ai-backend/server.py

from flask import Flask, request, jsonify
from flask_cors import CORS
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
import pytesseract
from PIL import Image
import requests
from io import BytesIO
import base64
import re
import datetime
import os

script_dir = os.path.dirname(os.path.abspath(__file__))
dist_dir = os.path.abspath(os.path.join(script_dir, '../dist'))

if os.path.exists(dist_dir):
    print(f"📦 Serving static files from production build: {dist_dir}")
    app = Flask(__name__, static_folder=dist_dir, static_url_path='/')
else:
    print("🧪 Running in development API mode (no dist folder found)")
    app = Flask(__name__)

# Enable CORS for all API paths
CORS(app, resources={r"/api/*": {"origins": "*"}})

print("🧠 Loading NLP model from dataset.csv...")
try:
    script_dir = os.path.dirname(os.path.abspath(__file__))
    dataset_path = os.path.join(script_dir, 'dataset.csv')
    df = pd.read_csv(dataset_path)
    df = df.dropna(subset=['text', 'Pattern Category'])
    model = make_pipeline(
        TfidfVectorizer(ngram_range=(1, 2)),
        LogisticRegression(C=10.0, class_weight='balanced', max_iter=1000)
    )
    model.fit(df['text'], df['Pattern Category'])
    print("✅ AI Model ready and trained!")
except Exception as e:
    print(f"❌ Error loading dataset: {e}")

print("📉 Loading Financial Distress model from Financial Distress.csv...")
distress_model = None
try:
    distress_path = os.path.join(script_dir, 'Financial Distress.csv')
    distress_df = pd.read_csv(distress_path)
    
    # Financial Distress target value <= -0.5 is distress (class 1), else healthy (class 0)
    features_cols = [f'x{i}' for i in range(1, 84)]
    distress_df = distress_df.dropna(subset=['Financial Distress'] + features_cols)
    
    X_distress = distress_df[features_cols]
    y_distress = (distress_df['Financial Distress'] <= -0.5).astype(int)
    
    distress_model = LogisticRegression(max_iter=1000)
    distress_model.fit(X_distress, y_distress)
    print("✅ Financial Distress Model ready and trained!")
except Exception as e:
    print(f"❌ Error loading Financial Distress dataset: {e}")

print("📰 Loading Reddit News from RedditNews.csv...")
news_list = []
try:
    news_path = os.path.join(script_dir, 'RedditNews.csv')
    news_df = pd.read_csv(news_path)
    news_df = news_df.dropna(subset=['News'])
    news_list = news_df.to_dict(orient='records')
    print(f"✅ Loaded {len(news_list)} news headlines successfully!")
except Exception as e:
    print(f"❌ Error loading RedditNews dataset: {e}")

def analyze_headline_sentiment(news_text):
    pos_words = ["gain", "rise", "success", "profit", "win", "high", "positive", "growth", "launch", "heal", "benefit", "good", "strong", "advance", "recover", "save", "safe"]
    neg_words = ["fail", "drop", "loss", "crash", "investigate", "lawsuit", "down", "recession", "decrease", "kill", "death", "protest", "strike", "bad", "weak", "decline", "default", "scandal", "abuse", "murder", "hurt", "risk"]
    
    text_lower = news_text.lower()
    pos_score = sum(1 for word in pos_words if word in text_lower)
    neg_score = sum(1 for word in neg_words if word in text_lower)
    
    if pos_score > neg_score:
        return "positive"
    elif neg_score > pos_score:
        return "negative"
    else:
        return "neutral"

def get_stock_news(symbol):
    symbol = symbol.upper()
    keywords = {
        "AAPL": ["apple", "iphone", "macbook", "ipad", "jobs", "tech"],
        "NVDA": ["chip", "nvidia", "gpu", "ai", "intel", "amd", "tech"],
        "TSLA": ["tesla", "elon", "musk", "electric", "battery", "car"],
        "COIN": ["bitcoin", "crypto", "blockchain", "exchange", "coinbase", "sec"],
        "MSFT": ["microsoft", "windows", "azure", "cloud", "tech", "gates"],
        "GOOGL": ["google", "alphabet", "search", "youtube", "android", "tech"],
    }
    
    stock_kws = keywords.get(symbol, [symbol.lower(), "market", "economy", "finance", "stocks", "trade", "shares"])
    
    matching = []
    for item in news_list:
        news_text = str(item['News'])
        text_lower = news_text.lower()
        if any(kw in text_lower for kw in stock_kws):
            matching.append(item)
            if len(matching) >= 100:
                break
                
    if len(matching) < 5:
        general_kws = ["market", "economy", "finance", "stocks", "trade", "shares"]
        for item in news_list:
            news_text = str(item['News'])
            text_lower = news_text.lower()
            if any(kw in text_lower for kw in general_kws):
                matching.append(item)
                if len(matching) >= 100:
                    break
                    
    formatted_news = []
    positive_count = 0
    negative_count = 0
    
    # We want a mix of headlines (e.g. 6 headlines)
    selected_items = matching[:6]
    if len(selected_items) < 6:
        selected_items = news_list[:6]
        
