Update app.py

app.py

@@ -1,128 +1,3 @@
-# import gradio as gr
-# import joblib
-# import pandas as pd
-# import re
-# import nltk
-# import numpy as np
-# import traceback
-# from khmernltk import word_tokenize
-
-# # --- 1. SETUP ---
-# try:
-#     nltk.data.find('corpora/stopwords')
-# except LookupError:
-#     nltk.download('stopwords')
-
-# from nltk.corpus import stopwords
-# english_stopwords = set(stopwords.words('english'))
-
-# # CRITICAL: This list MUST match the order of your LabelEncoder classes (0, 1, 2...)
-# LABELS = [
-#     'Culture', 'Economic', 'Education', 'Environment',
-#     'Health', 'Politics', 'Human Rights', 'Science'
-# ]
-
-# def clean_khmer_text(text):
-#     if not isinstance(text, str): return ""
-#     text = re.sub(r'<[^>]+>', '', text)
-#     text = re.sub(r'[\u200B-\u200D\uFEFF]', '', text)
-#     text = re.sub(r'[!"#$%&\'()*+,—./:;<=>?@[\]^_`{|}~។៕៖ៗ៘៙៚៛«»-]', '', text)
-#     text = re.sub(r'\s+', ' ', text).strip()
-#     return text
-
-# def khmer_tokenize(text):
-#     cleaned = clean_khmer_text(text)
-#     if not cleaned: return ""
-#     tokens = word_tokenize(cleaned)
-#     processed_tokens = []
-#     for token in tokens:
-#         if re.match(r'^[a-zA-Z0-9]+$', token):
-#             token_lower = token.lower()
-#             if token_lower in english_stopwords: continue
-#             processed_tokens.append(token_lower)
-#         else:
-#             processed_tokens.append(token)
-#     return " ".join(processed_tokens)
-
-# # # --- 2. LOAD MODELS ---
-# print("Loading processors...")
-# try:
-#     vectorizer = joblib.load("tfidf_vectorizer.joblib")
-#     svd = joblib.load("truncated_svd.joblib")
-#     print("✅ Vectorizer & SVD loaded")
-# except Exception as e:
-#     print(f"❌ CRITICAL LOAD ERROR: {e}")
-
-# models = {}
-# model_files = {
-#     "XGBoost": "xgboost_model.joblib",
-#     "LightGBM": "lightgbm_model.joblib",
-#     "Random Forest": "random_forest_model.joblib",
-#     "Logistic Regression": "logistic_regression_model.joblib",
-#     "Linear SVM": "linear_svm_model.joblib"
-# }
-
-# for name, filename in model_files.items():
-#     try:
-#         models[name] = joblib.load(filename)
-#         print(f"✅ Loaded {name}")
-#     except:
-#         print(f"⚠️ Skipping {name}")
-
-# # --- 3. PREDICTION FUNCTION ---
-# def predict(text, model_name):
-#     if not text: return "Please enter text", {}, []
-#     if model_name not in models: return "Model not found", {}, []
-    
-#     try:
-#         # Pipeline
-#         processed = khmer_tokenize(text)
-#         vectors = vectorizer.transform([processed]) # TF-IDF Matrix (Sparse)
-#         vectors_reduced = svd.transform(vectors)    # SVD Matrix (Dense)
-#         model = models[model_name]
-        
-#         # --- EXTRACT KEYWORDS ---
-#         # We look at the TF-IDF vector to find the strongest words
-#         feature_array = np.array(vectorizer.get_feature_names_out())
-#         # Sort by score (descending)
-#         tfidf_sorting = np.argsort(vectors.toarray()).flatten()[::-1]
-        
-#         # Get top 10 words that actually have a score > 0
-#         top_n = 10
-#         keywords = []
-#         for idx in tfidf_sorting[:top_n]:
-#             if vectors[0, idx] > 0:
-#                 keywords.append(feature_array[idx])
-        
-#         # --- PREDICTION ---
-#         if hasattr(model, "predict_proba"):
-#             probas = model.predict_proba(vectors_reduced)[0]
-#             confidences = {LABELS[i]: float(probas[i]) for i in range(len(LABELS))}
-#             top_label = max(confidences, key=confidences.get)
-#         else:
-#             raw_pred = model.predict(vectors_reduced)[0]
-#             pred_idx = int(raw_pred) if isinstance(raw_pred, (int, np.integer)) else np.argmax(raw_pred)
-#             top_label = LABELS[pred_idx]
-#             confidences = {LABELS[pred_idx]: 1.0}
-            
-#         # Return 3 items: Label, Confidences, Keywords List
-#         return top_label, confidences, keywords
-        
-#     except Exception as e:
-#         return f"Error: {str(e)}", {}, []
-
-
-
-# # --- 4. LAUNCH ---
-# # IMPORTANT: allowed_origins="*" fixes the 405 error
-# demo = gr.Interface(
-#     fn=predict,
-#     inputs=[gr.Textbox(), gr.Dropdown(choices=list(models.keys()))],
-#     outputs=[gr.Label(), gr.Label(), gr.JSON()]
-# )
-# demo.launch()
-
-
 import gradio as gr
 import joblib
 import pandas as pd