Spaces:
Sleeping
Sleeping
Add technical report generation feature
Browse files
app.py
CHANGED
@@ -4,16 +4,50 @@ from PIL import Image
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import numpy as np
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import os
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from pathlib import Path
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from model import RadarDetectionModel
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from feature_extraction import (calculate_amplitude, classify_amplitude,
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calculate_distribution_range, classify_distribution_range,
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calculate_attenuation_rate, classify_attenuation_rate,
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count_reflections, classify_reflections
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from report_generation import generate_report, render_report
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from utils import plot_detection
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from database import save_report, get_report_history
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# Initialize model with HF token from environment
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model = None
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try:
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@@ -31,15 +65,15 @@ def initialize_model():
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return None, f"Error initializing model: {str(e)}"
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return model, None
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def process_image(image):
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if image is None:
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return None, "Please upload an image."
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# Initialize model if needed
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global model
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model, error = initialize_model()
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if error:
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return None, error
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try:
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# Convert to PIL Image if needed
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@@ -78,18 +112,29 @@ def process_image(image):
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report = generate_report(detection_result, image, features)
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detection_image = plot_detection(image, detection_result)
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# Save report if database is configured
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save_report(report)
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except Exception as e:
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print(f"Warning: Could not save report: {str(e)}")
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return detection_image, render_report(report)
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except Exception as e:
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error_msg = f"Error processing image: {str(e)}"
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print(error_msg)
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return None, error_msg
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def display_history():
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try:
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@@ -127,11 +172,13 @@ with gr.Blocks(css=css) as iface:
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with gr.Row():
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with gr.Column(scale=1):
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input_image = gr.Image(type="pil", label="Upload Radar Image")
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analyze_button = gr.Button("Analyze", variant="primary")
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with gr.Column(scale=2):
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output_image = gr.Image(type="pil", label="Detection Result")
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output_report = gr.HTML(label="Analysis Report")
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with gr.Row():
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history_button = gr.Button("View History")
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@@ -140,8 +187,8 @@ with gr.Blocks(css=css) as iface:
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# Set up event handlers
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analyze_button.click(
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fn=process_image,
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inputs=[input_image],
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outputs=[output_image, output_report]
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)
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history_button.click(
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import numpy as np
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import os
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from pathlib import Path
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from datetime import datetime
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import tempfile
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from model import RadarDetectionModel
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from feature_extraction import (calculate_amplitude, classify_amplitude,
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calculate_distribution_range, classify_distribution_range,
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calculate_attenuation_rate, classify_attenuation_rate,
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count_reflections, classify_reflections,
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extract_features)
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from report_generation import generate_report, render_report
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from utils import plot_detection
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from database import save_report, get_report_history
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class TechnicalReportGenerator:
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def __init__(self):
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self.timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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def _generate_technical_section(self, detection_result, features):
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"""Generate technical analysis section of the report."""
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tech_doc = "## Technical Analysis\n\n"
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# Detection Results
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tech_doc += "### Detection Results\n\n```python\n"
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tech_doc += f"Confidence Scores: {detection_result['scores'].tolist()}\n"
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tech_doc += f"Bounding Boxes: {detection_result['boxes'].tolist()}\n"
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tech_doc += f"Labels: {detection_result['labels'].tolist()}\n"
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tech_doc += "```\n\n"
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# Feature Analysis
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tech_doc += "### Feature Analysis\n\n"
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for feature_name, value in features.items():
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tech_doc += f"- **{feature_name}**: {value}\n"
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# Signal Processing Details
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tech_doc += "\n### Signal Processing Metrics\n\n"
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tech_doc += "| Metric | Value | Classification |\n"
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tech_doc += "|--------|--------|----------------|\n"
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tech_doc += f"|Amplitude|{features['Amplitude']}|{classify_amplitude(features['Amplitude'])}|\n"
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tech_doc += f"|Distribution Range|{features['Distribution Range']}|{features['Distribution Range']}|\n"
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tech_doc += f"|Attenuation Rate|{features['Attenuation Rate']}|{features['Attenuation Rate']}|\n"
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tech_doc += f"|Reflection Count|{features['Reflection Count']}|{features['Reflection Count']}|\n"
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return tech_doc
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# Initialize model with HF token from environment
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model = None
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try:
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return None, f"Error initializing model: {str(e)}"
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return model, None
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def process_image(image, generate_tech_report=False):
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if image is None:
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return None, "Please upload an image.", None
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# Initialize model if needed
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global model
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model, error = initialize_model()
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if error:
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return None, error, None
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try:
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# Convert to PIL Image if needed
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report = generate_report(detection_result, image, features)
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detection_image = plot_detection(image, detection_result)
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# Generate technical report if requested
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tech_report = None
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if generate_tech_report:
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report_gen = TechnicalReportGenerator()
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tech_report = report_gen._generate_technical_section(detection_result, features)
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# Save technical report to a temporary file
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with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.md') as f:
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f.write(tech_report)
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tech_report = f.name
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# Save report if database is configured
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try:
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save_report(report)
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except Exception as e:
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print(f"Warning: Could not save report: {str(e)}")
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return detection_image, render_report(report), tech_report
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except Exception as e:
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error_msg = f"Error processing image: {str(e)}"
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print(error_msg)
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return None, error_msg, None
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def display_history():
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try:
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with gr.Row():
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with gr.Column(scale=1):
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input_image = gr.Image(type="pil", label="Upload Radar Image")
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tech_report_checkbox = gr.Checkbox(label="Generate Technical Report", value=False)
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analyze_button = gr.Button("Analyze", variant="primary")
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with gr.Column(scale=2):
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output_image = gr.Image(type="pil", label="Detection Result")
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output_report = gr.HTML(label="Analysis Report")
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tech_report_output = gr.File(label="Technical Report")
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with gr.Row():
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history_button = gr.Button("View History")
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# Set up event handlers
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analyze_button.click(
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fn=process_image,
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inputs=[input_image, tech_report_checkbox],
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outputs=[output_image, output_report, tech_report_output]
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)
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history_button.click(
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