Update app.py

app.py

@@ -2,7 +2,6 @@ import pandas as pd
 import streamlit as st
 import joblib
 import time
-import os
 import plotly.graph_objects as go
 import numpy as np
 from datetime import datetime
@@ -282,6 +281,7 @@ def stock_chart(data, company_name):
         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
 # =============================================
@@ -396,7 +396,6 @@ def main():
         """, unsafe_allow_html=True)
 
     st.markdown('<div class="divider"></div>', unsafe_allow_html=True)
-
     
     # Sidebar with dark teal background
     with st.sidebar:
@@ -471,14 +470,14 @@ def main():
         }
         LevelDescription = career_to_level_description.get(CareerLevel, [None])[0]
 
-        df_forecast=pd.read_excel('forecast.xlsx')
-        if  BusinessTitle in df_forecast["Business Titles"].values:
+        df_forecast = pd.read_excel('forecast.xlsx')
+        if BusinessTitle in df_forecast["Business Titles"].values:
             current_date = datetime.now()
             date = f'{current_date.month:02d}.2025'
             NumberOfPositions = df_forecast.loc[df_forecast["Business Titles"] == BusinessTitle, date].values[0]
         else:
             df = pd.read_csv('cleaned_job_salaries.csv')
-            NumberOfPositions=df.loc[df["Business Title"] == BusinessTitle]['# Of Positions'].mean()
+            NumberOfPositions = df.loc[df["Business Title"] == BusinessTitle]['# Of Positions'].mean()
 
         if st.button('Predict Salary', use_container_width=True):
             st.session_state.predict_clicked = True
@@ -516,13 +515,26 @@ def main():
         
         # Results section
         st.markdown("## Prediction Results")
-        prediction_card(
-            "Annual Salary", 
-            predicted_salary, 
-            BusinessTitle,
-            CareerLevel,
-            "💵"
-        )
+        
+        # Check if NumberOfPositions came from forecast and is not zero
+        show_positions = (BusinessTitle in df_forecast["Business Titles"].values) and (NumberOfPositions != 0)
+
+        if show_positions:
+            prediction_card(
+                "Annual Salary", 
+                predicted_salary, 
+                f"{BusinessTitle} | {NumberOfPositions:.0f} positions available",
+                CareerLevel,
+                "💵"
+            )
+        else:
+            prediction_card(
+                "Annual Salary", 
+                predicted_salary, 
+                BusinessTitle,
+                CareerLevel,
+                "💵"
+            )
         
         # Job details
         st.markdown("### Job Summary")
@@ -534,7 +546,46 @@ def main():
         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>
         .footer {
@@ -552,9 +603,7 @@ def main():
         <hr style="border: 0.5px solid #e2e8f0;">
         Created with ❤️ by Senasu & Sude
     </div>
-""", unsafe_allow_html=True)
-
-
+    """, unsafe_allow_html=True)
 
 if __name__ == '__main__':
     main()