Update src/streamlit_app.py
Browse files- src/streamlit_app.py +7 -315
src/streamlit_app.py
CHANGED
@@ -11,13 +11,14 @@ from model import (
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parse_doctor_response
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)
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import base64
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from reportlab.lib.pagesizes import A4
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from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
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from reportlab.lib.enums import TA_LEFT
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from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle
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from reportlab.lib import colors
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import tempfile
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from sendgrid_service import SendGridService
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# Set page config with dark theme
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@@ -528,315 +529,6 @@ assistant_logo_base64 = get_image_base64(assistant_logo_path)
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user_avatar = f"data:image/png;base64,{user_logo_base64}" if user_logo_base64 else "👤"
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assistant_avatar = f"data:image/png;base64,{assistant_logo_base64}" if assistant_logo_base64 else "🤖"
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# Add report generation functions
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def extract_medical_json(conversation_text: str) -> dict:
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"""
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Extract medical report data from conversation text into structured JSON.
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Uses the existing orchestrator_chat model.
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"""
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system_prompt = """
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You are an expert medical report generator. Extract ALL info from the patient-assistant conversation into this JSON schema:
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{
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"patient": {"name":"","age":"","gender":""},
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"visit_date":"YYYY-MM-DD",
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"chief_complaint":"",
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"history_of_present_illness":"",
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"past_medical_history":"",
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"medications":"",
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"allergies":"",
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"examination":"",
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"diagnosis":"",
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"recommendations":"",
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"reasoning":"",
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"sources":""
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}
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Do NOT invent any data. Return ONLY the JSON object.
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"""
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# Create a fake history with the system prompt as a system message
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history = [
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{"role": "system", "content": system_prompt}
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]
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# Use our existing orchestrator_chat function with valid parameters
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result, _, _ = orchestrator_chat(
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history=history,
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query=conversation_text,
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use_rag=True,
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is_follow_up=False
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)
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# Extract JSON from response
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try:
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# Find JSON object in the response
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json_match = re.search(r'({[\s\S]*})', result)
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if json_match:
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json_str = json_match.group(1)
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return json.loads(json_str)
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else:
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# If no clear JSON format found, try to parse the whole response
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return json.loads(result)
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except json.JSONDecodeError:
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# Fallback with basic structure
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st.error("Failed to parse report data")
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return {
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"patient": {"name":"", "age":"", "gender":""},
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"visit_date": datetime.now().strftime("%Y-%m-%d"),
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"chief_complaint": "Error generating report"
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}
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def build_medical_report(data: dict) -> bytes:
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"""
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Generates a PDF from the extracted JSON and returns PDF bytes.
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"""
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# Create a temporary file for the PDF
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fd, temp_path = tempfile.mkstemp(suffix='.pdf')
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os.close(fd)
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doc = SimpleDocTemplate(
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temp_path,
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pagesize=A4,
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rightMargin=40,
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leftMargin=40,
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topMargin=60,
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bottomMargin=60
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)
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styles = getSampleStyleSheet()
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styles.add(ParagraphStyle(
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name='SectionHeading',
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fontSize=14,
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leading=16,
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spaceAfter=8,
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alignment=TA_LEFT,
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textColor=colors.darkblue
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))
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styles.add(ParagraphStyle(
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name='NormalLeft',
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fontSize=11,
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leading=14,
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spaceAfter=6,
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alignment=TA_LEFT
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))
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elems = []
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# Title
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elems.append(Paragraph("Medical Consultation Report", styles['Title']))
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elems.append(Spacer(1, 12))
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# Patient info
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patient = data.get('patient', {})
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info = [
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['Name:', patient.get('name', '–')],
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['Age:', patient.get('age', '–')],
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['Gender:', patient.get('gender', '–')],
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['Date:', data.get('visit_date', datetime.now().strftime("%Y-%m-%d"))],
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]
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tbl = Table(info, colWidths=[80, 250])
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tbl.setStyle(TableStyle([
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('BACKGROUND', (0, 0), (1, 0), colors.lightgrey),
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('BOX', (0, 0), (-1, -1), 0.5, colors.grey),
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('INNERGRID', (0, 0), (-1, -1), 0.5, colors.grey),
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('VALIGN', (0, 0), (-1, -1), 'MIDDLE'),
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]))
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elems += [tbl, Spacer(1, 16)]
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# Helper to add sections
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def add_section(title, text):
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# Convert text to string if it's a list or another non-string type
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if isinstance(text, list):
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text = '\n'.join(map(str, text))
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elif text is not None and not isinstance(text, str):
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text = str(text)
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elems.append(Paragraph(title, styles['SectionHeading']))
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elems.append(Paragraph(text or '–', styles['NormalLeft']))
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elems.append(Spacer(1, 12))
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# Populate all sections
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add_section("Chief Complaint", data.get('chief_complaint'))
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add_section("History of Present Illness", data.get('history_of_present_illness'))
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add_section("Past Medical History", data.get('past_medical_history'))
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add_section("Medications", data.get('medications'))
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add_section("Allergies", data.get('allergies'))
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add_section("Examination Findings", data.get('examination'))
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add_section("Diagnosis", data.get('diagnosis'))
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add_section("Recommendations", data.get('recommendations'))
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add_section("Reasoning", data.get('reasoning'))
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add_section("Sources", data.get('sources'))
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doc.build(elems)
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# Read PDF bytes
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with open(temp_path, 'rb') as file:
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pdf_bytes = file.read()
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# Clean up the temporary file
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os.unlink(temp_path)
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return pdf_bytes
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def format_conversation_history(history, patient_info=None):
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"""
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Format the conversation history into a string suitable for LLM processing.
