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.env

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+GROQ_API_KEY = "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"  # <-- PASTE YOUR GROQ API KEY HERE
+# .env file
+# Paste your secret API keys here
+
+GROQ_API_KEY="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"
+GOOGLE_API_KEY="AIzaSyDaWTMQ3xQTFIDJ5YLJZaq67AMMKRdj76k"
+GOOGLE_DOCTOR_API_KEY="AIzaSyAAmC656joB1x5534GnQ3mMcjg7bkdPRbc"
+
+TOGETHER_API_KEY = "tgp_v1_dRxzBz77bgEiTVDqDli3KjVZ5rjhIXbE1k4l9GBBzu4" 

app.py.py

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+import gradio as gr
+import pandas as pd
+import asyncio
+import gspread
+from google.oauth2.service_account import Credentials
+from gradio.themes.base import Base
+import os
+import google.generativeai as genai
+from PIL import Image
+import traceback
+import time
+import re
+
+# --- PDF Generation Imports ---
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image as RLImage, PageBreak
+from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
+from reportlab.lib.units import inch
+from reportlab.lib.colors import navy, black, dimgrey
+from reportlab.lib.enums import TA_CENTER, TA_JUSTIFY
+
+# ==============================================================================
+# 1. AUTHENTICATION & CONFIGURATION
+# ==============================================================================
+
+MAX_IMAGES = 5
+SHEET_COLUMN_MAP = {
+    "image1_summary": "J", "image2_summary": "K", "image3_summary": "L",
+    "image4_summary": "M", "image5_summary": "N", "executive_summary": "O"
+}
+
+# --- Google Sheets ---
+GOOGLE_SHEETS_CREDS_PATH = os.getenv("GOOGLE_SHEETS_CREDS_PATH", "dbott-464906-c46c8756b829.json")
+is_sheets_authenticated = False
+try:
+    SCOPES = ["https://www.googleapis.com/auth/spreadsheets", "https://www.googleapis.com/auth/drive"]
+    creds = Credentials.from_service_account_file(GOOGLE_SHEETS_CREDS_PATH, scopes=SCOPES)
+    gc = gspread.authorize(creds)
+    sh = gc.open("PatientData")
+    ws = sh.get_worksheet(0)
+    print("✅ Google Sheets authenticated successfully.")
+    is_sheets_authenticated = True
+except Exception as e:
+    print(f"⚠️ Could not authenticate with Google Sheets: {e}. Using offline fallback data.")
+    ws = pd.DataFrame({
+        "abha_id": ["12345678901233"], "full_name": ["Pashwiwi Sharma"], "Age": [22], "weight_kg": ["64"],
+        "reason_for_visit": ["Allergy on right hand..."], "allergies": ["Pollen"],
+        "Medication": ["None"], "symptoms_description": ["Unsure of cause..."],
+        "Summary": ["Patient presents with an acute allergic reaction..."]
+    })
+
+# --- Gemini API ---
+is_gemini_configured = False
+try:
+    GOOGLE_API_KEY = "AIzaSyDO6o5B2u5WEMXqR7a-xnGorfzt64Vjq14"  # <- Replace with actual API key
+    genai.configure(api_key=GOOGLE_API_KEY)
+    gemini_model = genai.GenerativeModel('gemini-1.5-flash-latest')
+    print("✅ Gemini API configured successfully.")
+    is_gemini_configured = True
+except Exception as e:
+    print(f"⚠️ Could not configure Gemini API: {e}. AI features will be disabled.")
+    gemini_model = None
+
+# ==============================================================================
+# 2. SYSTEM PROMPTS
+# ==============================================================================
+SYSTEM_PROMPT_IMAGE_ANALYSIS = """
+You are a highly skilled medical imaging expert AI. Analyze the provided medical image, prescription, or report and structure your response according to the following points using clear markdown formatting.
+0. **Report Information** (if applicable): Doctor/Clinic Name, Date, Hospital/Facility, Patient Details (Age, Sex, etc.) if visible.
+1. **Image Type & Region**: Modality (X-ray, MRI, CT, Ultrasound, Photo, etc.), anatomical region, and positioning.
+2. **Key Findings**: Systematically list primary observations and potential abnormalities with detailed descriptions.
+3. **Diagnostic Assessment**: Provide a primary assessment or impression. List differential diagnoses if applicable. Highlight any critical/urgent findings.
+4. **Patient-Friendly Explanation**: Simplify the findings in clear, non-technical language.
+---
+***Disclaimer:** This AI-generated analysis is for informational purposes only and is NOT a substitute for professional medical advice, diagnosis, or treatment. A qualified healthcare professional must perform the final interpretation.*
+"""
+
