Upload sentiment_api.py

sentiment_api.py

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+# filepath: [sentiment_api.py](http://_vscodecontentref_/0)
+from fastapi import FastAPI, Request
+from fastapi.middleware.cors import CORSMiddleware  # ← TAMBAH INI
+from pydantic import BaseModel
+from fastapi.responses import JSONResponse
+import re
+import os
+
+app = FastAPI(title="Indonesian Sentiment Analysis API", 
+              description="API untuk analisis sentimen bahasa Indonesia dengan dukungan bahasa gaul",
+              version="1.0.0")
+
+# ← TAMBAH CORS MIDDLEWARE
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Allows all origins
+    allow_credentials=True,
+    allow_methods=["*"],  # Allows all methods
+    allow_headers=["*"],  # Allows all headers
+)
+
+# Global variable untuk model (akan diload jika tersedia)
+model = None
+tokenizer = None
+model_loaded = False
+
+def load_model():
+    """Try to load IndoBERT sentiment model, fallback to enhanced keyword if failed"""
+    global model, tokenizer, model_loaded
+    
+    # List model alternatif yang bisa dicoba
+    model_options = [
+        "indolem/indobert-base-uncased",  # Model yang lebih umum dan pasti tersedia
+        "cahya/bert-base-indonesian-1.5G",  # Alternative Indonesian BERT
+        "mdhugol/indonesia-bert-sentiment-classification"  # Specific sentiment model
+    ]
+    
+    for model_name in model_options:
+        try:
+            print(f"🔄 Trying to load model: {model_name}")
+            from transformers import AutoTokenizer, AutoModelForSequenceClassification
+            
+            # Load tokenizer
+            print(f"📥 Downloading tokenizer for {model_name}...")
+            tokenizer = AutoTokenizer.from_pretrained(model_name)
+            print("✅ Tokenizer loaded successfully!")
+            
+            # Load model
+            print(f"📥 Downloading model {model_name} (this may take a while)...")
+            model = AutoModelForSequenceClassification.from_pretrained(model_name)
+            print("✅ Model loaded successfully!")
+            
+            model_loaded = True
+            print(f"🎉 {model_name} ready for sentiment analysis!")
+            return  # Exit jika berhasil
+            
+        except Exception as e:
+            print(f"❌ Failed to load {model_name}: {e}")
+            continue  # Coba model berikutnya
+    
+    # Jika semua model gagal
+    print("❌ All models failed to load")
+    print("🔄 Using enhanced keyword-based analysis instead")
+    model_loaded = False
+
+# Try to load model on startup
+load_model()
+
+@app.get("/")
+async def root():
+    """Root endpoint"""
+    model_name = "Unknown"
+    if model_loaded and model is not None:
+        model_name = model.config.name_or_path if hasattr(model.config, 'name_or_path') else "Indonesian BERT Model"
+    
+    return {
+        "message": "Indonesian Sentiment Analysis API", 
+        "version": "1.0.0",
+        "docs": "/docs",
+        "model_loaded": model_loaded,
+        "model_name": model_name if model_loaded else "Enhanced Keyword Analysis",
+        "model_type": "🤖 AI Model" if model_loaded else "📝 Keyword Analysis",
+        "status": "🎉 Ready!" if model_loaded else "📝 Keyword Ready!"
