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import streamlit as st |
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from PIL import Image |
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import os |
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import base64 |
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import io |
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from dotenv import load_dotenv |
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from groq import Groq |
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from reportlab.lib.pagesizes import letter |
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from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer |
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from reportlab.lib.styles import getSampleStyleSheet |
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PAGE_CONFIG = { |
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"page_title": "Radiology Analyzer", |
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"page_icon": "π©Ί", |
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"layout": "wide", |
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"initial_sidebar_state": "expanded" |
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} |
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ALLOWED_FILE_TYPES = ['png', 'jpg', 'jpeg'] |
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CSS_STYLES = """ |
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<style> |
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.main { background-color: #f4f9f9; color: #000000; } |
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.sidebar .sidebar-content { background-color: #d1e7dd; } |
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.stTextInput textarea { color: #000000 !important; } |
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.stSelectbox div[data-baseweb="select"], |
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.stSelectbox option, |
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.stSelectbox div[role="listbox"] div { |
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color: black !important; |
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background-color: #d1e7dd !important; |
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} |
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.stSelectbox svg { fill: black !important; } |
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.main-title { |
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font-size: 88px; |
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font-weight: bold; |
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color: rgb(33, 238, 238); |
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} |
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.sub-title { |
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font-size: 100px; |
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color: #6B6B6B; |
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margin-top: -1px; |
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} |
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.stButton>button { |
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background-color: rgb(33, 225, 250); |
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color: white; |
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font-size: 69px; |
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} |
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.stImage img { |
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border-radius: 10px; |
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box-shadow: 2px 2px 10px rgba(0,0,0,0.1); |
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} |
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.logo { |
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text-align: center; |
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margin-bottom: 20px; |
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} |
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.report-container { |
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background-color: #ffffff; |
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border-radius: 15px; |
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padding: 25px; |
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margin-top: 20px; |
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box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); |
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border-left: 5px solid #21eeef; |
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} |
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.report-text { |
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font-family: 'Courier New', monospace; |
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font-size: 16px; |
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line-height: 1.6; |
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color: #2c3e50; |
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} |
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.download-btn { |
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background-color: #21eeef !important; |
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color: white !important; |
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border: none !important; |
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border-radius: 8px !important; |
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padding: 12px 24px !important; |
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} |
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</style> |
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""" |
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def configure_application(): |
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"""Initialize application settings and styling""" |
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st.set_page_config(**PAGE_CONFIG) |
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st.markdown(CSS_STYLES, unsafe_allow_html=True) |
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def initialize_api_client(): |
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"""Create and validate Groq API client""" |
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load_dotenv() |
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api_key = os.getenv("GROQ_API_KEY") |
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if not api_key: |
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st.error("API key not found. Please verify .env configuration.") |
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st.stop() |
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return Groq(api_key=api_key) |
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def encode_logo(image_path): |
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"""Encode logo image to base64""" |
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try: |
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with open(image_path, "rb") as img_file: |
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return base64.b64encode(img_file.read()).decode("utf-8") |
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except FileNotFoundError: |
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st.error("Logo image not found! Using placeholder.") |
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return "" |
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def process_image_data(uploaded_file): |
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"""Convert image to base64 encoded string""" |
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try: |
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image = Image.open(uploaded_file) |
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buffer = io.BytesIO() |
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image.save(buffer, format=image.format) |
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return base64.b64encode(buffer.getvalue()).decode('utf-8'), image.format |
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except Exception as e: |
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st.error(f"Image processing error: {str(e)}") |
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return None, None |
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def generate_pdf_report(report_text): |
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"""Generate PDF document from report text""" |
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buffer = io.BytesIO() |
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doc = SimpleDocTemplate(buffer, pagesize=letter) |
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styles = getSampleStyleSheet() |
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story = [] |
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title = Paragraph("<b>Radiology Report</b>", styles['Title']) |
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story.append(title) |
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story.append(Spacer(1, 12)) |
