import streamlit as st
from PIL import Image
import os
import base64
import io
from dotenv import load_dotenv
from groq import Groq
from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image as ReportLabImage
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import inch
from datetime import datetime
import re
from reportlab.lib import colors
import random
import streamlit.components.v1 as components
# ======================
# CONFIGURATION SETTINGS
# ======================
PAGE_CONFIG = {
"page_title": "Radiology Analyzer",
"page_icon": "🩺",
"layout": "wide",
"initial_sidebar_state": "expanded"
}
ALLOWED_FILE_TYPES = ['png', 'jpg', 'jpeg']
CSS_STYLES = """
"""
# ======================
# CORE FUNCTIONS
# ======================
def configure_application():
"""Initialize application settings and styling"""
st.set_page_config(**PAGE_CONFIG)
st.markdown(CSS_STYLES, unsafe_allow_html=True)
def initialize_groq_client():
"""Create and validate Groq API client"""
load_dotenv()
api_key = os.getenv("GROQ_API_KEY")
if not api_key:
st.error("Groq API key not found. Please provide an API key.")
return None
return Groq(api_key=api_key)
def encode_logo(image_path):
"""Encode logo image to base64"""
try:
with open(image_path, "rb") as img_file:
return base64.b64encode(img_file.read()).decode("utf-8")
except FileNotFoundError:
# Return a placeholder image (blue medical technology icon) encoded as base64
return "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"
def process_image_data(uploaded_file):
"""Convert image to base64 encoded string"""
try:
image = Image.open(uploaded_file)
buffer = io.BytesIO()
image.save(buffer, format=image.format)
return base64.b64encode(buffer.getvalue()).decode('utf-8'), image.format
except Exception as e:
st.error(f"Image processing error: {str(e)}")
return None, None
def generate_pdf_report(report_text, uploaded_file):
"""Generate professionally formatted PDF report with bold headers."""
buffer = io.BytesIO()
doc = SimpleDocTemplate(buffer, pagesize=letter,
rightMargin=72, leftMargin=72,
topMargin=72, bottomMargin=72)
# Create custom styles for different parts of the report
styles = getSampleStyleSheet()
# Custom styles for better formatting
title_style = ParagraphStyle(
'ReportTitle',
parent=styles['Title'],
fontSize=16,
alignment=1, # Center aligned
spaceAfter=12
)
header_style = ParagraphStyle(
'SectionHeader',
parent=styles['Heading2'],
fontSize=12,
fontName='Helvetica-Bold',
textColor=colors.black,
spaceBefore=12,
spaceAfter=6
)
normal_style = ParagraphStyle(
'NormalText',
parent=styles['BodyText'],
fontSize=11,
leading=14,
spaceAfter=8
)
abnormal_style = ParagraphStyle(
'AbnormalText',
parent=styles['BodyText'],
fontSize=11,
leading=14,
textColor=colors.red,
backColor=colors.lightgrey,
borderPadding=5,
spaceAfter=8
)
footer_style = ParagraphStyle(
'FooterText',
parent=styles['Italic'],
fontSize=9,
alignment=1 # Center aligned
)
# Begin building the report
story = []
# Hospital/Institution Header
header_text = "RADIOLOGY DEPARTMENT"
header = Paragraph(header_text, title_style)
story.append(header)
# Report Title
report_title = "RADIOLOGICAL EXAMINATION REPORT"
title = Paragraph(report_title, title_style)
story.append(title)
# Add date and report ID
date_text = f"Date: {datetime.now().strftime('%B %d, %Y')}"
report_id = f"Report ID: RAD-{datetime.now().strftime('%Y%m%d')}-{random.randint(1000, 9999)}"
date_para = Paragraph(date_text, normal_style)
id_para = Paragraph(report_id, normal_style)
story.append(date_para)
story.append(id_para)
story.append(Spacer(1, 12))
# Add the image to the PDF
if uploaded_file:
try:
uploaded_file.seek(0) # Reset file pointer to beginning
pil_image = Image.open(uploaded_file)
img_width = 5 * inch
aspect = float(pil_image.height) / float(pil_image.width)
img_height = img_width * aspect
img_temp = io.BytesIO()
pil_image.save(img_temp, format=pil_image.format if pil_image.format else 'JPEG')
img_temp.seek(0)
img = ReportLabImage(img_temp, width=img_width, height=img_height)
story.append(img)
story.append(Spacer(1, 12))
# Add image caption
caption = Paragraph("Figure 1: Radiological Image for Analysis", normal_style)
story.append(caption)
story.append(Spacer(1, 12))
except Exception as e:
error_text = Paragraph(f"Image processing error: {str(e)}", normal_style)
story.append(error_text)
story.append(Spacer(1, 12))
# Clean the report text (remove markdown-style formatting and unwanted characters)
cleaned_text = report_text.replace('**', '').replace('##', '').replace('*', '-')
# Define section headers to identify
section_headers = [
"DIAGNOSIS",