+SYSTEM_PROMPT_DETAILED_REPORT = """You are an expert medical scribe AI. Your task is to create a single, comprehensive, and data-rich patient report by synthesizing all the provided information.
+**Your Goal:** Weave the patient's demographics, their past medical summary, the reason for their current visit, and the findings from new medical images into a cohesive and professional narrative.
+**Required Structure:** Generate the report in Markdown format using the exact following headings:
+### Patient Information
+(Summarize the patient's key demographic details: ABHA ID, Name, Age, Weight.)
+### Medical History & Previous Summary
+(Detail the patient's known allergies, current medications, and the summary from their previous visits. This provides historical context.)
+### Current Visit Details
+(Describe the primary reason for the current visit and the specific symptoms the patient is experiencing now.)
+### Comprehensive Image Analysis
+(Integrate the findings from all the provided image analyses. For each image, present its key findings and diagnostic assessment in a clear, organized manner. If there are multiple images, address each one.)
+### Overall Synthesis & Impression
+(This is the most important section. Provide a concise, professional synthesis that connects the dots. Correlate the patient's history and current symptoms with the new findings from the image analysis. Formulate a concluding impression based on the totality of the information.)
+"""
+
+# ==============================================================================
+# 3. PDF GENERATION ENGINE
+# ==============================================================================
+def create_report_pdf(markdown_text, image_paths, image_analyses):
+    # This function remains the same.
+    try:
+        pdf_path = f"temp_report_{int(time.time())}.pdf"
+        doc = SimpleDocTemplate(pdf_path, pagesize=(8.5 * inch, 11 * inch), topMargin=0.75*inch, bottomMargin=0.75*inch, leftMargin=0.75*inch, rightMargin=0.75*inch)
+        styles = getSampleStyleSheet()
+        styles.add(ParagraphStyle(name='TitleStyle', fontName='Helvetica-Bold', fontSize=18, alignment=TA_CENTER, textColor=navy, spaceAfter=24))
+        styles.add(ParagraphStyle(name='HeadingStyle', fontName='Helvetica-Bold', fontSize=14, textColor=navy, spaceBefore=12, spaceAfter=6))
+        styles.add(ParagraphStyle(name='Justify', parent=styles['Normal'], alignment=TA_JUSTIFY))
+        styles.add(ParagraphStyle(name='BulletStyle', parent=styles['Justify'], leftIndent=20, spaceAfter=4))
+        styles.add(ParagraphStyle(name='ImageTitle', parent=styles['Normal'], alignment=TA_CENTER, spaceBefore=18, spaceAfter=4, fontName='Helvetica-Bold'))
+        styles.add(ParagraphStyle(name='ImageCaption', parent=styles['Normal'], alignment=TA_CENTER, spaceAfter=12, fontName='Helvetica-Oblique', textColor=dimgrey, fontSize=9))
+
+        story = [Paragraph("Comprehensive Medical Report", styles['TitleStyle'])]
+
+        for line in markdown_text.split('\n'):
+            line = line.strip()
+            if not line: continue
+            line = re.sub(r'\*\*(.*?)\*\*', r'<b>\1</b>', line)
+            if line.startswith('### '):
+                story.append(Paragraph(line.replace('### ', ''), styles['HeadingStyle']))
+            elif line.startswith('* '):
+                story.append(Paragraph(f"• {line.replace('* ', '', 1)}", styles['BulletStyle']))
+            else:
+                story.append(Paragraph(line, styles['Justify']))
+
+        if image_paths and image_analyses:
+            story.append(PageBreak())
+            story.append(Paragraph("Appendix: Medical Images & Findings", styles['HeadingStyle']))
+            
+            for i, img_path in enumerate(image_paths):
+                if i < len(image_analyses):
+                    analysis_text = image_analyses[i]
+                    caption_text = "No specific assessment found."
+                    assessment_match = re.search(r"3\.\s*\*\*Diagnostic Assessment\*\*\n(.*?)(?=\n\n|\n4\.|\Z)", analysis_text, re.DOTALL | re.IGNORECASE)
+                    if assessment_match:
+                        caption_text = assessment_match.group(1).strip()
+                    else:
+                        findings_match = re.search(r"2\.\s*\*\*Key Findings\*\*\n(.*?)(?=\n\n|\n3\.|\Z)", analysis_text, re.DOTALL | re.IGNORECASE)
+                        if findings_match:
+                            caption_text = findings_match.group(1).strip()
+                    
+                    story.append(Paragraph(f"Image {i+1}", styles['ImageTitle']))
+                    story.append(Paragraph(f"<i>Summary: {caption_text}</i>", styles['ImageCaption']))
+                    try:
+                        img = RLImage(img_path, width=5.5*inch, height=5.5*inch, kind='proportional')
+                        img.hAlign = 'CENTER'
+                        story.append(img)
+                    except Exception as img_e:
+                        traceback.print_exc()
+                        story.append(Paragraph(f"<i>Error: Could not display image {i+1}.</i>", styles['Normal']))
+        
+        doc.build(story)
+        return pdf_path
+    except Exception as e:
+        traceback.print_exc()
+        return None
+
+# ==============================================================================
+# 4. CORE LOGIC
+# ==============================================================================
+
+async def update_google_sheet(abha_id, report_text, *image_analyses):
+    if not is_sheets_authenticated:
+        gr.Warning("Google Sheets not authenticated. Skipping database update.")
+        return "Could not update Sheet: Authentication failed."
+    try:
+        print(f"Attempting to update Google Sheet for ABHA ID: {abha_id}")
+        cell = ws.find(abha_id, in_column=1) # Search only in the first column for efficiency
+        if not cell:
+            gr.Warning(f"ABHA ID {abha_id} not found in Sheet. Skipping database update.")
+            return f"Could not update Sheet: ABHA ID {abha_id} not found."
+        
+        row_number = cell.row
+        updates_to_make = []
+
+        # Prepare image analysis updates
+        for i, analysis in enumerate(image_analyses):
+            if i >= MAX_IMAGES: break
+            col_name = f"image{i+1}_summary"
+            if analysis and "Pending" not in analysis and "Failed" not in analysis:
+                col_letter = SHEET_COLUMN_MAP[col_name]
+                updates_to_make.append({'range': f'{col_letter}{row_number}', 'values': [[analysis]]})
+        
+        # Prepare executive summary update
+        col_letter = SHEET_COLUMN_MAP["executive_summary"]
+        updates_to_make.append({'range': f'{col_letter}{row_number}', 'values': [[report_text]]})
+
+        if updates_to_make:
+            ws.batch_update(updates_to_make)
+            print(f"✅ Successfully updated row {row_number} for ABHA ID: {abha_id}")
+            return "✅ Database update complete."
+        else:
+            return "No new data to update in the database."
+    except Exception as e:
+        print(f"❌ FAILED to update Google Sheet: {e}")
+        traceback.print_exc()
+        gr.Error(f"Failed to update Google Sheet: {e}")
+        return "❌ Database update failed. See console for details."
+
+
+# **ROBUST FIX APPLIED HERE**
+async def fetch_patient_data(abha_id):
+    placeholder_demographics = "*Patient details will appear here.*"
+    placeholder_summary = "*Patient history will appear here.*"
+    if not abha_id:
+        return placeholder_demographics, placeholder_summary
+
+    try:
+        if is_sheets_authenticated:
+            # ROBUST METHOD: Use get_all_values() to avoid header errors.
+            all_values = ws.get_all_values()
+            if len(all_values) < 2:
+                return "Spreadsheet has no data records.", ""
+            
+            # Manually construct the DataFrame
+            headers = all_values[0]
+            data = all_values[1:]
+            df = pd.DataFrame(data, columns=headers)
+        else:
+            df = ws
+
+        df["abha_id"] = df["abha_id"].astype(str).str.strip()
+        row = df[df["abha_id"] == abha_id.strip()]
+        
+        if row.empty:
+            return f"**Status:** No record found for ABHA ID: `{abha_id}`", ""
+        
+        record = row.iloc[0]
+        patient_info_md = f"""
+**ABHA ID:** {record.get('abha_id', 'N/A')}
+**Name:** {record.get('full_name', 'N/A')}
+**Age:** {record.get('Age', 'N/A')}
+**Weight:** {record.get('weight_kg', 'N/A')} kg
+
+---
+**Reason for Visit:**
+{record.get('reason_for_visit', 'N/A')}
+
+**Symptoms:**
+{record.get('symptoms_description', 'N/A')}
+"""
+        summary_text = f"""
+**Known Allergies:**
+{record.get('allergies', 'N/A')}
+
+**Current Medications:**
+{record.get('Medication', 'N/A')}
+
+---
+**Previous Visit Summary:**