    for item in selected_items:
        headline = str(item['News'])
        date = str(item['Date'])
        sentiment = analyze_headline_sentiment(headline)
        
        if sentiment == "positive":
            positive_count += 1
        elif sentiment == "negative":
            negative_count += 1
            
        formatted_news.append({
            "headline": headline,
            "date": date,
            "sentiment": sentiment
        })
        
    total_val = positive_count + negative_count
    if total_val > 0:
        sentiment_pct = round((positive_count / total_val) * 100)
    else:
        # A deterministic fallback sentiment based on symbol hash
        hash_val = sum(ord(c) for c in symbol)
        sentiment_pct = 40 + (hash_val % 30) # 40% to 70% positive
        
    return {
        "articles": formatted_news,
        "sentimentPercent": sentiment_pct
    }

def get_distress_risk(symbol):
    if distress_model is None:
        return {"riskLevel": "Low", "distressProbability": 15.0, "rawDistressScore": 0.05, "isDistressed": False}
        
    symbol = symbol.upper()
    try:
        script_dir = os.path.dirname(os.path.abspath(__file__))
        distress_path = os.path.join(script_dir, 'Financial Distress.csv')
        distress_df = pd.read_csv(distress_path)
        
        # Filter rows to select distressed vs healthy for demo consistency
        distressed_rows = distress_df[distress_df['Financial Distress'] <= -0.5]
        healthy_rows = distress_df[distress_df['Financial Distress'] > 0.5]
        
        if len(distressed_rows) == 0 or len(healthy_rows) == 0:
            return {"riskLevel": "Low", "distressProbability": 10.0, "rawDistressScore": 0.1, "isDistressed": False}

        # Deterministic row selection based on symbol hash
        hash_val = sum(ord(c) for c in symbol)
        
        # Override specific symbols for demonstration purposes:
        if symbol == 'COIN':
            # Map COIN to a distressed row
            row = distressed_rows.iloc[hash_val % len(distressed_rows)]
        elif symbol in ['AAPL', 'NVDA', 'MSFT', 'GOOGL']:
            # Map healthy tech to healthy row
            row = healthy_rows.iloc[hash_val % len(healthy_rows)]
        else:
            # Map deterministically from entire dataset
            row = distress_df.iloc[hash_val % len(distress_df)]
            
        features_cols = [f'x{i}' for i in range(1, 84)]
        features = row[features_cols].values.reshape(1, -1)
        
        prob = distress_model.predict_proba(features)[0][1] # probability of class 1 (distress)
        is_distressed = bool(distress_model.predict(features)[0] == 1)
        
        # Define risk levels:
        if prob > 0.6 or is_distressed:
            risk_level = "High"
        elif prob > 0.25:
            risk_level = "Medium"
        else:
            risk_level = "Low"
            
        raw_score = float(row['Financial Distress'])
        
        return {
            "riskLevel": risk_level,
            "distressProbability": round(float(prob) * 100, 1),
            "rawDistressScore": round(raw_score, 3),
            "isDistressed": is_distressed
        }
    except Exception as e:
        print(f"Error evaluating distress risk for {symbol}: {e}")
        return {"riskLevel": "Low", "distressProbability": 15.0, "rawDistressScore": 0.1, "isDistressed": False}

def get_severity(prediction):
    severity_map = {
        "Urgency": "high",
        "Scarcity": "medium",
        "Social Proof": "low",
        "Misdirection": "high",
        "Obstruction": "critical",
        "Sneaking": "critical",
        "Forced Action": "critical"
    }
    return severity_map.get(prediction, "medium")

def get_compliance_metadata(prediction, text):
    if prediction == "Urgency":
        violation = "Urgency tactics create artificial pressure to force immediate transaction decisions, potentially violating 12 CFR 1041 prohibiting deceptive acts or practices."
        recommendation = f"Remove countdown timers or false urgency text like '{text}'."
    elif prediction == "Scarcity":
        violation = "Scarcity tactics (e.g. artificial stock limits) manipulate consumers into immediate purchases, violating FTC Act Section 5 against deceptive practices."
        recommendation = f"Ensure the statement '{text}' is backed by real-time inventory systems. If not verified, remove it."
    elif prediction == "Social Proof":
        violation = "Unverified social proof notifications (e.g. 'X bought this recently') can mislead consumers, violating general rules on deceptive advertisements."
        recommendation = f"Validate that '{text}' is based on genuine user activity. Otherwise, disable this alert."
    elif prediction == "Misdirection":
        violation = "Misdirection visual/language design (like confirmshaming) steers users away from their intended choices, violating consumer choice principles."
        recommendation = f"Change the option text in '{text}' to use clear and neutral language (e.g. 'Cancel' / 'Confirm') without guilt-tripping."
    elif prediction == "Obstruction":
        violation = "Obstruction (making cancellation or opt-out complex) violates EFTA and CFPB guidelines against hard-to-cancel billing structures."
        recommendation = f"Simplify subscription cancellation related to '{text}'. The exit path should be as simple as the sign-up path."
    elif prediction == "Sneaking":
        violation = "Sneaking (adding hidden costs or pre-selected add-ons) violates EFTA and deceptive practices rules by charging without active consent."
        recommendation = f"Ensure '{text}' does not lead to pre-checked options. Require explicit opt-in for all additional items or services."
    elif prediction == "Forced Action":
        violation = "Forced Action requires consumers to perform unrelated actions (e.g. consent to tracking) to finish a task, violating consumer choice guidelines."
        recommendation = f"Allow users to proceed past '{text}' without mandatory signups or sharing non-essential data."
    else:
        violation = "General deceptive pattern detected that may violate CFPB guidelines against deceptive acts or practices."
        recommendation = "Redesign copy and flow to maximize user transparency and choice."
    
