Update app.py

app.py

@@ -2,13 +2,10 @@ import pandas as pd
 import streamlit as st
 import joblib
 import time
-import plotly.graph_objects as go
-import numpy as np
 from datetime import datetime
 from sklearn.pipeline import Pipeline
 from sklearn.compose import ColumnTransformer
 from sklearn.preprocessing import StandardScaler, OneHotEncoder
-from prophet import Prophet
 from PIL import Image, ImageFile
 ImageFile.LOAD_TRUNCATED_IMAGES = True  # Disables the check
 
@@ -200,180 +197,6 @@ def company_metric(label, value, change=None):
     </div>
     """, unsafe_allow_html=True)
 
-def stock_chart(data, company_name):
-    try:
-        # Debug: Print raw data
-        
-        # Ensure we have a DataFrame with proper datetime index
-        df_prophet = data.reset_index()
-        df_prophet.columns = ['ds', 'y']
-        
-        # Debug: Check date conversion
-        
-        
-        # Convert to datetime if not already
-        df_prophet['ds'] = pd.to_datetime(df_prophet['ds'], errors='coerce')
-        
-        # Drop any rows with invalid dates
-        df_prophet = df_prophet.dropna(subset=['ds'])
-        
-        # Debug: Check after datetime conversion
-        
-        
-        # Initialize and fit model
-        model_prophet = Prophet(daily_seasonality=True, yearly_seasonality=True)
-        model_prophet.fit(df_prophet)
-
-        # Create future dataframe
-        future = model_prophet.make_future_dataframe(periods=180)
-        
-        # Generate forecast
-        forecast_prophet = model_prophet.predict(future)
-        forecast_prophet['ds'] = pd.to_datetime(forecast_prophet['ds'])
-        
-        # Filter forecast to show only future predictions
-        forecast_prophet_filtered = forecast_prophet[forecast_prophet['ds'] >= pd.to_datetime('today').normalize()]
-        
-        # Create figure
-        fig = go.Figure()
-
-        # Add historical data trace
-        fig.add_trace(go.Scatter(
-            x=df_prophet['ds'],
-            y=df_prophet['y'],
-            name='Historical',
-            line=dict(color='#014d4f', width=3),
-            hovertemplate='Date: %{x|%b %d, %Y}<br>Price: %{y:$,.2f}<extra></extra>'
-        ))
-
-        # Add forecast trace
-        fig.add_trace(go.Scatter(
-            x=forecast_prophet_filtered['ds'],
-            y=forecast_prophet_filtered['yhat'],
-            name='Forecast',
-            line=dict(color='#017a7c', width=3, dash='dot'),
-            hovertemplate='Date: %{x|%b %d, %Y}<br>Forecast: %{y:$,.2f}<extra></extra>'
-        ))
-
-        # Update layout
-        fig.update_layout(
-            title=f"<b>{company_name}</b> Stock Performance",
-            xaxis_title=None,
-            yaxis_title="Stock Price",
-            hovermode="x unified",
-            template="plotly_white",
-            plot_bgcolor="white",
-            paper_bgcolor="white",
-            font=dict(color="#2b2d42"),
-            margin=dict(l=40, r=40, t=60, b=40),
-            legend=dict(
-                orientation="h",
-                yanchor="bottom",
-                y=1.02,
-                xanchor="right",
-                x=1
-            )
-        )
-
-        return fig
-
-    except Exception as e:
-        st.error(f"Error generating stock chart: {str(e)}")
-        st.write("Debug info - data received:", data)
-        return go.Figure()  # Return empty figure on error
-
-# =============================================
-# JOB POSITIONS DATA
-# =============================================
-
-job_positions = {
-    "Project Manager": {
-        1: "Aselsan",
-        2: "Turkcell İletişim Hizmetleri",
-        3: "Enka İnşaat ve Sanayi"
-    },
-    "Civil Engineer": {
-        1: "Enka İnşaat ve Sanayi",
-        2: "Tekfen Holding",
-        3: "Alarko Holding"
-    },
-    "Budget Analyst": {
-        1: "Koç Holding",
-        2: "Sabancı Holding",
-        3: "Türkiye İş Bankası"
-    },
-    "Data Scientist": {
-        1: "Turkcell İletişim Hizmetleri",
-        2: "Logo Yazılım Sanayi ve Ticaret",
-        3: "Türk Telekomünikasyon"
-    },
-    "Data Analyst": {
-        1: "Turkcell İletişim Hizmetleri",
-        2: "Türk Telekomünikasyon",
