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import os
import gradio as gr
from modules.eeg_processor import EEGProcessor
from modules.brain_mapper import BrainMapper
from modules.clinical_analyzer import ClinicalAnalyzer
from modules.treatment_planner import TreatmentPlanner
def process_eeg(file_obj):
processor = EEGProcessor()
mapper = BrainMapper()
analyzer = ClinicalAnalyzer()
planner = TreatmentPlanner()
# Process EEG data
eeg_data = processor.process_file(file_obj.name)
# Generate visualizations
brain_map = mapper.generate_topographic_map(eeg_data)
connectivity = mapper.generate_connectivity_map(eeg_data)
# Perform analysis
clinical_analysis = analyzer.analyze_eeg(eeg_data)
mental_health_assessment = analyzer.assess_mental_health(clinical_analysis)
risk_factors = analyzer.identify_risk_factors(clinical_analysis)
# Generate treatment plan
treatment_plan = planner.generate_plan(clinical_analysis, mental_health_assessment)
return {
"Brain Activity Map": brain_map,
"Brain Connectivity": connectivity,
"Clinical Analysis": clinical_analysis,
"Mental Health Assessment": mental_health_assessment,
"Risk Factors": risk_factors,
"Treatment Recommendations": treatment_plan
}
# Create Gradio interface
with gr.Blocks(title="AI-Powered Mental Health Analysis Platform") as demo:
gr.Markdown("# AI-Powered Mental Health Analysis Platform")
gr.Markdown("Upload your EEG data file for analysis and treatment recommendations.")
with gr.Row():
with gr.Column():
file_input = gr.File(label="Upload EEG Data (EDF, BDF, or CNT format)")
analyze_btn = gr.Button("Analyze")
with gr.Column():
brain_map = gr.Plot(label="Brain Activity Map")
connectivity_map = gr.Plot(label="Brain Connectivity")
with gr.Row():
with gr.Column():
clinical_output = gr.JSON(label="Clinical Analysis")
assessment_output = gr.JSON(label="Mental Health Assessment")
with gr.Column():
risk_output = gr.JSON(label="Risk Factors")
treatment_output = gr.Markdown(label="Treatment Recommendations")
analyze_btn.click(
fn=process_eeg,
inputs=[file_input],
outputs=[brain_map, connectivity_map, clinical_output,
assessment_output, risk_output, treatment_output]
)
# Launch the interface
if __name__ == "__main__":
demo.launch() |