    return violation, recommendation

@app.route('/api/analyze', methods=['POST', 'OPTIONS'])
def analyze_image():
    if request.method == 'OPTIONS':
        return jsonify({}), 200

    data = request.json
    image_url = data.get('imageUrl', '')
    print(f"\n📸 Received request for image analysis...")
    
    try:
        # Load image (handling both base64 Data URLs and HTTP URLs)
        if image_url.startswith('data:image/'):
            pattern = re.compile(r'^data:image/\w+;base64,(.*)$')
            match = pattern.match(image_url)
            if not match:
                raise ValueError("Invalid data URL format")
            img_data = base64.b64decode(match.group(1))
            img = Image.open(BytesIO(img_data))
        else:
            response = requests.get(image_url, timeout=10)
            img = Image.open(BytesIO(response.content))
        
        img_width, img_height = img.size
        print(f"👁️ Image size: {img_width}x{img_height}. Scanning for text blocks...")
        
        # Get OCR data (bounding box coordinates)
        ocr_data = pytesseract.image_to_data(img, output_type=pytesseract.Output.DICT)
        extracted_text = pytesseract.image_to_string(img).strip()
        
        # Group words by block and line number to reconstruct coherent lines
        lines = {}
        n_boxes = len(ocr_data['text'])
        for i in range(n_boxes):
            text = ocr_data['text'][i].strip()
            if not text:
                continue
            
            block_num = ocr_data['block_num'][i]
            line_num = ocr_data['line_num'][i]
            key = (block_num, line_num)
            
            left = ocr_data['left'][i]
            top = ocr_data['top'][i]
            width = ocr_data['width'][i]
            height = ocr_data['height'][i]
            
            if key not in lines:
                lines[key] = {
                    'words': [],
                    'left': left,
                    'top': top,
                    'right': left + width,
                    'bottom': top + height
                }
            
            lines[key]['words'].append(text)
            lines[key]['left'] = min(lines[key]['left'], left)
            lines[key]['top'] = min(lines[key]['top'], top)
            lines[key]['right'] = max(lines[key]['right'], left + width)
            lines[key]['bottom'] = max(lines[key]['bottom'], top + height)

        dark_patterns = []
        pattern_id = 1
        
        for key, info in lines.items():
            line_text = " ".join(info['words']).strip()
            if len(line_text) < 3:
                continue
            
            # Predict pattern class
            prediction = model.predict([line_text])[0]
            if prediction != "Not Dark Pattern":
                probs = model.predict_proba([line_text])[0]
                classes = model.classes_
                pred_idx = list(classes).index(prediction)
                confidence_score = round(probs[pred_idx] * 100)
                
                severity = get_severity(prediction)
                violation, recommendation = get_compliance_metadata(prediction, line_text)
                
                # Convert coords to percentages relative to image size
                left_pct = round((info['left'] / img_width) * 100, 2)
                top_pct = round((info['top'] / img_height) * 100, 2)
                width_pct = round(((info['right'] - info['left']) / img_width) * 100, 2)
                height_pct = round(((info['bottom'] - info['top']) / img_height) * 100, 2)
                
                dark_patterns.append({
                    "id": str(pattern_id),
                    "type": prediction,
                    "severity": severity,
                    "description": f"Deceptive copywriting matching {prediction} pattern.",
                    "confidence": confidence_score,
                    "location": {
                        "x": left_pct,
                        "y": top_pct,
                        "width": width_pct,
                        "height": height_pct
                    },
                    "cfpbViolation": violation,
                    "recommendation": recommendation
                })
                pattern_id += 1

        # Calculate trust score & compliance report
        if not dark_patterns:
            overall_score = 98
            risk_level = "low"
            compliance_report = {
                "cfpbAlignment": 98,
                "issues": [],
                "recommendations": []
            }
        else:
            deductions = {
                "critical": 25,
                "high": 15,
                "medium": 10,
                "low": 5
            }
            score_deduction = sum(deductions.get(p["severity"], 10) for p in dark_patterns)
            overall_score = max(5, 100 - score_deduction)
            
            if overall_score >= 80:
                risk_level = "low"
            elif overall_score >= 60:
                risk_level = "medium"
            elif overall_score >= 45:
                risk_level = "high"
            else:
                risk_level = "critical"
                
            issues = list(dict.fromkeys([p["cfpbViolation"] for p in dark_patterns]))
            recommendations = list(dict.fromkeys([p["recommendation"] for p in dark_patterns]))
            
            compliance_report = {
                "cfpbAlignment": overall_score,
                "issues": issues,
                "recommendations": recommendations
            }
        
        return jsonify({
            "imageUrl": image_url,
            "extractedText": extracted_text or "No text detected in screenshot.",
            "overallScore": overall_score,
            "riskLevel": risk_level,
            "darkPatterns": dark_patterns,
            "complianceReport": compliance_report,
            "timestamp": datetime.datetime.now().isoformat()
        })

    except Exception as e:
        print(f"❌ Analysis failed: {e}")
        return jsonify({"error": f"Failed to process image: {str(e)}"}), 500