+{record.get('Summary', 'No previous summary available.')}
+"""
+        return patient_info_md.strip(), summary_text.strip()
+    except Exception as e:
+        traceback.print_exc()
+        # Provide a more specific error message if it's the GSpreadException
+        if "GSpreadException" in str(e):
+             return ("**Error:** Could not read the spreadsheet. Please ensure the first row has unique, non-empty headers for all columns.", "")
+        return f"**Error:** An error occurred while fetching data: {e}", ""
+
+
+async def analyze_images_on_upload(files):
+    # This function remains the same.
+    gallery_update = gr.update(value=None, visible=False)
+    row_updates = [gr.update(visible=False)] * MAX_IMAGES
+    image_updates = [gr.update(value=None)] * MAX_IMAGES
+    markdown_updates = [gr.update(value="")] * MAX_IMAGES
+    
+    if not files:
+        yield (gallery_update, *row_updates, *image_updates, *markdown_updates)
+        return
+
+    if len(files) > MAX_IMAGES:
+        gr.Warning(f"Max {MAX_IMAGES} images allowed. Analyzing the first {MAX_IMAGES}.")
+        files = files[:MAX_IMAGES]
+
+    filepaths = [f.name for f in files]
+    gallery_update = gr.update(value=filepaths, visible=True)
+
+    for i in range(MAX_IMAGES):
+        if i < len(files):
+            row_updates[i] = gr.update(visible=True)
+            image_updates[i] = gr.update(value=filepaths[i])
+            markdown_updates[i] = gr.update(value="⌛ Pending analysis...")
+        else:
+            row_updates[i] = gr.update(visible=False)
+            image_updates[i] = gr.update(value=None)
+            markdown_updates[i] = gr.update(value="")
+            
+    yield (gallery_update, *row_updates, *image_updates, *markdown_updates)
+
+    if not is_gemini_configured:
+        for i in range(len(files)):
+             markdown_updates[i] = gr.update(value="### Analysis Disabled\nGemini API not configured.")
+        yield (gallery_update, *row_updates, *image_updates, *markdown_updates)
+        return
+
+    for i in range(len(files)):
+        markdown_updates[i] = gr.update(value=f"⏳ Analyzing Image {i+1}...")
+        yield (gallery_update, *row_updates, *image_updates, *markdown_updates)
+
+        try:
+            img = Image.open(filepaths[i])
+            response = await gemini_model.generate_content_async(
+                [SYSTEM_PROMPT_IMAGE_ANALYSIS, img],
+                generation_config=genai.GenerationConfig(temperature=0.1)
+            )
+            markdown_updates[i] = gr.update(value=response.text)
+        except Exception as e:
+            traceback.print_exc()
+            markdown_updates[i] = gr.update(value=f"### Analysis Failed\nAn error occurred: {e}")
+        
+        yield (gallery_update, *row_updates, *image_updates, *markdown_updates)
+
+
+async def generate_detailed_report(abha_id, uploaded_files, *image_analyses):
+    # This function remains the same.
+    yield "⏳ Generating report...", gr.update(visible=False), gr.update(visible=False, value="")
+
+    if not is_gemini_configured:
+        yield "### Report Generation Disabled\nGemini API not configured.", gr.update(visible=False), gr.update(visible=False, value="")
+        return
+
+    patient_info, visit_summary = await fetch_patient_data(abha_id)
+    if "No record found" in patient_info or "Error:" in patient_info:
+        yield "### Report Generation Failed\nPlease fetch a valid patient record first.", gr.update(visible=False), gr.update(visible=False, value="")
+        return
+
+    prompt_context = "Here is all the available information for a patient...\n"
+    prompt_context += f"## PATIENT DETAILS & CURRENT VISIT INFO:\n{patient_info}\n\n"
+    prompt_context += f"## PAST MEDICAL SUMMARY:\n{visit_summary}\n\n"
+    analysis_texts = [text for text in image_analyses if text and "Pending" not in text and "Failed" not in text]
+    if analysis_texts:
+        prompt_context += "## NEW IMAGE ANALYSIS FINDINGS:\n"
+        for i, text in enumerate(analysis_texts):
+            prompt_context += f"### Analysis of Image {i+1}\n{text}\n\n"
+    else:
+        prompt_context += "## NEW IMAGE ANALYSIS FINDINGS:\nNo successful image analyses were performed.\n\n"
+    
+    final_prompt = [SYSTEM_PROMPT_DETAILED_REPORT, prompt_context]
+
+    try:
+        response = await gemini_model.generate_content_async(final_prompt, generation_config=genai.GenerationConfig(temperature=0.4))
