Update app.py

app.py

@@ -10,7 +10,7 @@ from ComorbidityCheckerAgent import ComorbidityCheckerAgent
 from HCCDiagnosisListEngine import HCCDiagnosisListEngine
 from chartdiagnosischecker import ChartDiagnosisChecker
 from MeatValidatorAgent import MEATValidatorAgent
-from typing import Optional
+from PatientInfoExtractionEngine import PatientInfoExtractionEngine
 
 load_dotenv()
 APP_TITLE = "Risk Adjustment (HCC Chart Validation)"
@@ -26,11 +26,22 @@ def json_to_markdown(data) -> str:
             hcc_code = data.get("hcc_code", "N/A")
             model_version = data.get("model_version", "N/A")
             analyses = data.get("final_analysis", [])
+
+            # Demographics
+            patient_name = data.get("patient_name", "")
+            dob = data.get("dob", "")
+            age = data.get("age", "")
+            gender = data.get("gender", "")
+            address = data.get("address", "")
+            phone = data.get("phone", "")
+            patient_identifier = data.get("patient_identifier", "")
+
         elif isinstance(data, list):
             patient_id = "N/A"
             hcc_code = "N/A"
             model_version = "N/A"
             analyses = data
+            patient_name = dob = age = gender = address = phone = patient_identifier = ""
         else:
             return "<div style='color:red; font-weight:bold;'>⚠️ Invalid data format for report.</div>"
 
@@ -40,9 +51,23 @@ def json_to_markdown(data) -> str:
   <p><b>🧾 Patient ID:</b> {patient_id}</p>
   <p><b>🏷️ HCC Code:</b> {hcc_code}</p>
   <p><b>⚙️ Model Version:</b> {model_version}</p>
-</div>
-<br/>
 """
+
+        # Add demographics
+        if any([patient_name, dob, age, gender, address, phone, patient_identifier]):
+            md += "<hr style='border:0;border-top:1px solid #c8e6c9;margin:10px 0;'/>"
+            md += "<h3 style='color:#1b5e20;'>👤 Patient Demographics</h3>"
+            if patient_name: md += f"<p><b>Name:</b> {patient_name}</p>"
+            if patient_identifier: md += f"<p><b>Patient Identifier:</b> {patient_identifier}</p>"
+            if dob: md += f"<p><b>Date of Birth:</b> {dob}</p>"
+            if age: md += f"<p><b>Age:</b> {age}</p>"
+            if gender: md += f"<p><b>Gender:</b> {gender}</p>"
+            if address: md += f"<p><b>Address:</b> {address}</p>"
+            if phone: md += f"<p><b>Phone:</b> {phone}</p>"
+
+        md += "</div><br/>"
+
+        # Render analyses
         for idx, diag in enumerate(analyses, 1):
             md += f"""
 <div style="border:1px solid #ccc; padding:12px; border-radius:8px; margin-bottom:12px;">
@@ -113,7 +138,7 @@ def process_pipeline(pdf_file, hcc_code, model_version, csv_path=CSV_PATH, outpu
     try:
         start = time.time()
         step = 0
-        total_steps = 7
+        total_steps = 8  # Step 0 added for demographics
 
         def log(msg, step_progress=None):
             elapsed = time.time() - start
@@ -139,6 +164,14 @@ def process_pipeline(pdf_file, hcc_code, model_version, csv_path=CSV_PATH, outpu
         patient_name = os.path.splitext(os.path.basename(pdf_path))[0]
         print(f"[PROCESSING] {patient_name}")
 
+        # Step 0: Extract Demographics
+        step += 1
+        progress((step, total_steps), desc="Extracting demographics")
+        yield log(f"🧍 Step {step}/{total_steps}: Extracting patient demographics...", step/total_steps)
+        demographics_engine = PatientInfoExtractionEngine(pdf_path)
+        demographics_info = demographics_engine.run()
+        print(f"[DEMOGRAPHICS] Extracted: {demographics_info}")
+
         # Step 1: Diagnoses
         step += 1
         progress((step, total_steps), desc="Extracting diagnoses")
@@ -223,10 +256,18 @@ def process_pipeline(pdf_file, hcc_code, model_version, csv_path=CSV_PATH, outpu
             or e.get("answer_implicit", "").lower() == "yes"
         ]
 