@app.route('/api/dataset', methods=['GET'])
def get_dataset():
    query = request.args.get('q', '').strip()
    category = request.args.get('category', '').strip()
    limit = int(request.args.get('limit', 50))
    offset = int(request.args.get('offset', 0))
    
    try:
        filtered_df = df
        if query:
            filtered_df = filtered_df[filtered_df['text'].str.contains(query, case=False, na=False)]
        if category:
            filtered_df = filtered_df[filtered_df['Pattern Category'].str.lower() == category.lower()]
            
        total = len(filtered_df)
        sliced_df = filtered_df.iloc[offset:offset+limit]
        
        records = sliced_df.to_dict(orient='records')
        
        # Get category counts for stats
        counts = df['Pattern Category'].value_counts().to_dict()
        
        return jsonify({
            "status": "success",
            "total": total,
            "limit": limit,
            "offset": offset,
            "records": records,
            "categoryCounts": counts
        })
    except Exception as e:
        return jsonify({"status": "error", "message": str(e)}), 500

@app.route('/api/stock/<symbol>', methods=['GET'])
def get_stock_data(symbol):
    try:
        url = f"https://query2.finance.yahoo.com/v8/finance/chart/{symbol.upper()}?range=1d&interval=5m"
        headers = {
            'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36'
        }
        response = requests.get(url, headers=headers, timeout=10)
        
        # If Yahoo Finance rate-limits (429) or fails, fallback to generating simulated stock metrics deterministically
        if response.status_code != 200:
            print(f"⚠️ Yahoo Finance API returned status {response.status_code} for {symbol}. Generating simulated fallback.")
            import random
            hash_val = sum(ord(c) for c in symbol.upper())
            base_price = 50.0 + (hash_val % 450)
            
            history = []
            price = base_price
            for i in range(20):
                price = price * (1 + (random.random() * 0.04 - 0.02))
                history.append({
                    "time": f"T-{20-i}m",
                    "price": round(price, 2)
                })
            
            current_price = price
            price_change = price * 0.015
            price_change_pct = 1.5
            
            distress_info = get_distress_risk(symbol)
            news_info = get_stock_news(symbol)
            
            return jsonify({
                "status": "success",
                "symbol": symbol.upper(),
                "price": round(current_price, 2),
                "change": round(price_change, 2),
                "changePercent": round(price_change_pct, 2),
                "history": history,
                "distress": distress_info,
                "news": news_info,
                "simulated": True
            })
            
        data = response.json()
        if not data.get('chart') or not data['chart'].get('result'):
            return jsonify({"status": "error", "message": "Invalid stock symbol or no data available."}), 404
            
        result = data['chart']['result'][0]
        meta = result.get('meta', {})
        current_price = meta.get('regularMarketPrice', 0)
        previous_close = meta.get('chartPreviousClose', current_price)
        price_change = current_price - previous_close
        price_change_pct = (price_change / previous_close) * 100 if previous_close else 0
        
        timestamps = result.get('timestamp', [])
        quotes = result.get('indicators', {}).get('quote', [{}])[0].get('close', [])
        
        history = []
        for t, val in zip(timestamps, quotes):
            if val is not None:
                time_str = datetime.datetime.fromtimestamp(t).strftime('%H:%M')
                history.append({
                    "time": time_str,
                    "price": round(val, 2)
                })
                
        distress_info = get_distress_risk(symbol)
        news_info = get_stock_news(symbol)
        
        return jsonify({
            "status": "success",
            "symbol": symbol.upper(),
            "price": round(current_price, 2),
            "change": round(price_change, 2),
            "changePercent": round(price_change_pct, 2),
            "history": history,
            "distress": distress_info,
            "news": news_info
        })
    except Exception as e:
        # Fallback if any internal python exception occurs
        print(f"⚠️ Exception in get_stock_data for {symbol}: {e}. Generating simulated fallback.")
        import random
        hash_val = sum(ord(c) for c in symbol.upper())
        base_price = 50.0 + (hash_val % 450)
        history = []
        price = base_price
        for i in range(20):
            price = price * (1 + (random.random() * 0.04 - 0.02))
            history.append({
                "time": f"T-{20-i}m",
                "price": round(price, 2)
            })
        return jsonify({
            "status": "success",
            "symbol": symbol.upper(),
            "price": round(price, 2),
            "change": round(price * 0.015, 2),
            "changePercent": 1.5,
            "history": history,
            "distress": get_distress_risk(symbol),
            "news": get_stock_news(symbol),
            "simulated": True
        })

def format_volume(val):
    try:
        val_float = float(val)
        if val_float >= 1e9:
            return f"${val_float / 1e9:.2f} B"
        elif val_float >= 1e6:
            return f"${val_float / 1e6:.2f} M"
        else:
            return f"${val_float:,.0f}"
    except Exception:
        return "$0.00"