+        markdown_report = response.text
+        
+        valid_image_paths = [f.name for f in uploaded_files[:MAX_IMAGES]] if uploaded_files else []
+        pdf_path = create_report_pdf(markdown_report, valid_image_paths, analysis_texts)
+        pdf_update = gr.update(value=pdf_path, visible=True) if pdf_path else gr.update(visible=False)
+
+        yield markdown_report, pdf_update, gr.update(visible=True, value="🔄 Updating database...")
+
+        status_message = await update_google_sheet(abha_id, markdown_report, *analysis_texts)
+        
+        yield markdown_report, pdf_update, gr.update(visible=True, value=status_message)
+        await asyncio.sleep(3)
+        yield markdown_report, pdf_update, gr.update(visible=False)
+
+    except Exception as e:
+        traceback.print_exc()
+        yield f"### Report Generation Failed\nAn error occurred: {e}", gr.update(visible=False), gr.update(visible=False)
+
+
+# ==============================================================================
+# 5. GRADIO UI LAYOUT
+# ==============================================================================
+with gr.Blocks(theme=Base(), title="Advanced Medical Report Generator") as app:
+    # This section remains the same.
+    gr.Markdown("# Advanced Medical Report Generator")
+
+    with gr.Row():
+        abha_id_input = gr.Textbox(label="Enter Patient ABHA ID", scale=3)
+        fetch_button = gr.Button("Fetch Patient Details", variant="primary", scale=1)
+
+    with gr.Row(variant="panel"):
+        with gr.Column(scale=1):
+            with gr.Accordion("Patient Demographics & Current Visit", open=True):
+                patient_info_output = gr.Markdown("*Patient details will appear here.*")
+        with gr.Column(scale=1):
+            with gr.Accordion("Medical History & Visit Summary", open=True):
+                summary_output = gr.Markdown("*Patient history will appear here.*")
+
+    gr.Markdown("---")
+    
+    gr.Markdown("### 1. Upload Scans & View AI Analysis")
+    with gr.Column(variant="panel"):
+        image_uploader = gr.File(label=f"Upload up to {MAX_IMAGES} images", file_count="multiple", file_types=["image"])
+        image_gallery = gr.Gallery(label="Image Preview", visible=False, columns=5, height="auto")
+        
+        analysis_rows, analysis_images, analysis_markdowns = [], [], []
+        for i in range(MAX_IMAGES):
+            with gr.Row(visible=False, variant='panel') as row:
+                with gr.Column(scale=1, min_width=200):
+                    img = gr.Image(interactive=False, show_label=False)
+                with gr.Column(scale=2):
+                    md = gr.Markdown()
+                analysis_rows.append(row)
+                analysis_images.append(img)
+                analysis_markdowns.append(md)
+
+    gr.Markdown("---")
+    gr.Markdown("### 2. Generate Final Synthesized Report")
+    with gr.Column(variant='panel'):
+        generate_report_button = gr.Button("Generate Detailed Report & Update Database", variant="primary")
+        status_output = gr.Markdown(visible=False)
+        gr.Markdown("#### Report Preview")
+        report_preview_output = gr.Markdown("*Click the button above to generate a comprehensive, synthesized report.*")
+        download_report_button = gr.File(label="Download Report (PDF)", visible=False)
+
+# ==============================================================================
+# 6. EVENT LISTENERS
+# ==============================================================================
+    # This section remains the same.
+    fetch_button.click(
+        fn=fetch_patient_data,
+        inputs=[abha_id_input],
+        outputs=[patient_info_output, summary_output]
+    )
+
+    image_uploader.change(
+        fn=analyze_images_on_upload,
+        inputs=[image_uploader],
+        outputs=[image_gallery, *analysis_rows, *analysis_images, *analysis_markdowns]
+    )
+
+    generate_report_button.click(
+        fn=generate_detailed_report,
+        inputs=[
+            abha_id_input,
+            image_uploader,
+            *analysis_markdowns
+        ],
+        outputs=[report_preview_output, download_report_button, status_output]
+    )
+
+# ==============================================================================
+# 7. LAUNCH APP
+# ==============================================================================
+if __name__ == "__main__":
+    app.launch(share=True, debug=True)