+    }
+
+@app.get("/health")
+async def health_check():
+    """Health check endpoint"""
+    return {
+        "status": "healthy", 
+        "model_loaded": model_loaded,
+        "model_type": "IndoBERTweet" if model_loaded else "Enhanced Keyword Analysis",
+        "ready": True
+    }
+
+class TextRequest(BaseModel):
+    text: str
+
+def normalize_slang(text):
+    """Normalisasi kata gaul/slang ke bahasa baku"""
+    slang_dict = {
+        'gw': 'saya', 'gue': 'saya', 'w': 'saya',
+        'lu': 'kamu', 'elu': 'kamu', 'lo': 'kamu',
+        'gk': 'tidak', 'ga': 'tidak', 'gak': 'tidak', 'engga': 'tidak',
+        'bgt': 'banget', 'bgt': 'sangat',
+        'btw': 'ngomong ngomong', 'fyi': 'informasi',
+        'yg': 'yang', 'yng': 'yang',
+        'dgn': 'dengan', 'dg': 'dengan',
+        'org': 'orang', 'orng': 'orang',
+        'udh': 'sudah', 'udah': 'sudah', 'dah': 'sudah',
+        'blm': 'belum', 'blom': 'belum',
+        'bkn': 'bukan', 'bukan': 'bukan',
+        'krn': 'karena', 'krna': 'karena',
+        'trs': 'terus', 'trus': 'terus',
+        'jg': 'juga', 'jga': 'juga',
+        'aja': 'saja', 'ajah': 'saja',
+        'emg': 'memang', 'emang': 'memang',
+        'tp': 'tapi', 'tapi': 'tetapi',
+        'kalo': 'kalau', 'klo': 'kalau',
+        'gimana': 'bagaimana', 'gmn': 'bagaimana',
+        'knp': 'kenapa', 'knapa': 'kenapa',
+        'mantap': 'bagus', 'mantul': 'bagus',
+        'anjay': 'wah', 'anjir': 'wah',
+        'gabut': 'tidak ada kegiatan',
+        'mager': 'malas gerak',
+        'baper': 'bawa perasaan',
+        'santuy': 'santai',
+        'kepo': 'ingin tahu',
+        'php': 'pemberi harapan palsu',
+        'bucin': 'budak cinta',
+        # Tambahan kata positif yang sering dipakai
+        'seneng': 'senang', 'senang': 'senang',
+        'bahagia': 'bahagia', 'happy': 'senang',
+        'kamaren': 'kemarin', 'kemaren': 'kemarin'
+    }
+    
+    # Convert to lowercase
+    text = text.lower()
+    
+    # Replace slang words
+    for slang, formal in slang_dict.items():
+        text = re.sub(r'\b' + slang + r'\b', formal, text)
+    
+    return text
+
+def analyze_sentiment(text):
+    """Analisis sentimen dengan IndoBERTweet atau enhanced keyword"""
+    global model, tokenizer, model_loaded
+    
+    # Normalisasi kata gaul
+    normalized_text = normalize_slang(text)
+    
+    # Debug info
+    print(f"🔍 Analyzing: '{text}'")
+    print(f"🔧 Normalized: '{normalized_text}'")
+    print(f"🤖 Model loaded: {model_loaded}")
+    
+    # Coba gunakan IndoBERTweet jika tersedia
+    if model_loaded and model is not None and tokenizer is not None:
+        try:
+            import torch
+            print("🎯 Using IndoBERTweet model...")
+            
+            # Tokenize input
+            inputs = tokenizer(normalized_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+            
+            # Get prediction
+            with torch.no_grad():
+                outputs = model(**inputs)
+                logits = outputs.logits
+                probabilities = torch.softmax(logits, dim=1)
+                pred = torch.argmax(logits, dim=1).item()
+                confidence = torch.max(probabilities).item()
+            
+            print(f"📊 IndoBERTweet prediction: {pred} (confidence: {confidence:.3f})")
+            print(f"📊 Probabilities: {probabilities.numpy()}")
+            
+            # Mapping label IndoBERTweet ke rating bintang
+            # IndoBERTweet: 0=negative, 1=neutral, 2=positive
+            if pred == 2:  # positive
+                result = 5 if confidence > 0.8 else 4
+                print(f"✅ Result: {result} stars (Positive)")
+                return result
+            elif pred == 1:  # neutral
+                result = 3
+                print(f"😐 Result: {result} stars (Neutral)")
+                return result
+            else:  # negative (pred == 0)
+                result = 1 if confidence > 0.8 else 2
+                print(f"❌ Result: {result} stars (Negative)")
+                return result
+                
+        except Exception as e:
+            print(f"⚠️ Error using IndoBERTweet: {e}")
+            print("🔄 Falling back to keyword analysis...")