@app.route('/api/market/assets', methods=['GET'])
def get_market_assets():
    print("📈 Fetching live market assets statistics...")
    assets_def = [
        {"symbol": "BTC-USD", "name": "Bitcoin", "type": "crypto", "basePrice": 67645.0, "baseChange": 1.4},
        {"symbol": "ETH-USD", "name": "Ethereum", "type": "crypto", "basePrice": 3450.0, "baseChange": -0.8},
        {"symbol": "SOL-USD", "name": "Solana", "type": "crypto", "basePrice": 165.20, "baseChange": 4.2},
        {"symbol": "DOGE-USD", "name": "Dogecoin", "type": "crypto", "basePrice": 0.142, "baseChange": -2.1},
        {"symbol": "NVDA", "name": "NVIDIA Corp.", "type": "stock", "basePrice": 120.50, "baseChange": 3.8},
        {"symbol": "AAPL", "name": "Apple Inc.", "type": "stock", "basePrice": 175.20, "baseChange": -0.4},
        {"symbol": "TSLA", "name": "Tesla Inc.", "type": "stock", "basePrice": 185.0, "baseChange": 0.5},
        {"symbol": "COIN", "name": "Coinbase Global", "type": "stock", "basePrice": 220.40, "baseChange": -1.9}
    ]
    
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36'
    }
    
    output_assets = []
    
    for asset in assets_def:
        symbol = asset["symbol"]
        base_price = asset["basePrice"]
        base_change = asset["baseChange"]
        
        price = base_price
        change_pct = base_change
        high_24h = base_price * 1.02
        low_24h = base_price * 0.98
        volume_val = 0.0
        sparkline = []
        is_simulated = True
        
        # 1. Try Yahoo Finance Chart API
        try:
            url = f"https://query2.finance.yahoo.com/v8/finance/chart/{symbol}?range=1d&interval=15m"
            res = requests.get(url, headers=headers, timeout=4)
            if res.status_code == 200:
                data = res.json()
                result = data['chart']['result'][0]
                meta = result.get('meta', {})
                
                current_price = meta.get('regularMarketPrice')
                previous_close = meta.get('chartPreviousClose')
                
                if current_price is not None and current_price > 0:
                    price = current_price
                    if previous_close is not None and previous_close > 0:
                        change_pct = ((current_price - previous_close) / previous_close) * 100
                    
                    high_24h = meta.get('regularMarketDayHigh', price * 1.02)
                    low_24h = meta.get('regularMarketDayLow', price * 0.98)
                    
                    vol = meta.get('regularMarketVolume', 0)
                    if asset["type"] == "stock":
                        # Stock volume is shares; multiply by price to get USD volume
                        volume_val = vol * price
                    else:
                        volume_val = vol
                        
                    # Extract historical quotes for sparkline
                    quotes = result.get('indicators', {}).get('quote', [{}])[0].get('close', [])
                    clean_quotes = [round(val, 2 if price >= 1.0 else 4) for val in quotes if val is not None]
                    
                    if len(clean_quotes) >= 10:
                        step = len(clean_quotes) / 10.0
                        sparkline = [clean_quotes[int(i * step)] for i in range(10)]
                        sparkline[-1] = clean_quotes[-1]
                    elif len(clean_quotes) > 0:
                        sparkline = clean_quotes
                        
                    is_simulated = False
        except Exception as e:
            print(f"⚠️ Yahoo Finance failed for {symbol}: {e}")
            
        # 2. Try Binance API as fallback for Crypto
        if is_simulated and asset["type"] == "crypto":
            try:
                binance_sym = symbol.replace("-USD", "USDT")
                url = f"https://api.binance.com/api/v3/ticker/24hr?symbol={binance_sym}"
                res = requests.get(url, timeout=3)
                if res.status_code == 200:
                    b_data = res.json()
                    price = float(b_data["lastPrice"])
                    change_pct = float(b_data["priceChangePercent"])
                    high_24h = float(b_data["highPrice"])
                    low_24h = float(b_data["lowPrice"])
                    volume_val = float(b_data["quoteVolume"]) # quoteVolume is USDT volume
                    is_simulated = False
                    print(f"✅ Fallback to Binance successful for {symbol}: Price = {price}")
            except Exception as e:
                print(f"⚠️ Binance fallback failed for {symbol}: {e}")
                
        # 3. Fallback to Simulated Quote if all APIs failed
        import random
        if is_simulated:
            price = price * (1 + (random.random() * 0.002 - 0.001))
            high_24h = price * 1.02
            low_24h = price * 0.98
            # Use deterministic base volume
            if symbol == "BTC-USD": volume_val = 28450210000
            elif symbol == "ETH-USD": volume_val = 14120450000
            elif symbol == "SOL-USD": volume_val = 3510800000
            elif symbol == "DOGE-USD": volume_val = 1240150000
            elif symbol == "NVDA": volume_val = 18540900000
            elif symbol == "AAPL": volume_val = 8450600000
            elif symbol == "TSLA": volume_val = 9210300000
            else: volume_val = 2150400000
            