categorizer_prompt.txt

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+You are a medical information categorization engine. Your sole purpose is to analyze a new piece of conversation and determine which section of a standard medical note it belongs to.
+
+**CRITICAL INSTRUCTIONS:**
+1.  Analyze the "NEW TEXT SNIPPET". Use the "FULL TRANSCRIPT HISTORY" for context if needed.
+2.  Identify which single medical note section the new information belongs to. The available sections are: `Patient Information`, `History of Present Illness (HPI)`, `Past Medical History (PMH)`, `Medication History`, `Allergies`.
+3.  Summarize the new piece of information into a single, concise bullet point.
+4.  You MUST respond in a strict JSON format. **Your response MUST be a JSON object with two keys: "section" and "content".** Do not respond with a plain string.
+5.  If the new text is conversational filler, a greeting, or does not belong in any medical section, you MUST return the JSON object: `{"section": "N/A", "content": ""}`.
+6.  Do not invent information. If the snippet is unclear, use the "N/A" response.
+
+**Example 1:**
+- NEW TEXT SNIPPET: "and the pain has been going on for about three days now"
+- YOUR RESPONSE: `{"section": "HPI", "content": "- Onset was three days ago."}`
+
+**Example 2:**
+- NEW TEXT SNIPPET: "I'm allergic to penicillin, it gives me hives"
+- YOUR RESPONSE: `{"section": "Allergies", "content": "- Penicillin (causes hives)."}`
+
+**Example 3:**
+- NEW TEXT SNIPPET: "okay thank you so much doc"
+- YOUR RESPONSE: `{"section": "N/A", "content": ""}`

