new

backend/app.py

@@ -1,152 +1,23 @@
-from flask import Flask, jsonify, request, send_file
-from io import BytesIO
-import yfinance as yf
-import pandas as pd
-import numpy as np
-import plotly.express as px
+from flask import Flask
 from flask_cors import CORS
-import json
-import traceback
-import plotly
-import requests
-from tensorflow.keras.models import load_model
-from sklearn.preprocessing import MinMaxScaler
-from datetime import datetime, timedelta
-from sklearn.linear_model import LinearRegression
 import os
 from routes.stock_routes import stock_routes
 from dotenv import load_dotenv
 
+# Load env
 load_dotenv()
 
 app = Flask(__name__)
 CORS(app, resources={r"/*": {"origins": ["http://localhost:3000", "https://aistockanalyzer.onrender.com"]}})
 
+# Home route:
 @app.route('/')
 def home():
     return "Welcome to the Stock Analysis API"
 
-
-@app.route('/api/export/history', methods=['GET'])
-def export_stock_history():
-    ticker = request.args.get('ticker')
-    period = request.args.get('period', '1mo')
-    filetype = request.args.get('type', 'csv')
-
-    if not ticker:
-        return jsonify({'error': 'Ticker is required'}), 400
-
-    data = fetch_stock_data(ticker, period)
-    if isinstance(data, str):
-        return jsonify({'error': data}), 500
-
-    data = clean_stock_data(data)
-
-    file_io = BytesIO()
-    if filetype == 'excel':
-        data.to_excel(file_io, index=False, engine='openpyxl')
-        mimetype = 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
-        ext = 'xlsx'
-    else:
-        data.to_csv(file_io, index=False)
-        mimetype = 'text/csv'
-        ext = 'csv'
-
-    file_io.seek(0)
-    return send_file(file_io, mimetype=mimetype, as_attachment=True, download_name=f"{ticker}_history.{ext}")
-
-
-@app.route('/api/export/prediction', methods=['GET'])
-def export_predictions():
-    ticker = request.args.get('ticker')
-    if not ticker:
-        return jsonify({'error': 'Ticker is required'}), 400
-
-    try:
-        data = yf.download(ticker, period='5y')
-        if data.empty:
-            return jsonify({'error': 'No data found'}), 404
-
-        data['Date'] = data.index
-        data['Date'] = data['Date'].map(datetime.toordinal)
-        X = data['Date'].values.reshape(-1, 1)
-        y = data['Close'].values.flatten()
-
-        model = LinearRegression()
-        model.fit(X, y)
-
-        future_dates = [datetime.now() + timedelta(days=i*365) for i in range(1, 11)]
-        predictions = model.predict(np.array([d.toordinal() for d in future_dates]).reshape(-1, 1))
-
-        df_pred = pd.DataFrame({
-            'Date': [d.strftime('%Y-%m-%d') for d in future_dates],
-            'Predicted Price': [round(float(p), 2) for p in predictions]
-        })
-
-        file_io = BytesIO()
-        filetype = request.args.get('type', 'csv')
-        if filetype == 'excel':
-            df_pred.to_excel(file_io, index=False, engine='openpyxl')
-            mimetype = 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
-            ext = 'xlsx'
-        else:
-            df_pred.to_csv(file_io, index=False)
-            mimetype = 'text/csv'
-            ext = 'csv'
-
-        file_io.seek(0)
-        return send_file(file_io, mimetype=mimetype, as_attachment=True, download_name=f"{ticker}_prediction.{ext}")
-
-    except Exception as e:
-        return jsonify({'error': str(e)}), 500
-
-
-@app.route('/api/stock/predict', methods=['GET', 'OPTIONS'])
-def predict_stock():
-    ticker = request.args.get('ticker')
-    if not ticker:
-        return jsonify({'error': 'Ticker symbol is required'}), 400
-
-    try:
-        data = yf.download(ticker, period='5y')
-        if data.empty:
-            return jsonify({'error': 'No data found for the given ticker'}), 404
-
-        data['Date'] = data.index
-        data['Date'] = data['Date'].map(datetime.toordinal)
-        X = data['Date'].values.reshape(-1, 1)
-        y = data['Close'].values.flatten()
-
-        model = LinearRegression()
-        model.fit(X, y)
-
-        future_dates = [datetime.now() + timedelta(days=i*365) for i in range(1, 11)]
-        predictions = model.predict(np.array([d.toordinal() for d in future_dates]).reshape(-1, 1))
-
-        stocks = [10, 20, 50, 100]
-        returns = []
-        current_price = float(y[-1])
-
-        for stock in stocks:
-            returns.append({
-                'stocks_bought': stock,
-                'current_price': round(current_price * stock, 2),
-                'after_1_year': round(float(predictions[0]) * stock, 0),
-                'after_5_years': round(float(predictions[4]) * stock, 0),
-                'after_10_years': round(float(predictions[9]) * stock, 0)
-            })
-
-        return jsonify({
-            "future_predictions": [round(float(p), 2) for p in predictions],
-            "returns": returns
-        })
-
-    except Exception as e:
-        return jsonify({'error': str(e)}), 500
-
-
+# Register blueprint
 app.register_blueprint(stock_routes, url_prefix="/api")
 