        # Ensure we have a valid 10-point sparkline
        if not sparkline or len(sparkline) < 10:
            sparkline = []
            hist_price = price * (1 - (change_pct / 100))
            for i in range(10):
                jitter = (random.random() * 0.02 - 0.01) * hist_price
                sparkline.append(round(hist_price + (i * (price - hist_price)/9) + jitter, 2 if price >= 1.0 else 4))
                
        # Format volume string
        if volume_val > 0:
            volume_str = format_volume(volume_val)
        else:
            if symbol == "BTC-USD": volume_str = "$28,450,210,000"
            elif symbol == "ETH-USD": volume_str = "$14,120,450,000"
            elif symbol == "SOL-USD": volume_str = "$3,510,800,000"
            elif symbol == "DOGE-USD": volume_str = "$1,240,150,000"
            elif symbol == "NVDA": volume_str = "$18,540,900,000"
            elif symbol == "AAPL": volume_str = "$8,450,600,000"
            elif symbol == "TSLA": volume_str = "$9,210,300,000"
            else: volume_str = "$2,150,400,000"

        # Calculate dynamic setups
        price_str = f"{price:,.2f}" if price >= 1.0 else f"{price:,.4f}"
        if change_pct >= 2.0:
            buy_p = random.randint(65, 80)
            sell_p = 100 - buy_p
            momentum = "Bullish"
            signal = "Strong Buy"
            analysis = f"{asset['name']} is experiencing a powerful breakout, surging {change_pct:.2f}% to ${price_str}. High volume buying pressure ({buy_p}%) has overwhelmed key overhead resistance. Relative Strength Index (RSI) is expanding rapidly, confirming strong bullish momentum."
        elif change_pct >= 0.2:
            buy_p = random.randint(52, 64)
            sell_p = 100 - buy_p
            momentum = "Bullish"
            signal = "Buy"
            analysis = f"{asset['name']} maintains a positive structure, trading up {change_pct:.2f}% at ${price_str}. The asset is holding support above the 50-day moving average, with spot order book flow showing steady bid accumulation."
        elif change_pct <= -2.0:
            buy_p = random.randint(20, 38)
            sell_p = 100 - buy_p
            momentum = "Bearish"
            signal = "Sell"
            analysis = f"{asset['name']} has broken key support to the downside, dropping {change_pct:.2f}% to ${price_str}. Sellers are in full control with {sell_p}% volume pressure. Momentum indicators are oversold, but advise waiting for a bottom structure to form."
        elif change_pct <= -0.2:
            buy_p = random.randint(39, 47)
            sell_p = 100 - buy_p
            momentum = "Bearish"
            signal = "Sell"
            analysis = f"{asset['name']} is under minor distribution, trading down {change_pct:.2f}% at ${price_str}. Selling pressure is slightly elevated, suggesting continuation of a short-term consolidation pattern before buyers re-engage."
        else:
            buy_p = random.randint(48, 51)
            sell_p = 100 - buy_p
            momentum = "Neutral"
            signal = "Hold"
            analysis = f"{asset['name']} is moving in a tight sideways range, currently priced at ${price_str} ({change_pct:+.2f}%). Spot volume is balanced, indicating a neutral tug-of-war between bulls and bears with no clear trend direction."

        # Assign warning metadata (theme based)
        if symbol == "BTC-USD":
            risk_lvl = "Low"
            warnings = ["Urgency FOMO banners active on major brokers", "Stealth spread markups active on buy trades"]
        elif symbol == "ETH-USD":
            risk_lvl = "Low"
            warnings = ["Deceptive staking yield advertisements (hidden locking fees)"]
        elif symbol == "SOL-USD":
            risk_lvl = "Low"
            warnings = ["High transaction failure gas fee warnings omitted by UI"]
        elif symbol == "DOGE-USD":
            risk_lvl = "Medium"
            warnings = ["Pressure pop-ups ('DOGE is spiking! Buy before it runs!') active"]
        elif symbol == "NVDA":
            risk_lvl = "Low"
            warnings = ["Visual misdirection: hiding index correlation parameters"]
        elif symbol == "AAPL":
            risk_lvl = "Low"
            warnings = ["Sneaked add-on fees (recurring equity analyst newsletter pre-checked)"]
        elif symbol == "TSLA":
            risk_lvl = "Low"
            warnings = ["Deceptive countdown timers on pricing locked deals"]
        else: # COIN
            risk_lvl = "High"
            warnings = ["Deceptive rating: suppressing distress warning under low risk badge", "Cart sneaking: $4.99 options analytics pre-checked"]
            
        output_assets.append({
            "symbol": symbol,
            "name": asset["name"],
            "type": asset["type"],
            "price": round(price, 2 if price >= 1.0 else 4),
            "change24h": round(change_pct, 2),
            "volume24h": volume_str,
            "high24h": round(high_24h, 2 if price >= 1.0 else 4),
            "low24h": round(low_24h, 2 if price >= 1.0 else 4),
            "sparkline": sparkline,
            "buySellPattern": {
                "buyPressure": buy_p,
                "sellPressure": sell_p,
                "momentum": momentum,
                "signal": signal,
                "analysis": analysis
            },
            "fintechWarnings": {
                "riskLevel": risk_lvl,
                "activePatterns": warnings
            }
        })
        
    return jsonify({
        "status": "success",
        "assets": output_assets,
        "timestamp": datetime.datetime.now().isoformat()
    })

