fifa19_streamlit

README.md

@@ -11,3 +11,21 @@ short_description: ML App
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# FIFA 19 Player Recommender
+
+A Streamlit web application that recommends FIFA 19 players based on team and position preferences, and predicts their market value.
+
+## Setup
+1. Install requirements: `pip install -r requirements.txt`
+2. Run the app: `streamlit run app.py`
+
+## Data Files
+Make sure all required pickle files are in the `data` directory:
+- newdf3.pkl
+- predictorsscale.pkl
+- newpredictors.pkl
+- train_predictors_val.pkl
+- newfifa.pkl
+- df3scaled.pkl
+- finalxbrmodel.pkl

app.py

@@ -0,0 +1,170 @@
+import numpy as np
+import pandas as pd
+import streamlit as st
+from sklearn.preprocessing import StandardScaler
+from sklearn.neighbors import NearestNeighbors
+import pickle
+
+# Set page config
+st.set_page_config(
+    page_title="FIFA 19 Player Recommender",
+    page_icon="⚽",
+    layout="wide"
+)
+
+# Load all pickle files
+@st.cache_resource
+def load_data():
+    try:
+        with open('newdf3.pkl', 'rb') as f:
+            df3 = pickle.load(f)
+        with open('predictorsscale.pkl', 'rb') as f:
+            predictors_scaled = pickle.load(f)
+        with open('newpredictors.pkl', 'rb') as f:
+            predictors_df = pickle.load(f)
+        with open('train_predictors_val.pkl', 'rb') as f:
+            train_predictors_val = pickle.load(f)
+        with open('newfifa.pkl', 'rb') as f:
+            fifa = pickle.load(f)
+        with open('df3scaled.pkl', 'rb') as f:
+            df3scaled = pickle.load(f)
+        with open('finalxbrmodel.pkl', 'rb') as f:
+            xbr = pickle.load(f)
+        return df3, predictors_scaled, predictors_df, train_predictors_val, fifa, df3scaled, xbr
+    except Exception as e:
+        st.error(f"Error loading data: {str(e)}")
+        raise e
+
+# Load data
+df3, predictors_scaled, predictors_df, train_predictors_val, fifa, df3scaled, xbr = load_data()
+predscale_target = predictors_scaled.columns.tolist()
+
+def player_sim_team(team, position, NUM_RECOM, AGE_upper_bound):
+# part 1(recommendation)
+    target_cols = predscale_target
+
+
+# team stats
+    team_stats = df3scaled.query('position_group == @position and Club == @team').head(3)[target_cols].mean(axis=0)
+    team_stats_np = team_stats.values
+
+# player stats by each position
+    ply_stats = df3scaled.query('position_group == @position and Club != @team and Age1 <= @AGE_upper_bound')[
+    ['ID'] + target_cols]
+    ply_stats_np = ply_stats[target_cols].values
+    X = np.vstack((team_stats_np, ply_stats_np))
+
+## KNN
+    nbrs = NearestNeighbors(n_neighbors=NUM_RECOM + 1, algorithm='auto').fit(X)
+    dist, rank = nbrs.kneighbors(X)
+
+
+    global indice
+    global predicted_players_name
+    global predicted_players_value
+    global predictions
+
+    indice = ply_stats.iloc[rank[0, 1:]].index.tolist()
+    predicted_players_name=df3['Name'].loc[indice,].tolist()
+    predicted_players_value=fifa['Value'].loc[indice,].tolist()
+    display_df1 = predictors_scaled.loc[indice,]
+    playrpredictorss = predictors_df.loc[indice,]
+    display_df2 = df3.loc[indice,]
+    display_df = fifa.loc[indice,]
+
+
+#part 2(prediction)
+    predictors_anomaly_processed=playrpredictorss[playrpredictorss.index.isin(list(display_df2['ID']))].copy()
+    predictors_anomaly_processed['Forward_Skill'] = predictors_anomaly_processed.loc[:,['LS',  'ST', 'RS',  'LW', 'LF', 'CF', 'RF', 'RW']].mean(axis=1)
+
+    predictors_anomaly_processed['Midfield_Skill'] = predictors_anomaly_processed.loc[:,['LAM','CAM','RAM', 'LM', 'LCM', 'CM' ,'RCM', 'RM','LDM', 'CDM', 'RDM']].mean(axis=1)
+
+    predictors_anomaly_processed['Defence_Skill'] = predictors_anomaly_processed.loc[:,['LWB','RWB', 'LB','LCB','CB','RCB','RB']].mean(axis=1)
+
+    predictors_anomaly_processed = predictors_anomaly_processed.drop(['LS',  'ST', 'RS',  'LW', 'LF', 'CF', 'RF', 'RW',
+          'LAM','CAM','RAM', 'LM', 'LCM', 'CM' ,'RCM', 'RM','LDM', 'CDM', 'RDM',