+    
+    # Enhanced keyword-based analysis (fallback)
+    print("🔤 Using enhanced keyword analysis...")
+    result = enhanced_keyword_analysis(normalized_text, text)
+    print(f"📝 Keyword analysis result: {result} stars")
+    return result
+
+def enhanced_keyword_analysis(normalized_text, original_text):
+    """Enhanced keyword analysis untuk bahasa Indonesia + slang"""
+    text_lower = normalized_text.lower()
+    
+    # Positive keywords (diperbanyak dan lebih sensitif)
+    positive_words = [
+        "senang", "bahagia", "happy", "mantap", "bagus", "keren", "suka", "cinta", "love",
+        "amazing", "luar biasa", "hebat", "fantastis", "sempurna", "excellent", "good",
+        "positif", "optimis", "gembiraan", "kebahagiaan", "sukses", "berhasil", "menang",
+        "excited", "antusias", "semangat", "motivasi", "inspirasi", "grateful", "bersyukur",
+        "mantul", "jos", "top", "juara", "recommended", "worth it", "puas", "satisfied",
+        "gembira", "asyik", "asik", "cool", "nice", "wonderful", "great", "awesome"
+    ]
+    
+    # Strong positive words (kata yang sangat positif)
+    strong_positive_words = [
+        "banget", "sangat", "luar biasa", "fantastis", "sempurna", "amazing", "awesome",
+        "gembira", "bahagia banget", "senang banget", "happy banget"
+    ]
+    
+    # Negative keywords (diperbanyak)
+    negative_words = [
+        "marah", "kesal", "benci", "jelek", "buruk", "jahat", "sedih", "kecewa", "galau",
+        "frustrated", "angry", "hate", "bad", "terrible", "awful", "horrible", "disgusting",
+        "menyebalkan", "annoying", "stress", "depresi", "down", "hopeless", "putus asa",
+        "fail", "gagal", "rugi", "loss", "disappointed", "broken heart", "sakit hati",
+        "toxic", "drama", "problem", "masalah", "susah", "sulit", "capek", "tired"
+    ]
+    
+    # Neutral/Mixed keywords
+    neutral_words = [
+        "biasa", "standard", "normal", "okay", "ok", "fine", "lumayan", "so so",
+        "average", "medium", "moderate", "netral", "balanced", "mixed feelings"
+    ]
+    
+    # Negation words (kata negasi)
+    negation_words = ["tidak", "bukan", "jangan", "gak", "ga", "engga", "no", "nope", "never"]
+    
+    # Count sentiment words
+    positive_count = sum(1 for word in positive_words if word in text_lower)
+    strong_positive_count = sum(1 for word in strong_positive_words if word in text_lower)
+    negative_count = sum(1 for word in negative_words if word in text_lower)
+    neutral_count = sum(1 for word in neutral_words if word in text_lower)
+    
+    # Check for combinations like "senang banget"
+    if "senang banget" in text_lower or "bahagia banget" in text_lower or "happy banget" in text_lower:
+        strong_positive_count += 2
+    
+    # Check for negations
+    has_negation = any(neg in text_lower for neg in negation_words)
+    
+    # Advanced scoring with context
+    if has_negation:
+        # If there's negation, flip the sentiment partially
+        if positive_count > negative_count:
+            return 3  # Neutral instead of positive
+        elif negative_count > positive_count:
+            return 4  # Less negative
+    
+    # Calculate sentiment score with strong positive bonus
+    total_positive = positive_count + (strong_positive_count * 2)  # Strong words worth double
+    
+    if total_positive > negative_count + neutral_count:
+        return 5  # Strong positive
+    elif total_positive > negative_count:
+        return 4  # Mild positive
+    elif negative_count > total_positive + neutral_count:
+        return 1  # Strong negative
+    elif negative_count > total_positive:
+        return 2  # Mild negative
+    else:
+        return 3  # Neutral
+
+@app.post("/predict")
+async def predict(req: TextRequest):
+    stars = analyze_sentiment(req.text)
+    return JSONResponse(content={"stars": stars})