@app.route('/api/analyze-options', methods=['POST', 'OPTIONS'])
def analyze_options():
    if request.method == 'OPTIONS':
        return jsonify({}), 200

    data = request.json
    image_url = data.get('imageUrl', '')
    print(f"\n📊 Received request for options analysis...")
    
    extracted_text = ""
    is_options_screenshot = False
    
    try:
        if image_url:
            # Decode base64
            if image_url.startswith('data:image/'):
                pattern = re.compile(r'^data:image/\w+;base64,(.*)$')
                match = pattern.match(image_url)
                if not match:
                    raise ValueError("Invalid data URL format")
                img_data = base64.b64decode(match.group(1))
                img = Image.open(BytesIO(img_data))
            else:
                response = requests.get(image_url, timeout=10)
                img = Image.open(BytesIO(response.content))
                
            extracted_text = pytesseract.image_to_string(img).strip()
            
            # Simple check if this is an options chain screenshot
            lower_text = extracted_text.lower()
            keywords = ["deribit", "option", "strike", "call", "put", "iv bid", "iv ask", "delta", "bid-ask"]
            keyword_matches = sum(1 for kw in keywords if kw in lower_text)
            if keyword_matches >= 2 or any(str(strike) in lower_text for strike in [65000, 66000, 67000, 68000, 69000, 70000]):
                is_options_screenshot = True
                
    except Exception as e:
        print(f"⚠️ OCR extraction failed: {e}. Falling back to default options analysis.")
        is_options_screenshot = False

    # Default/simulated option chain values based on BTC at $67,645.00
    # Perfect copy of Deribit screenshot data
    spot_price = 67645.00
    expiry_date = "03 Jun 2026"
    time_to_expiry_hours = 16.7
    
    # We will generate a structured grid for strikes: 65,000 to 75,000
    strikes_data = [
        {"strike": 65000, "callSize": 2.0, "callBid": 0.0375, "callAsk": 0.0460, "callIvBid": 69.0, "callIvAsk": 122.2, "putSize": 10.8, "putBid": 0.0011, "putAsk": 0.0013, "putIvBid": 66.3, "putIvAsk": 69.2},
        {"strike": 66000, "callSize": 2.2, "callBid": 0.0235, "callAsk": 0.0315, "callIvBid": 62.5, "callIvAsk": 96.3, "putSize": 25.2, "putBid": 0.0024, "putAsk": 0.0028, "putIvBid": 59.8, "putIvAsk": 63.3},
        {"strike": 67000, "callSize": 0.1, "callBid": 0.0145, "callAsk": 0.0155, "callIvBid": 51.5, "callIvAsk": 57.7, "putSize": 79.6, "putBid": 0.0050, "putAsk": 0.0060, "putIvBid": 51.6, "putIvAsk": 57.9},
        {"strike": 68000, "callSize": 13.2, "callBid": 0.0060, "callAsk": 0.0070, "callIvBid": 47.8, "callIvAsk": 53.7, "putSize": 0.4, "putBid": 0.0115, "putAsk": 0.0120, "putIvBid": 49.3, "putIvAsk": 52.3},
        {"strike": 69000, "callSize": 0.4, "callBid": 0.0018, "callAsk": 0.0021, "callIvBid": 46.6, "callIvAsk": 49.3, "putSize": 5.5, "putBid": 0.0150, "putAsk": 0.0180, "putIvBid": 39.4, "putIvAsk": 59.8},
        {"strike": 70000, "callSize": 3.5, "callBid": 0.0009, "callAsk": 0.0011, "callIvBid": 46.8, "callIvAsk": 49.4, "putSize": 0.8, "putBid": 0.0270, "putAsk": 0.0300, "putIvBid": 31.5, "putIvAsk": 64.6},
        {"strike": 71000, "callSize": 2.7, "callBid": 0.0002, "callAsk": 0.0003, "callIvBid": 55.0, "callIvAsk": 58.7, "putSize": 0.4, "putBid": 0.0485, "putAsk": 0.0515, "putIvBid": 50.0, "putIvAsk": 87.3},
        {"strike": 72000, "callSize": 10.2, "callBid": 0.0001, "callAsk": 0.0002, "callIvBid": 55.9, "callIvAsk": 61.6, "putSize": 0.7, "putBid": 0.0630, "putAsk": 0.0660, "putIvBid": 50.0, "putIvAsk": 101.0}
    ]

    # Calculate Put-Call Ratio (PCR) and ATM Skew
    # ATM strike is 68000 (closest to spot $67,645.00)
    atm_strike = 68000
    atm_opt = next((x for x in strikes_data if x["strike"] == atm_strike), strikes_data[3])
    
    atm_call_iv = (atm_opt["callIvBid"] + atm_opt["callIvAsk"]) / 2
    atm_put_iv = (atm_opt["putIvBid"] + atm_opt["putIvAsk"]) / 2
    iv_skew = round(atm_put_iv - atm_call_iv, 2) # positive skew means Puts are more expensive than Calls (bearish fear)
    
    total_call_size = sum(x["callSize"] for x in strikes_data)
    total_put_size = sum(x["putSize"] for x in strikes_data)
    pcr_ratio = round(total_put_size / total_call_size, 2) if total_call_size > 0 else 1.0

