try:
-                # Call the process_video function from src.pipeline
-                # Pass checkbox states to process_video
                 process_video(
                     video_path,
                     output_csv_path,
@@ -118,8 +126,6 @@ def process_multiple_videos(
                     f"Successfully processed uploaded video: {original_filename}"
                 )
             except Exception as e:
-                import traceback
-
                 logger.error(
                     f"Exception while processing {original_filename}: {str(e)}"
                 )
@@ -147,10 +153,8 @@ def process_multiple_videos(
 
     if processed_csvs:
         try:
-            # Display the first processed CSV in the DataFrame
             df = pd.read_csv(processed_csvs[0])
             logger.info(f"Displaying data from: {os.path.basename(processed_csvs[0])}")
-            # Generate plots and convert to base64 HTML
             plot_img = plot_speaker_charts(df)
             return (
                 f"Successfully processed {len(processed_csvs)} video(s). Displaying data from {os.path.basename(processed_csvs[0])}.",
@@ -160,8 +164,6 @@ def process_multiple_videos(
                 gr.update(open=True),
             )
         except Exception as e:
-            import traceback
-
             logger.error(
                 f"Error reading processed CSV {os.path.basename(processed_csvs[0])}: {str(e)}"
             )
@@ -214,12 +216,18 @@ def create_interface():
         }
     """
 
+    logo_path = "assets/trip_lab_logo.png"
+    if not os.path.exists(logo_path):
+        logger.warning(f"Logo asset not found at '{logo_path}'. Displaying without logo.")
+        html_img = ""
+    else:
+        html_img = get_image_base64(logo_path)
+
     with gr.Blocks(
         title="Driving Simulator Communication Analysis Tool",
         theme=gr.themes.Monochrome(),
-        css=custom_css,  # Added custom CSS
+        css=custom_css,
     ) as demo:
-        html_img = get_image_base64("assets/trip_lab_logo.png")
         gr.HTML(
             f"""
         <div style="text-align: center;">
@@ -239,12 +247,12 @@ def create_interface():
                 video_files = gr.File(
                     label="Upload Videos (Optional)",
                     file_count="multiple",
-                    type="filepath",  # Ensures a temporary file path is provided
+                    type="filepath",
                     file_types=[".mp4", ".mov", ".avi", ".webm"],
                 )
 
                 with gr.Group():
-                    with gr.Row():  # Make checkboxes inline
+                    with gr.Row():
                         checkbox_ner = gr.Checkbox(
                             label="NER (Named Entity Recognition)",
                             value=True,
@@ -279,7 +287,7 @@ def create_interface():
                 download_csv = gr.File(
                     label="Download Processed CSV",
                     visible=False,
-                    type="filepath",  # This is for download, not upload
+                    type="filepath",
                     file_count="single",
                 )
                 with gr.Accordion("Show Plots", open=True) as plot_accordion:
@@ -301,4 +309,4 @@ def create_interface():
 
 
 demo = create_interface()
-demo.launch()
+demo.launch()

ISSR_Communication_Analysis_Tool_Samuel_Kalu/src/sentiment_analysis.py

@@ -12,10 +12,20 @@
 warnings.filterwarnings("ignore")
 
 analyzer = SentimentIntensityAnalyzer()
+hf_analyzer = None
 
 
-def analyze_sentiment(text):
+def analyze_sentiment(text, use_hf=False):
     """Perform sentiment analysis using VADER and return a composite score from -1 to +1."""
+    global hf_analyzer
+    if use_hf:
+        if hf_analyzer is None:
+            from transformers import pipeline
+            hf_analyzer = pipeline("sentiment-analysis")
+        result = hf_analyzer(text)[0]
+        label = result["label"].upper()
+        confidence = float(result["score"])
+        return confidence if label == "POSITIVE" else -confidence
+
     scores = analyzer.polarity_scores(text)
-    sentiment_score = scores["compound"]
-    return sentiment_score
+    return scores["compound"]