Update app.py

app.py

@@ -2,60 +2,65 @@ import gradio as gr
 from bertopic import BERTopic
 from sentence_transformers import SentenceTransformer
 
+
 def run_from_textfile(file):
     if file is None:
         return "Please upload a .txt file.", "", None
 
-    # Read text file
-    text = file.read().decode("utf-8")
+    # ---- Handle file input for both HuggingFace and local environments ----
+    try:
+        # HuggingFace Spaces: file is NamedString and supports .decode()
+        text = file.decode("utf-8")
+    except:
+        # Local Gradio: file is a TemporaryFile-like object
+        text = file.read().decode("utf-8")
+
+    # Split the text into documents (one per line)
     docs = [line.strip() for line in text.split("\n") if line.strip()]
 
     if len(docs) < 3:
         return "Need at least 3 documents (one per line).", "", None
 
-    # Embedding model
+    # ---- Embedding Model ----
     embedder = SentenceTransformer("all-MiniLM-L6-v2")
 
-    # Topic model
+    # ---- Topic Modeling ----
     topic_model = BERTopic(embedding_model=embedder)
     topics, probs = topic_model.fit_transform(docs)
 
-    # Topic info
+    # ---- Topic Summary ----
     topic_info = topic_model.get_topic_info().to_string()
 
-    # Map each doc to its topic
+    # ---- Document → Topic Assignments ----
     assignments = "\n".join([f"Doc {i+1}: Topic {topics[i]}" for i in range(len(docs))])
 
-    # Visualization
+    # ---- Visualization ----
     fig = topic_model.visualize_barchart(top_n_topics=10)
 
     return topic_info, assignments, fig
 
 
+# ---- Gradio Interface ----
 with gr.Blocks() as demo:
     gr.Markdown("# 🧠 Topic Modeling from TXT File (BERTopic)")
     gr.Markdown(
-        "Upload a plain text (.txt) file. Each line should contain one LLM response.\n\n"
-        "Example format:\n"
-        "```\n"
-        "Response 1...\n"
-        "Response 2...\n"
-        "Response 3...\n"
-        "```\n"
+        "Upload a plain text (.txt) file. Each line should contain **one LLM response**.\n"
+        "\nExample format:\n```\nResponse 1...\nResponse 2...\nResponse 3...\n```"
     )
 
     file_input = gr.File(label="Upload .txt file")
 
-    run_btn = gr.Button("Run Topic Modeling")
+    run_button = gr.Button("Run Topic Modeling")
 
-    topic_output = gr.Textbox(label="Topic Overview", lines=10)
-    assignment_output = gr.Textbox(label="Document → Topic Assignments", lines=10)
+    topic_output = gr.Textbox(label="Topic Overview", lines=12)
+    assignment_output = gr.Textbox(label="Document → Topic Assignments", lines=12)
     fig_output = gr.Plot(label="Topic Visualization")
 
-    run_btn.click(
+    run_button.click(
         fn=run_from_textfile,
         inputs=file_input,
         outputs=[topic_output, assignment_output, fig_output]
     )
 
+# Launch app
 demo.launch()