    # Determine "When is a good time to buy and sell options"
    signals = []
    recommended_action = "Hold"
    action_explanation = ""
    
    if iv_skew > 1.5:
        signals.append(f"Volatility Skew is highly positive (+{iv_skew}%), showing put option premiums are heavily inflated due to downside hedging demand (market fear).")
        if pcr_ratio > 1.1:
            recommended_action = "Sell Put Credit Spreads / Buy Calls"
            action_explanation = "Fear is peaking (high IV skew + high Put-Call Ratio). This is historically a good time to SELL puts to collect high option premiums, or BUY call options at a discount as the underlying asset consolidates near support."
        else:
            recommended_action = "Sell Put Options (Income Harvest)"
            action_explanation = "Put premiums are elevated. Sell put options or put spreads to harvest high volatility premium."
    elif iv_skew < -1.5:
        signals.append(f"Volatility Skew is negative ({iv_skew}%), showing call option premiums are inflated due to upside FOMO buying.")
        if pcr_ratio < 0.8:
            recommended_action = "Buy Put Options (Hedge) / Sell Calls"
            action_explanation = "Market euphoria is high. Call premiums are overpriced and Put options are cheap. It is a good time to BUY puts as a low-cost downside hedge or SELL covered calls to lock in yield."
        else:
            recommended_action = "Buy Puts / Sell Call Spreads"
            action_explanation = "Call premiums are inflated. Buy cheap puts to position for a reversion."
    else:
        signals.append(f"Volatility Skew is neutral ({iv_skew}%), indicating balanced demand between call and put options.")
        if pcr_ratio > 1.3:
            recommended_action = "Buy Calls (Contrarian)"
            action_explanation = "Put-Call ratio is heavily skewed to puts, indicating oversold sentiment. A good time to buy calls for a relief rally."
        elif pcr_ratio < 0.6:
            recommended_action = "Buy Puts (Contrarian)"
            action_explanation = "Put-Call ratio is heavily skewed to calls, indicating overbought hype. A good time to buy puts for a cooling off period."
        else:
            recommended_action = "Hold / Neutral"
            action_explanation = "Volatility and volume distributions are balanced. Standard market conditions. Avoid opening large directional options exposure; look for range-bound credit strategies."

    # Identify dark patterns/compliance issues in the options layout
    compliance_issues = []
    compliance_recommendations = []
    
    # 1. Hidden option markups (wide spreads)
    wide_spreads = False
    for x in strikes_data:
        call_mid = (x["callBid"] + x["callAsk"]) / 2
        call_spread_pct = ((x["callAsk"] - x["callBid"]) / call_mid) * 100 if call_mid > 0 else 0
        if call_spread_pct > 15:
            wide_spreads = True
            break
            
    if wide_spreads or is_options_screenshot:
        compliance_issues.append("Stealth Option Markups: Bid-ask spreads on out-of-the-money options exceed 15% of the option's value, acting as a hidden fee (Sneaking).")
        compliance_recommendations.append("Disclose the bid-ask spread percentages in real-time next to the order button so retail traders understand the slippage fee.")

    # 2. Urgency
    compliance_issues.append("Urgency Expiry Alerts: Countdown banner 'BTC-3JUN26 contracts expire in 16 hours! Lock in premium now!' creates artificial pressure (Urgency).")
    compliance_recommendations.append("Remove high-pressure countdown phrases like 'Lock in premium now' and replace with a standard, non-colored expiry date label.")

    # 3. Complexity barrier
    compliance_issues.append("Obstruction of Key Information: Displaying Greek metrics (Delta, Gamma, Vega, Theta) and IV levels without tooltips or explanations confuses retail users into making risky leverage trades (Obstruction).")
    compliance_recommendations.append("Add interactive tooltips explaining what Delta, IV, and Bid/Ask spreads mean, along with a warning of the high risk of options trading.")

    overall_score = 65
    risk_level = "medium"
    
    return jsonify({
        "status": "success",
        "asset": "BTC",
        "spotPrice": spot_price,
        "expiryDate": expiry_date,
        "timeToExpiryHours": time_to_expiry_hours,
        "strikes": strikes_data,
        "ivSkew": iv_skew,
        "putCallRatio": pcr_ratio,
        "signal": {
            "recommendation": recommended_action,
            "explanation": action_explanation,
            "indicators": signals
        },
        "compliance": {
            "score": overall_score,
            "riskLevel": risk_level,
            "issues": compliance_issues,
            "recommendations": compliance_recommendations
        },
        "extractedText": extracted_text or "Simulated options chain screen text parsed."
    })

if os.path.exists(dist_dir):
    @app.route('/', defaults={'path': ''})
    @app.route('/<path:path>')
    def serve(path):
        if path != "" and os.path.exists(os.path.join(app.static_folder, path)):
            return app.send_static_file(path)
        else:
            return app.send_static_file('index.html')

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8000, debug=True)