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import gradio as gr
import pandas as pd
import random
from io import StringIO
# Simulates mutation detection
def analyze_sequences(input_df):
if input_df is None or input_df.empty:
return pd.DataFrame(columns=["Sequence", "Predicted Mutation", "Confidence Score"])
results = []
for _, row in input_df.iterrows():
seq = row['DNA_Sequence']
mutation_types = ['SNV', 'Insertion', 'Deletion', 'No Mutation']
mutation = random.choice(mutation_types)
confidence = round(random.uniform(0.7, 0.99), 2)
results.append({
"Sequence": seq,
"Predicted Mutation": mutation,
"Confidence Score": confidence
})
return pd.DataFrame(results)
# Loads example data and analyzes it
def load_example_data():
example_df = pd.DataFrame({
"DNA_Sequence": [
"AGCTAGCTA",
"GATCGATCG",
"TTAGCTAGCT",
"ATGCGTAGC"
]
})
return analyze_sequences(example_df)
# Converts DataFrame to CSV string
def dataframe_to_csv(df):
if df is None or df.empty:
return ""
csv_buffer = StringIO()
df.to_csv(csv_buffer, index=False)
return csv_buffer.getvalue()
# Generate mutation statistics summary and chart
def get_mutation_stats(result_df):
if result_df is None or result_df.empty:
return "No data available.", None
# Count mutations
mutation_counts = result_df["Predicted Mutation"].value_counts()
summary_text = "📊 Mutation Statistics:\n"
for mutation, count in mutation_counts.items():
summary_text += f"- {mutation}: {count}\n"
# Create bar chart
chart = gr.BarPlot(
mutation_counts.reset_index(),
x="Predicted Mutation",
y="count",
title="Mutation Frequency",
color="Predicted Mutation",
tooltip=["Predicted Mutation", "count"],
vertical=False,
height=200
)
return summary_text, chart
# Unified function to process and return all outputs
def process_and_get_stats(file=None):
if file is not None:
result_df = analyze_sequences(file)
else:
result_df = load_example_data()
summary, chart = get_mutation_stats(result_df)
return result_df, summary, chart
# Gradio Interface
with gr.Blocks(theme="default") as demo:
gr.Markdown("""
# 🧬 MutateX – Liquid Biopsy Mutation Detection Tool
Upload a CSV file containing DNA sequences or use the example data to simulate mutation detection.
*Developed by [GradSyntax](https://www.gradsyntax.com )*
""")
with gr.Row(equal_height=True):
upload_btn = gr.File(label="📁 Upload CSV File", file_types=[".csv"])
example_btn = gr.Button("🧪 Load Example Data")
output_table = gr.DataFrame(
label="Analysis Results",
headers=["Sequence", "Predicted Mutation", "Confidence Score"]
)
stats_text = gr.Textbox(label="Mutation Statistics Summary")
stats_chart = gr.Plot(label="Mutation Frequency Chart")
download_btn = gr.File(label="⬇️ Download Results as CSV")
# Function calls
upload_btn.upload(fn=process_and_get_stats, inputs=upload_btn, outputs=[output_table, stats_text, stats_chart])
example_btn.click(fn=process_and_get_stats, inputs=None, outputs=[output_table, stats_text, stats_chart])
download_btn.upload(fn=dataframe_to_csv, inputs=output_table, outputs=download_btn)
demo.launch() |