fix: make model more robust and aware of the tone of the comment.

.gitattributes

@@ -2,3 +2,4 @@
 *.kv filter=lfs diff=lfs merge=lfs -text
 SA_model/model.safetensors filter=lfs diff=lfs merge=lfs -text
 *.bin filter=lfs diff=lfs merge=lfs -text
+sentiment_analyzer_pro/model.safetensors filter=lfs diff=lfs merge=lfs -text

app.py

@@ -4,44 +4,70 @@ import torch
 
 app = Flask(__name__)
 
-# MODEL LOADING
-# Replace './SA_model' with the actual path to your model folder.
-model_path = "./SA_model"
+#  1. MODEL CONFIGURATION 
+# Ensure this path matches your unzipped folder name exactly.
+MODEL_PATH = "./sentiment_analyzer_pro"
 
-# We use the pipeline API which handles tokenization and inference automatically.
-# device=0 for GPU, but use device=-1 for Hugging Face free CPU spaces.
-print("Loading DistilBERT model...")
-classifier = pipeline(
-    "sentiment-analysis", 
-    model=model_path, 
-    tokenizer=model_path,
-    device=-1 
-)
-print("Model loaded successfully!")
+# Load the DistilBERT pipeline. 
+# We use device=-1 to ensure it runs on CPU, which is standard for free Hugging Face Spaces.
+print("Loading DistilBERT 3-class model...")
+try:
+    classifier = pipeline(
+        "sentiment-analysis", 
+        model=MODEL_PATH, 
+        tokenizer=MODEL_PATH,
+        device=-1 
+    )
+    print("Model loaded successfully!")
+except Exception as e:
+    print(f"Error loading model: {e}")
 
-# FLASK ENDPOINT
+#  2. PREDICTION ENDPOINT 
 @app.route('/predict', methods=['POST'])
 def predict_endpoint():
+    """
+    Receives JSON input: {"text": "Your review here"}
+    Returns JSON: {"sentiment": "Label", "score": 0.99, "confidence_flag": "High/Low"}
+    """
     data = request.get_json()
+    
+    # Validate input
     if not data or 'text' not in data:
         return jsonify({'error': 'No text provided'}), 400
     
     sentence = data['text']
     
-    # Transformer models handle negation (like "not good") automatically
-    # No manual negation flipping is needed anymore.
+    # Perform inference
+    # Result is a list: [{'label': 'POSITIVE', 'score': 0.98}]
     result = classifier(sentence)[0]
     
-    # result is like: {'label': 'POSITIVE', 'score': 0.99}
     label = result['label']
     score = result['score']
     
-    # For the extension UI, we can normalize the score if needed
-    # (Transformers already give high confidence for clear sentiment)
+    #  3. INTELLIGENT SARCASM/MIXED LOGIC 
+    # We use 0.70 (70%) as the "Sureness" threshold.
+    # If the model is less than 70% confident, we categorize it as Neutral/Mixed.
+    # This captures sarcasm where the model sees conflicting emotional signals.
+    if score < 0.70:
+        final_sentiment = "Neutral / Mixed"
+        confidence_flag = "Low"
+    else:
+        # Standardize labels from 'POSITIVE' to 'Positive'
+        final_sentiment = label.capitalize()
+        confidence_flag = "High"
+    
     return jsonify({
-        'sentiment': label.capitalize(), 
-        'score': score
+        'sentiment': final_sentiment, 
+        'score': round(score, 4),
+        'confidence_flag': confidence_flag
     })
 
+#  4. HEALTH CHECK 
+@app.route('/', methods=['GET'])
+def health_check():
+    return "Sentiment Analyzer Pro API is online."
+
 if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=7860) # Port 7860 is standard for HF Spaces
+    # Port 7860 is required for Hugging Face Spaces deployment.
+    # host='0.0.0.0' allows external connections (like your Chrome Extension).
+    app.run(host='0.0.0.0', port=7860)

{SA_model → sentiment_analyzer_pro}/config.json

@@ -10,12 +10,14 @@
   "hidden_dim": 3072,
   "id2label": {
     "0": "NEGATIVE",
-    "1": "POSITIVE"
+    "1": "NEUTRAL",
+    "2": "POSITIVE"
   },
   "initializer_range": 0.02,
   "label2id": {
     "NEGATIVE": 0,
-    "POSITIVE": 1
+    "NEUTRAL": 1,
+    "POSITIVE": 2
   },
   "max_position_embeddings": 512,
   "model_type": "distilbert",

{SA_model → sentiment_analyzer_pro}/model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6220a6c2266a1b6c7da3bf162edb758fe1e5ddbfd3bf324c2109ad1344257f11
-size 267832560
+oid sha256:b16ebe1022a63bc0374b028e0f6db207742dfc47630a185499f969cfda26e337
+size 267835644

{SA_model → sentiment_analyzer_pro}/training_args.bin

@@ -1,3 +1,3 @@
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-oid sha256:2179dc78f0e4e7a1a23648e4b57ef62308d7f8eb9a6d6d6f697e66945c10574a
-size 5777
+oid sha256:d92bbcf424eeef97dde37cd8973c7852e1d09ef372ece889b0be6bce50f03c4b
+size 5841