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import pandas as pd
import plotly.express as px
import gradio as gr
# Load datasets
file_paths = {
"Training Session 1": "./ZED 2005 Session 1-12-2024.csv",
"Training Session 2": "./ZED 2005 Session 2-12-2024.csv",
"Training Session 6": "./ZED 2005 Session 6-12-2024.csv"
}
# Load and clean datasets
def load_and_clean_data(file_path):
df = pd.read_csv(file_path)
# Converting relevant columns to numeric
for col in ['Played Time (min)', 'Top Speed (km/h)', 'Dist. Covered (m)', 'Dribbling Count (#)',
'Kick Power (km/h)', 'Session Intensity', 'High Intensity Run (#)']:
if col in df.columns:
df[col] = pd.to_numeric(df[col], errors='coerce')
# Dropping rows with missing values in key metrics
df = df.dropna(subset=['Played Time (min)', 'Top Speed (km/h)', 'Dist. Covered (m)',
'Session Intensity', 'High Intensity Run (#)'])
return df
dataframes = {session: load_and_clean_data(path) for session, path in file_paths.items()}
# Generate interactive visualizations
def generate_visualizations(session, metric):
df = dataframes[session]
if metric == "Played Time Distribution":
fig = px.histogram(df, x="Played Time (min)", nbins=20, title="Played Time Distribution",
labels={"Played Time (min)": "Played Time (min)"}, color_discrete_sequence=["skyblue"])
elif metric == "Top Speed Boxplot":
fig = px.box(df, y="Top Speed (km/h)", title="Top Speed of Players",
labels={"Top Speed (km/h)": "Top Speed (km/h)"}, color_discrete_sequence=["lightgreen"])
elif metric == "Distance Covered vs Played Time":
fig = px.scatter(df, x="Played Time (min)", y="Dist. Covered (m)", color="Main Possition",
title="Distance Covered vs Played Time",
labels={"Played Time (min)": "Played Time (min)", "Dist. Covered (m)": "Distance Covered (m)"},
color_discrete_sequence=px.colors.qualitative.Vivid)
elif metric == "Dribbling Contribution by Position":
fig = px.bar(df, x="Main Possition", y="Dribbling Count (#)", title="Dribbling Contribution by Position",
labels={"Main Possition": "Position", "Dribbling Count (#)": "Dribbling Count (#)"},
color="Main Possition", color_discrete_sequence=px.colors.qualitative.Vivid)
elif metric == "Kick Power Distribution":
fig = px.histogram(df, x="Kick Power (km/h)", nbins=10, title="Kick Power Distribution",
labels={"Kick Power (km/h)": "Kick Power (km/h)"}, color_discrete_sequence=["orange"])
elif metric == "Session Intensity by Position":
fig = px.box(df, x="Main Possition", y="Session Intensity", title="Session Intensity by Position",
labels={"Main Possition": "Position", "Session Intensity": "Session Intensity"},
color="Main Possition", color_discrete_sequence=px.colors.qualitative.Vivid)
elif metric == "High-Intensity Runs":
fig = px.scatter(df, x="High Intensity Run (#)", y="High Intensity Run (m)", color="Main Possition",
title="High-Intensity Runs: Distance vs Frequency",
labels={"High Intensity Run (#)": "High Intensity Runs (#)",
"High Intensity Run (m)": "High Intensity Distance (m)"},
size="Session Intensity", color_discrete_sequence=px.colors.qualitative.Vivid)
else:
fig = px.scatter(df, x="Max Acceleration (m/s\u00b2)", y="Max Deceleration (m/s\u00b2)", color="Main Possition",
title="Max Intensity: Acceleration vs Deceleration",
labels={"Max Acceleration (m/s\u00b2)": "Max Acceleration (m/s\u00b2)",
"Max Deceleration (m/s\u00b2)": "Max Deceleration (m/s\u00b2)"},
color_discrete_sequence=px.colors.qualitative.Vivid)
return fig
# Gradio Interface
def visualize_data(session, metric):
fig = generate_visualizations(session, metric)
return fig
sessions = list(file_paths.keys())
metrics = ["Played Time Distribution", "Top Speed Boxplot", "Distance Covered vs Played Time",
"Dribbling Contribution by Position", "Kick Power Distribution", "Session Intensity by Position",
"High-Intensity Runs", "Max Intensity: Acceleration vs Deceleration"]
gr.Interface(
fn=visualize_data,
inputs=[gr.Dropdown(choices=sessions, label="Select Session"),
gr.Dropdown(choices=metrics, label="Select Metric")],
outputs=gr.Plot(label="Visualization"),
title="Football Analytics: Interactive Visualizations"
).launch()
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