deploy app to space

App.py

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+from PIL import Image
+import gradio as gr
+import re
+import pandas as pd
+import joblib
+import datetime
+import matplotlib.pyplot as plt
+from io import BytesIO
+from nltk.tokenize import TreebankWordTokenizer
+from nltk.stem import WordNetLemmatizer
+from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS
+import os
+import time
+import zipfile
+
+# Load models and label mapping
+lda = joblib.load("lda_model.joblib")
+vectorizer = joblib.load("vectorizer.joblib")
+auto_labels = joblib.load("topic_labels.joblib")
+
+#Optional topic summaries
+topic_summaries = {
+    "Politics & Gun Rights": "Discussions about government policies, laws, gun control, and rights.",
+    "Computing & Hardware": "Technical issues and terms related to computer hardware and drivers.",
+    "Programming & Software": "Programming terms, file handling, software output.",
+    "Sports & Games": "Topics related to teams, players, seasons, and matches.",
+    "Health & Medicine": "Diseases, treatment, healthcare, and medical facilities.",
+    "Religion & Philosophy": "Talks involving faith, belief systems, philosophical views.",
+    "Space & NASA": "Space exploration, NASA missions, satellites, and astronomy.",
+    "Cryptography & Security": "Discussions on encryption, digital security, and data protection.",
+    "Internet & Networking": "Terms around internet use, FTP, web versions, and networks.",
+    "Middle East Politics & Conflicts": "Topics involving Israel, Armenia, conflict regions."
+}
+
+#Tokenizer and lemmatizer
+tokenizer = TreebankWordTokenizer()
+lemmatizer = WordNetLemmatizer()
+
+# --- Utility Functions ---
+
+def preprocess(text):
+    text = re.sub(r'\W+', ' ', text.lower())
+    tokens = tokenizer.tokenize(text)
+    tokens = [lemmatizer.lemmatize(w) for w in tokens if w not in ENGLISH_STOP_WORDS and len(w) > 2 and w.isalpha()]
+    return ' '.join(tokens)
+
+def get_topic_keywords(model, vectorizer, topic_idx, top_n=10):
+    feature_names = vectorizer.get_feature_names_out()
+    topic = model.components_[topic_idx]
+    top_indices = topic.argsort()[:-top_n - 1:-1]
+    return [feature_names[i] for i in top_indices]
+
+def plot_topic_distribution(distribution, labels):
+    plt.figure(figsize=(8, 4))
+    plt.bar(range(len(distribution)), distribution, tick_label=labels)
+    plt.xticks(rotation=45, ha="right")
+    plt.ylabel("Probability")
+    plt.title("Topic Distribution")
+    plt.tight_layout()
+    buf = BytesIO()
+    plt.savefig(buf, format="png")
+    plt.close()
+    buf.seek(0)
+    return Image.open(buf)
+
+def save_prediction_file(text):
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+    filename = f"lda_prediction_{timestamp}.txt"
+    with open(filename, "w", encoding="utf-8") as f:
+        f.write(text)
+    return filename
+
+def cleanup_old_predictions(directory=".", extension=".txt", max_age_minutes=10):
+    now = time.time()
+    max_age = max_age_minutes * 60
+    for fname in os.listdir(directory):
+        if fname.endswith(extension) and fname.startswith("lda_prediction_"):
+            full_path = os.path.join(directory, fname)
+            if os.path.isfile(full_path) and (now - os.path.getmtime(full_path)) > max_age:
+                try:
+                    os.remove(full_path)
+                except Exception as e:
+                    print(f"Failed to delete {fname}: {e}")
+
+def download_log():
+    zip_filename = "lda_predictions_log.zip"
+    with zipfile.ZipFile(zip_filename, "w", zipfile.ZIP_DEFLATED) as zipf:
+        zipf.write("lda_predictions_log.csv")
+    return zip_filename
+
+def save_feedback(text, feedback):
+    timestamp = datetime.datetime.now().isoformat()
+    log_entry = pd.DataFrame([{
+        "timestamp": timestamp,
+        "feedback": feedback,
+        "text_excerpt": text[:300].replace('\n', ' ') + "..."
+    }])
+    feedback_log = "lda_feedback_log.csv"
