| Factor |
Evidence-based Approaches |
References |
| Word Retrieval |
Semantic feature analysis, phonological cueing, word generation tasks |
Boyle, 2010; Kiran & Thompson, 2003 |
| Grammatical Errors |
Treatment of Underlying Forms (TUF), Morphosyntactic therapy |
Thompson et al., 2003; Ebbels, 2014 |
| Fluency/Prosody |
Rate control, rhythmic cueing, contrastive stress exercises |
Ballard et al., 2010; Tamplin & Baker, 2017 |
""", elem_classes="markdown-text")
# Evidence tab
with gr.TabItem("Language Sample Evidence", id=2, elem_classes="tab-content"):
gr.Markdown("### Speech Sample Evidence", elem_classes="markdown-text")
# Create a collapsible section for each speech factor
factors = ["Word Retrieval", "Grammatical Errors", "Repetitions/Revisions",
"Fluency", "Neologisms", "Perseveration"]
for factor in factors:
with gr.Accordion(f"{factor} Examples", open=False):
gr.Markdown(f"Examples of {factor.lower()} will be highlighted here from the transcript.")
gr.Markdown("### Transcript Annotations", elem_classes="markdown-text")
gr.Markdown("A detailed analysis of the transcript will appear here after processing.")
# Full report tab
with gr.TabItem("Full Report", id=3, elem_classes="tab-content"):
full_analysis = gr.Markdown()
# Add PDF export option
export_btn = gr.Button("Export Report as PDF", variant="secondary")
export_status = gr.Markdown("")
# Footer
gr.HTML(
"""
"""
)
# Define app functions
# Function to load sample transcript
def load_sample():
return SAMPLE_TRANSCRIPT
# Handle file upload
def process_upload(file):
if file is None:
return ""
file_path = file.name
if file_path.endswith('.pdf'):
return read_pdf(file_path)
else:
with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
return f.read()
# Handle analysis button click
def on_analyze_click(transcript_text, age_val, gender_val, patient_name_val, record_id_val, clinician_val, assessment_date_val):
if not transcript_text or len(transcript_text.strip()) < 50:
return (
pd.DataFrame(),
pd.DataFrame(),
None,
None,
"Error: Please provide a longer transcript for analysis.",
"The transcript is too short for meaningful analysis.",
"Please provide a speech sample with at least 50 characters."
)
try:
results, plot_img, radar_img, full_text = analyze_transcript(transcript_text, age_val, gender_val)
# Format treatment suggestions as markdown
treatment_text = ""
for i, suggestion in enumerate(results['treatment_suggestions']):
treatment_text += f"- {suggestion}\n"
# Format to include patient metadata in the full report
patient_info = ""
if patient_name_val:
patient_info += f"**Patient:** {patient_name_val}\n"
if record_id_val:
patient_info += f"**Record ID:** {record_id_val}\n"
if age_val:
patient_info += f"**Age:** {age_val} years\n"
if gender_val:
patient_info += f"**Gender:** {gender_val}\n"
if assessment_date_val:
patient_info += f"**Assessment Date:** {assessment_date_val}\n"
if clinician_val:
patient_info += f"**Clinician:** {clinician_val}\n"
if patient_info:
full_report = f"## Patient Information\n\n{patient_info}\n\n## Analysis Report\n\n{full_text}"
else:
full_report = f"## Complete Analysis Report\n\n{full_text}"
# Convert image buffers to PIL images
plot_img_pil = Image.open(plot_img)
radar_img_pil = Image.open(radar_img)
return (
results['speech_factors'],
results['casl_data'],
plot_img_pil,
radar_img_pil,
treatment_text,
results['explanation'],
full_report
)
except Exception as e:
logger.exception("Error during analysis")
return (
pd.DataFrame(),
pd.DataFrame(),
None,
None,
f"Error during analysis: {str(e)}",
"An error occurred while processing the transcript.",
f"Error details: {str(e)}"
)
# Function to simulate PDF export
def export_pdf():
# In a real app, this would generate a PDF
# For this demo, we'll just return a status message
return "Report export initiated. The PDF would be downloaded in a production environment."
# Connect UI components to functions
sample_btn.click(load_sample, outputs=[transcript])
file_upload.upload(process_upload, file_upload, transcript)
export_btn.click(export_pdf, outputs=[export_status])
analyze_btn.click(
on_analyze_click,
inputs=[
transcript, age, gender,
patient_name, record_id, clinician_name, assessment_date
],
outputs=[
speech_factors_table,
casl_table,
output_image,
radar_chart,
treatment_md,
explanation_md,
full_analysis
]
)
return app
# Create requirements.txt file for HuggingFace Spaces
def create_requirements_file():
requirements = [
"gradio>=4.0.0",
"pandas",
"matplotlib",
"numpy",
"Pillow",
"PyPDF2",
"boto3"
]
with open("requirements.txt", "w") as f:
for req in requirements:
f.write(f"{req}\n")
# Create and launch the interface
if __name__ == "__main__":
# Create requirements.txt for HuggingFace Spaces
create_requirements_file()
# Check for AWS credentials
if not AWS_ACCESS_KEY or not AWS_SECRET_KEY:
print("NOTE: AWS credentials not found. The app will run in demo mode with simulated responses.")
print("To enable full functionality, set AWS_ACCESS_KEY and AWS_SECRET_KEY environment variables.")
# Launch the Gradio app
app = create_interface()
app.launch()