dbott-464906-c46c8756b829.json

@@ -0,0 +1,13 @@
+{
+  "type": "service_account",
+  "project_id": "dbott-464906",
+  "private_key_id": "c46c8756b8299f42f67124c7004ef65e7b151a0a",
+  "private_key": "-----BEGIN PRIVATE KEY-----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\nEsxZ3XcbS36sHs7ofT/QG+k=\n-----END PRIVATE KEY-----\n",
+  "client_email": "spreedsheet1@dbott-464906.iam.gserviceaccount.com",
+  "client_id": "113104737724933717188",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/spreedsheet1%40dbott-464906.iam.gserviceaccount.com",
+  "universe_domain": "googleapis.com"
+}

doctor_prompt.txt

@@ -0,0 +1,11 @@
+You are a clinical decision support assistant for a doctor. Your role is to listen to the live transcript of a patient encounter and provide real-time, actionable insights.
+
+**CRITICAL INSTRUCTIONS:**
+1.  Analyze the **FULL CONVERSATION TRANSCRIPT**.
+2.  Your output MUST be in two distinct sections: "Suggested Next Steps" and "Key Questions Asked by Doctor".
+3.  Under "Suggested Next Steps," recommend potential actions, tests, or follow-ups based on the patient's symptoms and history (e.g., "Consider ordering a CBC," "Recommend a follow-up in 2 weeks").
+4.  Under "Key Questions Asked by Doctor," list the important diagnostic questions the doctor has asked the patient so far. This helps the doctor track what has been covered.
+5.  Be concise and to the point.
+6.  Format the output using clean Markdown (`###` for headers, `-` for bullets).
+7.  If there isn't enough information for a section, you can state "Awaiting more information..." under the relevant heading.
+8.  Your output MUST ONLY be the two-section Markdown note. Do not add any other text or explanations.

jarvis_command_prompt.txt

@@ -0,0 +1,8 @@
+You are a highly efficient command parser. Your sole function is to analyze a given text segment for the activation keyword "jarvis".
+
+**CRITICAL INSTRUCTIONS:**
+1.  Read the "TEXT SEGMENT TO ANALYZE".
+2.  If you find the keyword "jarvis" (case-insensitive), extract the specific and complete command or question that immediately follows it.
+3.  Your output MUST BE ONLY the extracted command text.
+4.  If the keyword "jarvis" is NOT present in the text, you MUST respond with the exact string: `[NO_COMMAND]`
+5.  Do not add any greetings, explanations, or introductory phrases like "The command is:".

jarvis_prompt.txt

@@ -0,0 +1,8 @@
+You are a highly efficient command parser. Your sole function is to analyze a given text segment for the activation keyword "jarvis".
+
+**CRITICAL INSTRUCTIONS:**
+1.  Read the "TEXT SEGMENT TO ANALYZE".
+2.  If you find the keyword "jarvis" (case-insensitive), extract the specific and complete command or question that immediately follows it.
+3.  Your output MUST BE ONLY the extracted command text.
+4.  If the keyword "jarvis" is NOT present in the text, you MUST respond with the exact string: `[NO_COMMAND]`
+5.  Do not add any greetings, explanations, or introductory phrases like "The command is:".

system_prompt.txt

@@ -0,0 +1,16 @@
+You are an expert medical scribe. Your task is to generate a structured clinical note from a real-time conversation transcript.
+
+**CRITICAL INSTRUCTIONS:**
+1.  Analyze the **FULL CONVERSATION TRANSCRIPT**.
+2.  Use the **PREVIOUS SUMMARY** as a reference to build upon. Do not repeat information.
+3.  **ONLY create a heading and its corresponding section if you find relevant information for it in the transcript.** DO NOT output empty sections or headers like "Past Medical History:" if there is no such information.
+4.  Format the output using clean and professional Markdown. Use `###` for main headings (e.g., `### History of Present Illness (HPI)`) and `-` for bullet points.
+5.  Keep the language concise and clinical.
+6.  Your output MUST ONLY be the Markdown-formatted note. Do not include any other text, greetings, or explanations.
+
+**Available Section Headings (Use only when applicable):**
+- Patient Information
+- History of Present Illness (HPI)
+- Past Medical History (PMH)
+- Medication History
+- Allergies