Create app.py

app.py

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+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")
+    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
+    embedder = SentenceTransformer("all-MiniLM-L6-v2")
+
+    # Topic model
+    topic_model = BERTopic(embedding_model=embedder)
+    topics, probs = topic_model.fit_transform(docs)
+
+    # Topic info
+    topic_info = topic_model.get_topic_info().to_string()
+
+    # Map each doc to its topic
+    assignments = "\n".join([f"Doc {i+1}: Topic {topics[i]}" for i in range(len(docs))])
+
+    # Visualization
+    fig = topic_model.visualize_barchart(top_n_topics=10)
+
+    return topic_info, assignments, fig
+
+
+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"
+    )
+
+    file_input = gr.File(label="Upload .txt file")
+
+    run_btn = gr.Button("Run Topic Modeling")
+
+    topic_output = gr.Textbox(label="Topic Overview", lines=10)
+    assignment_output = gr.Textbox(label="Document → Topic Assignments", lines=10)
+    fig_output = gr.Plot(label="Topic Visualization")
+
+    run_btn.click(
+        fn=run_from_textfile,
+        inputs=file_input,
+        outputs=[topic_output, assignment_output, fig_output]
+    )
+
+demo.launch()