+    log_entry.to_csv(feedback_log, mode='a', header=not os.path.exists(feedback_log), index=False)
+    return " Feedback recorded. Thank you!"
+
+# --- Main Prediction Function ---
+
+def predict_topic(text_input, file_input):
+    cleanup_old_predictions()
+
+    if file_input is not None:
+        text = file_input.read().decode("utf-8")
+    elif text_input.strip():
+        text = text_input
+    else:
+        return "Please provide input", None, None
+
+    cleaned = preprocess(text)
+    bow = vectorizer.transform([cleaned])
+    topic_distribution = lda.transform(bow)[0]
+    dominant_topic = topic_distribution.argmax()
+    label = auto_labels.get(dominant_topic, f"Topic {dominant_topic+1}")
+    top_words = get_topic_keywords(lda, vectorizer, dominant_topic)
+    summary = topic_summaries.get(label, "No summary available.")
+
+    # Confidence threshold warning
+    confidence_threshold = 0.4
+    if topic_distribution[dominant_topic] < confidence_threshold:
+        label += " ( Low confidence)"
+        summary = " The model is uncertain. Try providing more context or a longer input."
+
+    # Log entry
+    timestamp = datetime.datetime.now().isoformat()
+    log_entry = pd.DataFrame([{
+        "timestamp": timestamp,
+        "predicted_topic": label,
+        "dominant_topic_index": dominant_topic,
+        "top_words": ", ".join(top_words),
+        "text_excerpt": text[:300].replace('\n', ' ') + "..."
+    }])
+    log_path = "lda_predictions_log.csv"
+    log_entry.to_csv(log_path, mode='a', header=not os.path.exists(log_path), index=False)
+
+    chart = plot_topic_distribution(topic_distribution, [auto_labels.get(i, f"Topic {i+1}") for i in range(len(topic_distribution))])
+
+    result = f" **Predicted Topic:** {label}\n\n"
+    result += f" **Summary:** {summary}\n\n"
+    result += f" **Top Words:** {', '.join(top_words)}\n\n"
+    result += " **Topic Distribution:**\n"
+    for idx, prob in enumerate(topic_distribution):
+        tlabel = auto_labels.get(idx, f"Topic {idx+1}")
+        result += f"{tlabel}: {prob:.3f}\n"
+
+    prediction_file = save_prediction_file(result)
+    return result, chart, prediction_file
+
+# --- Gradio Interface ---
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Topic Modeling with LDA")
+    gr.Markdown("Upload a `.txt` file or paste in text. See predicted topic, keywords, and a chart.")
+
+    with gr.Row():
+        with gr.Column():
+            text_input = gr.Textbox(lines=10, label=" Paste Text")
+            file_input = gr.File(label=" Or Upload a .txt File", file_types=[".txt"])
+            predict_btn = gr.Button(" Predict Topic")
+            download_btn = gr.Button("⬇ Download All Logs")
+
+            feedback_input = gr.Radio(
+                choices=["Accurate", " Inaccurate", "Unclear"],
+                label=" Was this prediction useful?",
+                interactive=True
+            )
+            feedback_btn = gr.Button("Submit Feedback")
+            feedback_output = gr.Textbox(visible=False)
+
+        with gr.Column():
+            output_text = gr.Textbox(label=" Prediction Result")
+            output_chart = gr.Image(type="pil", label=" Topic Distribution")
+            download_prediction = gr.File(label="⬇ Download This Prediction")
+
+    predict_btn.click(
+        fn=predict_topic,
+        inputs=[text_input, file_input],
+        outputs=[output_text, output_chart, download_prediction]
+    )
+
+    download_btn.click(fn=download_log, outputs=[gr.File()])
+
+    feedback_btn.click(
+        fn=save_feedback,
+        inputs=[text_input, feedback_input],
+        outputs=[feedback_output]
+    )
+
+demo.launch()

lda_model.joblib

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+size 4572069

topic_labels.joblib

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+oid sha256:5d73a67d3020a72d2b6a70491eaabc006f46ed077d4fedecb5d33d3a63d01792
+size 146

vectorizer.joblib

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