sentiment.py

import os
import re
from groq import Groq
from dotenv import load_dotenv

load_dotenv()
client = Groq(api_key=os.environ.get("GROQ_API_KEY"))

def is_meaningful(text):
    """Checks if the text contains actual words or just gibberish."""
    text_stripped = text.strip()
    
    # 1. Basic length check (already enforced by UI, but good for safety)
    if len(text_stripped) < 3:
        return False

    # 2. Check for vowel ratio (catches 'bdfghj')
    vowels = len(re.findall(r'[aeiouAEIOU]', text_stripped))
    total_chars = len(re.findall(r'[a-zA-Z]', text_stripped))
    if total_chars > 0 and (vowels / total_chars) < 0.1:
        return False 

    # 3. Check for repetitive characters (catches 'aaaaaa')
    if re.search(r'(.)\1{4,}', text_stripped):
        return False
        
    return True

def analyze_sentiment(review_text, rating=3):
    """
    Core sentiment logic. Gibberish is filtered locally; 
    meaningful text is analyzed by Llama 3.1.
    """
    text_clean = str(review_text).strip()

    # --- STEP 1: SENSE CHECK (GIBBERISH FILTER) ---
    if not is_meaningful(text_clean):
        return "Neutral"

    # --- STEP 2: AI LOGIC ---
    # Enhanced prompt to ensure 'bad product' is caught as Negative
    system_msg = (
        "You are a strict sentiment classifier. Output ONLY one word: Positive, Negative, or Neutral. "
        "Priority Rule: If the text contains negative words (bad, poor, hate, worst), "
        "the output MUST be Negative, regardless of the star rating."
    )
    
    user_prompt = f"Review Text: '{text_clean}'\nStar Rating: {rating}/5\nSentiment:"

    try:
        chat_completion = client.chat.completions.create(
            messages=[
                {"role": "system", "content": system_msg},
                {"role": "user", "content": user_prompt}
            ],
            model="llama-3.1-8b-instant",
            temperature=0, # Zero temperature ensures the most predictable result
            max_tokens=3 
        )
        
        # Clean the output from the AI
        result = chat_completion.choices[0].message.content.strip().replace(".", "")
        
        if result in ["Positive", "Negative", "Neutral"]:
            return result
        return "Neutral"
        
    except Exception as e:
        print(f"Error calling Groq API: {e}")
        return "Neutral"