-        3: "Koç Holding"
-    },
-    "Design Engineer": {
-        1: "Aselsan",
-        2: "Arçelik",
-        3: "Enka İnşaat ve Sanayi"
-    },
-    "Project Engineer": {
-        1: "Enka İnşaat ve Sanayi",
-        2: "Tekfen Holding",
-        3: "Alarko Holding"
-    },
-    "Construction Engineer": {
-        1: "Enka İnşaat ve Sanayi",
-        2: "Tekfen Holding",
-        3: "Alarko Holding"
-    },
-    "Software Engineer": {
-        1: "Türk Telekomünikasyon",
-        2: "Netas Telekomünikasyon",
-        3: "Vestel"
-    },
-    "Environmental Engineer": {
-        1: "Şişecam",
-        2: "Tekfen Holding",
-        3: "Enka İnşaat ve Sanayi"
-    },
-    "Mechanical Engineer": {
-        1: "Ford Otosan",
-        2: "Tofaş Türk Otomobil Fabrikası",
-        3: "Arçelik"
-    },
-    "Energy": {
-        1: "Aksa Enerji Üretim",
-        2: "Enerjisa Enerji",
-        3: "Galata Wind Enerji"
-    },
-    "Project Coordinator": {
-        1: "Aselsan",
-        2: "Türk Hava Yolları",
-        3: "Tekfen Holding"
-    },
-    "Project Director": {
-        1: "Aselsan",
-        2: "Türk Telekomünikasyon",
-        3: "Tekfen Holding"
-    },
-    "Systems Administrator": {
-        1: "Türk Telekomünikasyon",
-        2: "Vestel",
-        3: "Aselsan"
-    },
-    "IT Support Specialist": {
-        1: "Türk Telekomünikasyon",
-        2: "Koç Holding",
-        3: "Vestel"
-    },
-    "Applications Developer": {
-        1: "Turkcell İletişim Hizmetleri",
-        2: "Logo Yazılım Sanayi ve Ticaret",
-        3: "Yapı Kredi Teknoloji"
-    }
-}
-
 # =============================================
 # MAIN APP
 # =============================================
@@ -545,46 +368,6 @@ def main():
             company_metric("Experience Required", f"{MinExperience} years")
         with col3:
             company_metric("Education Level", RequiredDegree)
-        
-        # Market analysis section
-        if BusinessTitle in job_positions:
-            st.markdown("## Market Intelligence")
-            st.markdown("""
-            <p style="color: #64748b;">
-                Below you'll find financial performance data for top companies hiring this position
-            </p>
-            """, unsafe_allow_html=True)
-            companies = job_positions[BusinessTitle]
-            for i in range(1, 4):  # Show first 3 companies (keys 1, 2, 3)
-                company_name = companies[i]
-                with st.expander(f"{company_name} Market Analysis", expanded=(i==1)):
-                    try:
-                        data = pd.read_csv(f'Finance Data/{company_name}.csv', index_col=0, header=[0, 1])
-                        close_data = data['Close'].copy()
-                        close_data = pd.DataFrame(close_data)
-                        close_data.index = pd.to_datetime(close_data.index)
-                        close_data.columns = ['Close']
-                        
-                        fig = stock_chart(close_data, company_name)
-                        st.plotly_chart(fig, use_container_width=True)
-                        
-                        # Company metrics
-                        current_price = close_data['Close'].iloc[-1]
-                        monthly_change = ((current_price - close_data['Close'].iloc[-30]) / close_data['Close'].iloc[-30]) * 100
-                        volatility = (close_data['Close'].std() / close_data['Close'].mean()) * 100
-                        
-                        cols = st.columns(3)
-                        with cols[0]:
-                            company_metric("Current Price", f"${current_price:.2f}")
-                        with cols[1]:
-                            company_metric("30-Day Change", f"{monthly_change:.2f}%", monthly_change)
-                        with cols[2]:
-                            company_metric("Volatility", f"{volatility:.2f}%")
-                            
-                    except FileNotFoundError:
-                        st.error(f"Data not available for {company_name}")
-                    except Exception as e:
-                        st.error(f"Error loading data for {company_name}: {str(e)}")
     
     st.markdown("""
     <style>