fifa19_streamlit

app.py

@@ -121,30 +121,32 @@ def final_pred(num_of_players,b=[],c=[],d=[]):
 def main():
     st.title("FIFA 19 Player Recommender 🎮⚽")
     
-    # Add business context and description
-    st.markdown("""
-    ### About this App
-    This FIFA 19 Player Recommender helps football clubs and managers identify similar players and predict their market value. 
-    It's particularly useful for:
-    - Scouting potential replacements for current players
-    - Finding undervalued talents in the market
-    - Discovering players that match your team's playing style
-    - Making informed decisions about player acquisitions
+    # Create two columns - one for instructions and one for results
+    left_col, right_col = st.columns([1, 1])
     
-    ### How it Works
-    1. The app uses advanced machine learning to analyze player attributes and find similar players
-    2. It considers over 70 different player statistics and characteristics
-    3. Provides market value predictions to help with transfer budget planning
-    
-    ### How to Use
-    1. **Select Your Team** from the sidebar - this helps find players that would fit your team's style
-    2. **Choose Position** you're looking to fill
-    3. **Adjust Number of Recommendations** (1-10 players)
-    4. **Set Maximum Age** to focus on your preferred age range
-    5. Click "Get Recommendations" to see your matches!
-    
-    ---
-    """)
+    with left_col:
+        # Add business context and description
+        st.markdown("""
+        ### About this App
+        This FIFA 19 Player Recommender helps football clubs and managers identify similar players and predict their market value. 
+        It's particularly useful for:
+        - Scouting potential replacements for current players
+        - Finding undervalued talents in the market
+        - Discovering players that match your team's playing style
+        - Making informed decisions about player acquisitions
+        
+        ### How it Works
+        1. The app uses advanced machine learning to analyze player attributes and find similar players
+        2. It considers over 70 different player statistics and characteristics
+        3. Provides market value predictions to help with transfer budget planning
+        
+        ### How to Use
+        1. **Select Your Team** from the sidebar - this helps find players that would fit your team's style
+        2. **Choose Position** you're looking to fill
+        3. **Adjust Number of Recommendations** (1-10 players)
+        4. **Set Maximum Age** to focus on your preferred age range
+        5. Click "Get Recommendations" to see your matches!
+        """)
     
     # Sidebar inputs
     st.sidebar.header("Search Parameters")
@@ -159,22 +161,21 @@ def main():
     age_up = st.sidebar.slider("Maximum Age", 16, 45, 30)
     
     if st.sidebar.button("Get Recommendations"):
-        with st.spinner("Finding similar players..."):
-            recommendations = player_sim_team(team_chosen, postion_chosen, num_of_players, age_up)
-            
-            # Display results in a nice format
-            st.subheader(f"Recommended Players for {team_chosen} - {postion_chosen}")
-            
-            # Create columns for each player
-            cols = st.columns(min(3, len(recommendations)))
-            for idx, player in enumerate(recommendations):
-                col_idx = idx % 3
-                with cols[col_idx]:
-                    st.markdown(f"""
-                    #### {player['player_name']}
-                    **Estimated Value:** €{player['starting_bid']:,.2f}
-                    ---
-                    """)
+        with right_col:
+            with st.spinner("Finding similar players..."):
+                recommendations = player_sim_team(team_chosen, postion_chosen, num_of_players, age_up)
+                
+                # Display results in a nice format
+                st.subheader(f"Recommended Players for {team_chosen} - {postion_chosen}")
+                
+                # Create a container for recommendations
+                for player in recommendations:
+                    with st.container():
+                        st.markdown(f"""
+                        ### {player['player_name']}
+                        **Estimated Value:** €{player['starting_bid']:,.2f}
+                        ---
+                        """)
 
 if __name__ == '__main__':
     main()