Update sentiment_analyzer.py

sentiment_analyzer.py

@@ -3,13 +3,13 @@ import torch
 import re
 
 class NewsAnalyzer:
-    def __init__(self, model_name=None): # Model_name is no longer needed
+    def __init__(self, model_name=None):
         """
         Initialize news analyzer with fast, CPU-friendly Zero-Shot pipelines
         """
         print("Initializing Zero-Shot News Analyzer...")
         
-        self.device = 0 if torch.cuda.is_available() else -1 # Use 0 for GPU, -1 for CPU
+        self.device = 0 if torch.cuda.is_available() else -1
         print(f"Using device: {'cuda' if self.device == 0 else 'cpu'}")
 
         try:
@@ -23,11 +23,13 @@ class NewsAnalyzer:
 
             # Pipeline 2: For Zero-Shot Classification (Theme & Impact)
             print("Loading Zero-Shot model...")
+            # --- MODIFIED: Corrected model name ---
             self.classifier_pipeline = pipeline(
                 "zero-shot-classification",
-                model="Moritz/bart-large-mnli-fever-anli-ling-wanli",
+                model="facebook/bart-large-mnli", # นี่คือโมเดลมาตรฐานที่ถูกต้อง
                 device=self.device
             )
+            # --- End of modification ---
             
             print("Models loaded successfully!")
 
@@ -47,15 +49,23 @@ class NewsAnalyzer:
         """
         วิเคราะห์ข่าว (Sentiment, Theme, Impact) โดยใช้ Zero-Shot
         """
-        if not text or len(text.strip()) == 0 or not self.classifier_pipeline:
+        # ตรวจสอบว่า pipeline โหลดสำเร็จหรือไม่
+        if not self.classifier_pipeline or not self.sentiment_pipeline:
+            print("Error: Pipelines are not loaded.")
             return {
                 "sentiment": "Neutral", "score": 0.5, "theme": "N/A",
-                "impact": "N/A", "explanation": "No text or model"
+                "impact": "N/A", "explanation": "Model loading failed"
+            }
+
+        if not text or len(text.strip()) == 0:
+            return {
+                "sentiment": "Neutral", "score": 0.5, "theme": "N/A",
+                "impact": "N/A", "explanation": "No text"
             }
 
         try:
             # 1. Analyze Sentiment
-            sentiment_result = self.sentiment_pipeline(text[:512])[0] # Truncate for speed
+            sentiment_result = self.sentiment_pipeline(text[:512])[0]
             sentiment = sentiment_result['label'].capitalize()
             score = sentiment_result['score']
             
@@ -76,8 +86,12 @@ class NewsAnalyzer:
             # 4. Create an explanation
             explanation = f"Classified as '{theme}' (Impact: {impact}) via zero-shot analysis."
 
+            # แปลง Sentiment จาก 'Positive'/'Negative' ของ distilbert
+            # เป็น 'Positive'/'Negative'/'Neutral' (แต่โมเดลนี้ไม่มี Neutral)
+            final_sentiment = "Positive" if sentiment == "Positive" else "Negative"
+
             return {
-                "sentiment": "Positive" if sentiment == "Positive" else "Negative", # Simple conversion
+                "sentiment": final_sentiment,
                 "score": score,
                 "theme": theme,
                 "impact": impact,
@@ -103,6 +117,4 @@ class NewsAnalyzer:
                 **news,
                 **sentiment_result
             })
-        return results
-
-# --- ไม่ต้องใช้ฟังก์ชัน _parse หรือ _fallback อีกต่อไป ---
+        return results