+          'LWB','RWB', 'LB','LCB','CB','RCB','RB'], axis = 1)
+
+    predictors_anomaly_processed=predictors_anomaly_processed.drop(predictors_anomaly_processed.iloc[:,predictors_anomaly_processed.columns.get_loc('Position_CAM'):predictors_anomaly_processed.columns.get_loc('Position_ST')+1], axis=1)
+
+    predictors_anomaly_processed=predictors_anomaly_processed[train_predictors_val.columns]
+    predictors_anomaly_processed[['International Reputation','Real Face']]=predictors_anomaly_processed[['International Reputation','Real Face']].astype('category')
+
+    scaler = StandardScaler()
+    predictors_anomaly_processed[predictors_anomaly_processed.select_dtypes(include=['float64','float32','int64','int32'], exclude=['category']).columns] = scaler.fit_transform(predictors_anomaly_processed.select_dtypes(include=['float64','float32','int64','int32'], exclude=['category']))
+    predictors_anomaly_processed[predictors_anomaly_processed.select_dtypes(include='category').columns]=predictors_anomaly_processed[predictors_anomaly_processed.select_dtypes(include='category').columns].astype('int')
+
+
+    predictions = abs(xbr.predict(predictors_anomaly_processed))
+    predictions = predictions.astype('int64')
+
+    result=final_pred(NUM_RECOM,predictions,predicted_players_value,predicted_players_name)
+    return result
+
+
+
+
+
+def final_pred(num_of_players,b=[],c=[],d=[]):
+
+    z=[]
+    for m in range(0,num_of_players):
+
+
+        c[m]=((c[m]+b[m])/2)
+        z.append({"starting_bid":c[m],"player_name":d[m]})
+
+
+    return z
+
+def main():
+    st.title("FIFA 19 Player Recommender 🎮⚽")
+    
+    # Sidebar inputs
+    st.sidebar.header("Search Parameters")
+    
+    # Get unique teams and positions
+    teams = sorted(df3['Club'].unique())
+    positions = sorted(df3['position_group'].unique())
+    
+    team_chosen = st.sidebar.selectbox("Select Team", teams)
+    postion_chosen = st.sidebar.selectbox("Select Position", positions)
+    num_of_players = st.sidebar.slider("Number of Players to Recommend", 1, 10, 5)
+    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}
+                    ---
+                    """)
+
+if __name__ == '__main__':
+    main()
+
+
+
+
+    #print("postions=side_df,cent_df,cent_md,side_md,cent_fw,side_fw,goalkeep")
+    #print("team=any club teams in any of the countries ")
+    #print("*********************************************** \n")
+    #team_chosen = str(input("Enter the team you are looking for:  \n"))
+    #postion_chosen = str(input("Enter the position you are looking for:  \n"))
+    #num_of_players = input("Enter the number of similar players you are looking for: \n")
+    #age_up = input("Enter the age limit: ")
+    #print("***please have some biscuits, it will take some time***")
+
+    #player_sim_team(team_chosen,postion_chosen, int(num_of_players), int(age_up))
+    #finalfunction = player_sim_team(team_chosen,postion_chosen, int(num_of_players), int(age_up))
+    #pickle.dump(finalfunction, open('finalfunction.pkl', 'wb'))

df3scaled.pkl

@@ -0,0 +1,3 @@
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+size 12965615

finalxbrmodel.pkl

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+oid sha256:7c3bc0ea4da01e2e0472b65613058efc7bda182555688ccb93cb464e5e5150ac
+size 337921

newdf3.pkl

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+oid sha256:eb9025fc993b7662b20b43fbbd2b51743e3e8e7d8639c1823163b3a05bef2d44
+size 14511354

newfifa.pkl

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+oid sha256:4e5b9afc1d32d860f5580c513e7473d86313230d2ff6e39411ba9563fe3dc437
+size 14505670

newpredictors.pkl

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+size 13605297

predictorsscale.pkl

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+oid sha256:b2803a7d147279fef83e66a70999e86e376de6cd2fd98d700cca3b13bab06ad9
+size 11769153

requirements.txt

@@ -0,0 +1,5 @@
+streamlit
+numpy
+pandas
+scikit-learn
+pickle5 

train_predictors_val.pkl

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