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content = Paragraph(report_text.replace('\n', '<br/>'), styles['BodyText']) |
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story.append(content) |
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doc.build(story) |
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buffer.seek(0) |
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return buffer |
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def generate_radiology_report(uploaded_file, client): |
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"""Generate AI-powered radiology analysis""" |
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base64_image, img_format = process_image_data(uploaded_file) |
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if not base64_image: |
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return None |
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image_url = f"data:image/{img_format.lower()};base64,{base64_image}" |
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try: |
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response = client.chat.completions.create( |
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model="llama-3.2-11b-vision-preview", |
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messages=[{ |
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"role": "user", |
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"content": [ |
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{"type": "text", "text": ( |
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"As an AI radiologist, provide a detailed structured report including: " |
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"1. Imaging modality identification\n2. Anatomical structures visualized\n" |
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"3. Abnormal findings description\n4. Differential diagnoses\n" |
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"5. Clinical correlation recommendations" |
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)}, |
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{"type": "image_url", "image_url": {"url": image_url}}, |
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] |
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}], |
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temperature=0.2, |
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max_tokens=400, |
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top_p=0.5 |
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) |
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return response.choices[0].message.content |
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except Exception as e: |
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st.error(f"API communication error: {str(e)}") |
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return None |
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def display_main_interface(): |
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"""Render primary application interface""" |
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logo_b64 = encode_logo("src/radiology.png") |
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st.markdown( |
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f""" |
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<div style="text-align: center;"> |
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<div class="logo"> |
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<img src="data:image/png;base64,{logo_b64}" width="100"> |
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</div> |
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<p class="main-title"> Radiology Analyzer</p> |
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<p class="sub-title">Advanced Medical Imaging Analysis</p> |
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</div> |
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""", |
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unsafe_allow_html=True |
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) |
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st.markdown("---") |
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col1, col2 = st.columns([1, 1]) |
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with col1: |
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if st.session_state.get('analysis_result'): |
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pdf_report = generate_pdf_report(st.session_state.analysis_result) |
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st.download_button( |
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label="π Download PDF Report", |
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data=pdf_report, |
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file_name="radiology_report.pdf", |
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mime="application/pdf", |
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use_container_width=True, |
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help="Download formal PDF version of the report", |
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key="download_pdf" |
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) |
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with col2: |
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if st.button("Clear Analysis ποΈ", use_container_width=True, help="Remove current results"): |
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st.session_state.pop('analysis_result') |
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st.rerun() |
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if st.session_state.get('analysis_result'): |
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st.markdown("### π― Radiological Findings Report") |
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st.markdown( |
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f'<div class="report-container"><div class="report-text">{st.session_state.analysis_result}</div></div>', |
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unsafe_allow_html=True |
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) |
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def render_sidebar(client): |
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"""Create sidebar interface elements""" |
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with st.sidebar: |
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st.divider() |
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st.markdown("### Diagnostic Capabilities") |
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st.markdown(""" |
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- **Multi-Modality Analysis**: X-ray, MRI, CT, Ultrasound |
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- **Pathology Detection**: Fractures, tumors, infections |
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- **Comparative Analysis**: Track disease progression |
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- **Structured Reporting**: Standardized output format |
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- **Clinical Correlation**: Suggested next steps |
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""") |
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st.divider() |
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st.subheader("Image Upload Section") |
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uploaded_file = st.file_uploader( |
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"Select Medical Image", |
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type=ALLOWED_FILE_TYPES, |
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help="Supported formats: PNG, JPG, JPEG" |
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) |
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if uploaded_file: |
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st.image(Image.open(uploaded_file), |
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caption="Uploaded Medical Image", |
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use_container_width=True) |
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if st.button("Initiate Analysis π", use_container_width=True): |
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with st.spinner("Analyzing image. This may take 20-30 seconds..."): |
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report = generate_radiology_report(uploaded_file, client) |
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st.session_state.analysis_result = report |
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st.rerun() |
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def main(): |
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"""Primary application controller""" |
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configure_application() |
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groq_client = initialize_api_client() |
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display_main_interface() |
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render_sidebar(groq_client) |
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if __name__ == "__main__": |
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main() |