"ETIOLOGY",
"RISK FACTORS",
"PATHOPHYSIOLOGY",
"CLINICAL FEATURES",
"SIGNS AND SYMPTOMS",
"INVESTIGATIONS",
"MANAGEMENT",
"INITIAL STABILIZATION",
"MEDICAL MANAGEMENT",
"SURGICAL MANAGEMENT",
"PROGNOSIS"
]
# Split into lines for more precise processing
lines = cleaned_text.split('\n')
current_section = ""
section_content = ""
for i, line in enumerate(lines):
line = line.strip()
if not line:
continue
# Remove any "Step X:" prefixes
line = re.sub(r'^Step \d+:\s*', '', line)
# Check if this is a section header
is_header = False
for header in section_headers:
if line.upper().startswith(header) or line.upper() == header + ":":
is_header = True
break
# Also check if it's a short line ending with a colon (likely a header)
if not is_header and len(line) < 60 and line.endswith(':'):
is_header = True
# If we found a header
if is_header:
# First add any accumulated content from previous section
if section_content.strip():
# Check for severe abnormalities to highlight
severe_abnormal_keywords = [
'severe', 'critical', 'urgent', 'emergency', 'life-threatening',
'malignant', 'neoplasm', 'carcinoma', 'metastasis', 'hemorrhage',
'fracture', 'rupture', 'perforation',
]
has_severe_issue = any(keyword in section_content.lower() for keyword in severe_abnormal_keywords)
if current_section.upper().startswith("DIAGNOSIS") or current_section.upper().startswith("ABNORMAL"):
# This is a diagnosis section - highlight abnormalities
p = Paragraph(section_content, abnormal_style if has_severe_issue else normal_style)
else:
p = Paragraph(section_content, normal_style)
story.append(p)
story.append(Spacer(1, 6))
section_content = ""
# Add the new section header - remove any trailing colon for cleaner look
clean_header = line.strip()
if clean_header.endswith(':'):
clean_header = clean_header[:-1]
current_section = clean_header
p = Paragraph(f"{clean_header}", header_style) # Bold the header
story.append(p)
else:
# This is content - append to the current section content
if section_content:
section_content += " " + line
else:
section_content = line
# Add any remaining content
if section_content.strip():
p = Paragraph(section_content, normal_style)
story.append(p)
# Add conclusion if not present
if not any("PROGNOSIS" in line.upper() for line in lines):
conclusion_header = Paragraph("PROGNOSIS", header_style)
story.append(conclusion_header)
story.append(Spacer(1, 6))
conclusion_text = "Prognosis varies based on the extent and location of findings. Clinical correlation with the patient's symptoms and medical history is recommended."
conclusion_para = Paragraph(conclusion_text, normal_style)
story.append(conclusion_para)
# Add footer with disclaimer
story.append(Spacer(1, 24))
disclaimer = "This report was generated with AI assistance and should be reviewed by a qualified healthcare professional."
footer = Paragraph(disclaimer, footer_style)
story.append(footer)
# Build PDF
doc.build(story)
buffer.seek(0)
return buffer
def generate_radiology_report_groq(uploaded_file, client):
"""Generate AI-powered radiology analysis using Groq API"""
base64_image, img_format = process_image_data(uploaded_file)
if not base64_image:
return None
image_url = f"data:image/{img_format.lower()};base64,{base64_image}"
try:
with st.spinner("Analyzing image..."):
# Add progress bar for visual feedback
progress_bar = st.progress(0)
for i in range(100):
# Update progress bar
progress_bar.progress(i + 1)
import time
time.sleep(0.025) # Simulate processing time
# Updated prompt to request the detailed, structured format
response = client.chat.completions.create(
model="meta-llama/llama-4-maverick-17b-128e-instruct", # Use Groq's model
messages=[{
"role": "user",
"content": [
{"type": "text", "text": (
"""As a radiologist, analyze the following Medical report and provide a comprehensive report structured as follows:
1. **DIAGNOSIS**: Clearly state the primary diagnosis, including dimensions in mmIf where applicable (e.g., if a tumor is present). Use specific anatomical terms relevant to the body part being examined.If it is chest xray also mention if pneumonia is present or not.
2. **FINDINGS**:
- Provide detailed observations from the Medical report, including:
- The size, shape, and location of any lesions or abnormalities if applicable.
- Description of the surrounding tissues and structures if applicable.
- Any noted changes in signal intensity on various sequences (e.g., T1W, T2W, FLAIR) if applicable.
- Mention of any associated findings, such as edema, mass effect, or midline shift if applicble.
- Specific comments on vascular structures, if applicable.
