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04046fe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | import gradio as gr
import pandas as pd
import tempfile
# --------------------------------------------------
# Metric Computation Function
# --------------------------------------------------
def compute_metrics(tp, tn, fp, fn):
tp, tn, fp, fn = int(tp), int(tn), int(fp), int(fn)
total = tp + tn + fp + fn
def safe_div(a, b):
return a / b if b != 0 else 0
accuracy = safe_div(tp + tn, total)
precision = safe_div(tp, tp + fp)
recall = safe_div(tp, tp + fn) # Sensitivity
specificity = safe_div(tn, tn + fp)
f1_score = safe_div(2 * precision * recall, precision + recall)
npv = safe_div(tn, tn + fn)
fpr = safe_div(fp, fp + tn)
fnr = safe_div(fn, fn + tp)
fdr = safe_div(fp, fp + tp)
balanced_accuracy = (recall + specificity) / 2
metrics = {
"Accuracy": accuracy,
"Precision (PPV)": precision,
"Recall / Sensitivity (TPR)": recall,
"Specificity (TNR)": specificity,
"F1 Score": f1_score,
"Negative Predictive Value (NPV)": npv,
"False Positive Rate (FPR)": fpr,
"False Negative Rate (FNR)": fnr,
"False Discovery Rate (FDR)": fdr,
"Balanced Accuracy": balanced_accuracy,
"Total Samples": total
}
df = pd.DataFrame(list(metrics.items()), columns=["Metric", "Value"])
# Save CSV temporarily for download
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
df.to_csv(temp_file.name, index=False)
return df, temp_file.name
# --------------------------------------------------
# Gradio Interface
# --------------------------------------------------
with gr.Blocks(title="Classification Metrics Calculator") as demo:
gr.Markdown(
"""
# 📊 Classification Metrics Calculator
Enter Confusion Matrix values (TP, TN, FP, FN) to compute evaluation metrics.
"""
)
with gr.Row():
tp = gr.Number(value=50, label="True Positives (TP)")
tn = gr.Number(value=40, label="True Negatives (TN)")
with gr.Row():
fp = gr.Number(value=10, label="False Positives (FP)")
fn = gr.Number(value=5, label="False Negatives (FN)")
compute_btn = gr.Button("Compute Metrics")
output_table = gr.Dataframe(
headers=["Metric", "Value"],
datatype=["str", "number"],
label="Computed Metrics"
)
download_file = gr.File(label="⬇ Download Results (CSV)")
compute_btn.click(
fn=compute_metrics,
inputs=[tp, tn, fp, fn],
outputs=[output_table, download_file]
)
# Launch (important for HF Spaces)
demo.launch() |