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Optionally adds patient info at the beginning.
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"""
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formatted_text = "# Medical Consultation\n\n"
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# Add patient info if provided
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if patient_info:
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formatted_text += "## Patient Information\n"
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formatted_text += f"* Name: {patient_info.get('name', '')}\n"
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formatted_text += f"* Age: {patient_info.get('age', '')}\n"
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formatted_text += f"* Gender: {patient_info.get('gender', '')}\n\n"
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formatted_text += "## Conversation Transcript\n\n"
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for message in history:
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role = message.get("role", "").strip()
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content = message.get("content", "").strip()
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if not content:
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continue # Skip empty messages
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if role.lower() == "user":
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formatted_text += f"PATIENT: {content}\n\n"
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elif role.lower() == "assistant":
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formatted_text += f"ASSISTANT: {content}\n\n"
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# Include explanations which often contain diagnostic reasoning
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if "explanation" in message and message["explanation"]:
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explanation = message.get("explanation", "").strip()
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if explanation:
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formatted_text += f"REASONING: {explanation}\n\n"
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return formatted_text
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# Function to handle the report generation process
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def generate_and_download_report():
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# Store the current conversation step
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if 'report_step' not in st.session_state:
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st.session_state.report_step = 1
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st.session_state.patient_info = {"name": "", "age": "", "gender": ""}
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st.session_state.pdf_data = None # Store PDF data for email
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# Step 1: Collect patient name
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if st.session_state.report_step == 1:
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name = st.text_input("Patient Name:")
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if st.button("Next"):
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st.session_state.patient_info["name"] = name
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st.session_state.report_step = 2
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st.rerun()
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# Step 2: Collect age
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elif st.session_state.report_step == 2:
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age = st.text_input("Patient Age:")
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if st.button("Next"):
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st.session_state.patient_info["age"] = age
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st.session_state.report_step = 3
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st.rerun()
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# Step 3: Collect gender
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elif st.session_state.report_step == 3:
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gender = st.selectbox("Patient Gender:", ["Male", "Female", "Other", "Prefer not to say"])
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if st.button("Generate Report"):
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st.session_state.patient_info["gender"] = gender
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st.session_state.report_step = 4
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st.rerun()
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# Step 4: Generate report
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elif st.session_state.report_step == 4:
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with st.spinner("Generating medical report..."):
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# Format conversation with patient info
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conversation_text = format_conversation_history(
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st.session_state.history,
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st.session_state.patient_info
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)
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# Extract structured data
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report_json = extract_medical_json(conversation_text)
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# Override with collected patient info
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report_json["patient"] = st.session_state.patient_info
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report_json["visit_date"] = datetime.now().strftime("%Y-%m-%d")
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# Generate PDF
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pdf_bytes = build_medical_report(report_json)
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st.session_state.pdf_data = pdf_bytes # Store PDF data for email
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# Display success message
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st.success("Your medical report is ready!")
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# Create two columns for the download and email buttons
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col1, col2 = st.columns(2)
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with col1:
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# Offer download
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st.download_button(
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label="📥 Download Report",
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data=pdf_bytes,
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file_name=f"medical_report_{datetime.now().strftime('%Y%m%d')}.pdf",
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mime="application/pdf",
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key="report_download"
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)
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with col2:
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# Email button that shows email form when clicked
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if st.button("📧 Email Report"):
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st.session_state.show_email_form = True
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st.rerun()
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# Show email form if the button was clicked
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if st.session_state.get('show_email_form', False):
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st.subheader("Send Report via Email")
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# Check SendGrid configuration
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sendgrid_api_key = os.getenv('SENDGRID_API_KEY')
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if not sendgrid_api_key:
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st.warning("Email service not configured. Please contact administrator.")
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# Email form
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email = st.text_input("Recipient Email Address:")
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# Email send button
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if st.button("Send"):
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if not email:
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st.error("Please enter an email address.")
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else:
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# Validate email
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sendgrid = SendGridService()
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if not sendgrid.validate_email(email):
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st.error("Please enter a valid email address.")
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else:
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# Send email
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with st.spinner("Sending email... This may take a few seconds."):
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success, message = sendgrid.send_report(
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email,
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st.session_state.pdf_data,
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st.session_state.patient_info["name"]
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)
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if success:
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st.success(message)
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# Hide the form after successful send
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st.session_state.show_email_form = False
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st.rerun()
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else:
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st.error(message)
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829 |
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# Cancel button to hide email form
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if st.button("Cancel"):
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st.session_state.show_email_form = False
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st.rerun()
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# Button to start over
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if st.button("Generate Another Report"):
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st.session_state.report_step = 1
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837 |
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st.session_state.show_email_form = False
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st.rerun()
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839 |
-
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# Put the toggle in the sidebar - this is the most reliable approach
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with st.sidebar:
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st.header("Features")
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parse_doctor_response
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)
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import base64
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from sendgrid_service import SendGridService
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from report_generator import (
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extract_medical_json,
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build_medical_report,
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format_conversation_history,
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generate_and_download_report,
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show_email_form
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)
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# Set page config with dark theme
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user_avatar = f"data:image/png;base64,{user_logo_base64}" if user_logo_base64 else "👤"
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assistant_avatar = f"data:image/png;base64,{assistant_logo_base64}" if assistant_logo_base64 else "🤖"
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532 |
# Put the toggle in the sidebar - this is the most reliable approach
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533 |
with st.sidebar:
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534 |
st.header("Features")
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