3. **PATHOPHYSIOLOGY**: Briefly explain the disease mechanism related to the diagnosis, focusing on how it affects the specific body part.
4. **CLINICAL FEATURES**: Provide an overview of typical clinical presentations associated with this diagnosis, emphasizing symptoms that may arise from abnormalities in the specified anatomical area.
5. **SIGNS AND SYMPTOMS**: List common signs and symptoms relevant to the findings in the MRI report. Tailor this section to align with the specific anatomy being assessed.
6. **INVESTIGATIONS**: Mention diagnostic tests typically used for confirmation of the diagnosis, including imaging studies or laboratory tests pertinent to the body part.
7. **MANAGEMENT**: Outline the management plans in three parts:
- Initial Stabilization: Describe immediate steps for patient care.
- Medical Management: Outline pharmacological treatments and monitoring.
- Surgical Management (if applicable): Discuss any surgical interventions specific to the diagnosis and body part.
8. **PROGNOSIS**: Describe expected outcomes and factors that may affect prognosis based on the diagnosis. Include considerations specific to the anatomical region and associated complications.
Please ensure to focus on the following findings from the report:
- Mention specific abnormalities based on the region (e.g., "T2/FLAIR hyperintensities in the right fronto-parietal region" for brain MRI).
- Highlight any significant lesions or deviations from the norm.
- Include any other abnormal findings noted in the report that are relevant to the specific anatomy.
Format each section with appropriate headings and use bullet points for lists. Base your analysis on the provided MRI report details."""
)},
{"type": "image_url", "image_url": {"url": image_url}},
]
}],
temperature=0.1,
max_tokens=3000, # Increased token limit for more detailed response
top_p=0.3
)
return response.choices[0].message.content
except Exception as e:
st.error(f"Groq API error: {str(e)}")
return None
def generate_radiology_report(uploaded_file, api_choice='groq'):
client = initialize_groq_client()
if client:
return generate_radiology_report_groq(uploaded_file, client)
else:
st.error("Failed to initialize Groq client. Please check your API key.")
return None
# ======================
# UI COMPONENTS
# ======================
def display_animated_logo():
"""Display an animated medical logo"""
logo_b64 = encode_logo("src/Round_image_depicting_a_futuristic_medical_image_a-1742282117033-photoaidcom-cropped.png")
# If logo file doesn't exist, use the placeholder from encode_logo
st.markdown(
f"""
""",
unsafe_allow_html=True
)
def display_main_interface():
"""Render primary application interface"""
# Display animated logo and titles
display_animated_logo()
st.markdown('
Radiology Analyzer
', unsafe_allow_html=True)
st.markdown('
Advanced Medical Imaging Analysis
', unsafe_allow_html=True)
# Action buttons
if st.session_state.get('analysis_result'):
st.markdown(
"""
Analysis Complete
""",
unsafe_allow_html=True
)
col1, col2 = st.columns([1, 1])
with col1:
pdf_report = generate_pdf_report(st.session_state.analysis_result, st.session_state.uploaded_file)
st.download_button(
label="📄 Download PDF Report",
data=pdf_report,
file_name="radiology_report.pdf",
mime="application/pdf",
use_container_width=True,
help="Download formal PDF version of the report"
)
with col2:
if st.button("Clear Analysis 🗑️", use_container_width=True, help="Remove current results"):
st.session_state.pop('analysis_result', None)
st.session_state.pop('uploaded_file', None)
st.rerun()
# Display analysis results in a styled container
st.markdown("### 🎯 Radiological Findings Report")
st.markdown(
f'
{st.session_state.analysis_result}
',
unsafe_allow_html=True
)
else:
# Show a centered placeholder message
st.markdown(
"""
', unsafe_allow_html=True)
uploaded_file = st.file_uploader(
"Select Medical Image",
type=ALLOWED_FILE_TYPES,
label_visibility="collapsed",
help="Supported formats: PNG, JPG, JPEG"
)
if uploaded_file:
st.session_state.uploaded_file = uploaded_file # Store uploaded file in session state
# Display image with a styled container
# st.markdown('
', unsafe_allow_html=True)
st.image(Image.open(uploaded_file),
caption="Uploaded Medical Image",
use_container_width=True)
st.markdown('
', unsafe_allow_html=True)
if st.button("▶️ Initiate Analysis", use_container_width=True):
# Use the selected API provider
api_choice = 'groq'
report = generate_radiology_report(uploaded_file, api_choice)
if report:
st.session_state.analysis_result = report
st.rerun()
st.markdown('
', unsafe_allow_html=True)
# ======================
# APPLICATION ENTRYPOINT
# ======================
def main():
"""Primary application controller"""
# Check if dark mode is in session state, default to true
if 'dark_mode' not in st.session_state:
st.session_state.dark_mode = True
configure_application()
render_sidebar()
display_main_interface()
if __name__ == "__main__":
main()