 if __name__ == '__main__':
     port = int(os.environ.get('PORT', 10000))
-    app.run(host='0.0.0.0', port=port)
+    app.run(host='0.0.0.0', port=port)

backend/requirements.txt

@@ -1,43 +1,41 @@
-Flask==3.1.1
-flask-cors==6.0.1
-gunicorn==23.0.0
-python-dotenv
-numpy==2.3.2
-pandas==2.3.1
-scikit-learn==1.7.1
-scipy==1.16.1
-tensorflow==2.17.0
-joblib==1.5.1
-threadpoolctl==3.6.0
-yfinance==0.2.65
-requests==2.32.4
 beautifulsoup4==4.13.4
-textblob
-plotly==6.2.0
-openpyxl
-frozendict==2.4.6
-typing_extensions==4.14.1
-packaging==25.0
+blinker==1.9.0
 certifi==2025.8.3
+cffi==1.17.1
 charset-normalizer==3.4.2
+click==8.2.1
+curl_cffi==0.12.0
+dotenv
+Flask==3.1.1
+flask-cors==6.0.1
+frozendict==2.4.6
 idna==3.10
-urllib3==2.5.0
-pytz==2025.2
-tzdata==2025.2
+itsdangerous==2.2.0
+Jinja2==3.1.6
+joblib==1.5.1
+MarkupSafe==3.0.2
 multitasking==0.0.12
 narwhals==2.0.1
+numpy==2.3.2
+packaging==25.0
+pandas==2.3.1
 peewee==3.18.2
 platformdirs==4.3.8
-blinker==1.9.0
-click==8.2.1
-itsdangerous==2.2.0
-Jinja2==3.1.6
-MarkupSafe==3.0.2
-Werkzeug==3.1.3
-redis>=5.0.0
-websockets==15.0.1
-soupsieve==2.7
-curl_cffi==0.12.0
+plotly==6.2.0
 protobuf==6.31.1
 pycparser==2.22
-six==1.17.0
+python-dateutil==2.9.0.post0
+pytz==2025.2
+requests==2.32.4
+scikit-learn==1.7.1
+scipy==1.16.1
+six==1.17.0
+soupsieve==2.7
+threadpoolctl==3.6.0
+typing_extensions==4.14.1
+tzdata==2025.2
+urllib3==2.5.0
+websockets==15.0.1
+Werkzeug==3.1.3
+yfinance==0.2.65
+textblob

src/components/StockList.jsx

@@ -7,7 +7,7 @@ import { auth } from "./firebase";
 import stockData from "./data/stockData.json";
 import BackToTopBtn from "./BackToTopBtn";
 import styles from "./StockList.module.css";
-import stockBg2 from "../Images/stock2.png";
+import stockBg2 from "./images/bg.png";
 
 const StocksList = () => {
   const [stocks, setStocks] = useState([]);
@@ -54,13 +54,13 @@ const StocksList = () => {
       animate={{ opacity: 1 }}
     >
       {/* 🚀 Hero Section */}
-  <section
+      <section
         className={styles.hero}
         style={{
           backgroundImage: `linear-gradient(135deg, rgba(33,84,214,0.63), rgba(29,30,41,0.46)), url(${stockBg2})`,
           backgroundSize: "contain", // CHANGED from 'cover' to 'contain'
           backgroundPosition: "center bottom", // Focus lower part (the phone/hand)
-          backgroundRepeat: "no-repeat"
+          backgroundRepeat: "no-repeat",
         }}
       >
         <h1>Welcome to Stock Analyzer!</h1>