+        # Include demographics in output
         output_data = {
             "patient_id": patient_name,
             "hcc_code": hcc_code_str,
             "model_version": model_version,
+            "patient_name": demographics_info.get("name", ""),
+            "dob": demographics_info.get("dob", ""),
+            "age": demographics_info.get("age", ""),
+            "gender": demographics_info.get("gender", ""),
+            "address": demographics_info.get("address", ""),
+            "phone": demographics_info.get("phone", ""),
+            "patient_identifier": demographics_info.get("patient_identifier", ""),
             "final_analysis": filtered_final_analysis
         }
 
@@ -281,7 +322,6 @@ def pdf_to_iframe(file):
         return f"<p style='color:red;'>Failed to display PDF: {e}</p>"
 
 
-
 # ---------- Gradio UI ----------
 with gr.Blocks(theme=simple_theme, title=APP_TITLE) as interface:
     gr.HTML(f"""
@@ -306,10 +346,8 @@ with gr.Blocks(theme=simple_theme, title=APP_TITLE) as interface:
                 value="<div style='border:2px solid #1e40af; border-radius:12px; padding:15px; background-color:#f0f9ff;'>📄 Upload a PDF and click <b>Run Validation</b> to start.</div>",
             )
 
-    # Connect PDF upload to preview
     pdf_upload.change(fn=pdf_to_iframe, inputs=pdf_upload, outputs=pdf_preview)
 
-    # Connect run button with progress
     run_btn.click(
         fn=process_pipeline,
         inputs=[pdf_upload, hcc_code, model_version],
@@ -322,8 +360,7 @@ with gr.Blocks(theme=simple_theme, title=APP_TITLE) as interface:
         outputs=[output_md],
         fn=lambda x: process_pipeline(load_sample_pdf(), hcc_code="12", model_version="V24"),
         cache_examples=False
-   )
-
+    )
 
 
 if __name__ == "__main__":
@@ -333,3 +370,346 @@ if __name__ == "__main__":
     )
 
 
+
+
+
+
+
+
+
+# import os
+# import gradio as gr
+# import json
+# import time
+# import base64
+# from dotenv import load_dotenv
+# from ClinicalStatusAgent import ClinicalStatusAgent
+# from TestFindingAgent import TestFindingAgent
+# from ComorbidityCheckerAgent import ComorbidityCheckerAgent
+# from HCCDiagnosisListEngine import HCCDiagnosisListEngine
+# from chartdiagnosischecker import ChartDiagnosisChecker
+# from MeatValidatorAgent import MEATValidatorAgent
+# from PatientInfoExtractionEngine import PatientInfoExtractionEngine
+# from typing import Optional
+
+# load_dotenv()
+# APP_TITLE = "Risk Adjustment (HCC Chart Validation)"
+# CSV_PATH = "hcc_mapping.csv"
+# SAMPLE_PDF = "sample_patient_chart.pdf"  # Place a sample PDF in the same folder
+
+
+# # ---------- JSON to Markdown ----------
+# def json_to_markdown(data) -> str:
+#     try:
+#         if isinstance(data, dict) and "final_analysis" in data:
+#             patient_id = data.get("patient_id", "Unknown Patient")
+#             hcc_code = data.get("hcc_code", "N/A")
+#             model_version = data.get("model_version", "N/A")
+#             analyses = data.get("final_analysis", [])
+#         elif isinstance(data, list):
+#             patient_id = "N/A"
+#             hcc_code = "N/A"
+#             model_version = "N/A"
+#             analyses = data
+#         else:
+#             return "<div style='color:red; font-weight:bold;'>⚠️ Invalid data format for report.</div>"
+
+#         md = f"""
+# <div style="border:2px solid #4CAF50; padding:15px; border-radius:10px; background:#f9fdf9;">
+#   <h2 style="color:#2e7d32;">📋 HCC Chart Validation Report </h2>
+#   <p><b>🧾 Patient ID:</b> {patient_id}</p>
+#   <p><b>🏷️ HCC Code:</b> {hcc_code}</p>
+#   <p><b>⚙️ Model Version:</b> {model_version}</p>
+# </div>
+# <br/>
+# """
+#         for idx, diag in enumerate(analyses, 1):
+#             md += f"""
+# <div style="border:1px solid #ccc; padding:12px; border-radius:8px; margin-bottom:12px;">
+#   <h3 style="color:#1565c0;">{idx}. {diag.get("diagnosis", "Unknown Diagnosis")}</h3>
+#   <p><b>ICD-10:</b> {diag.get("icd10", "N/A")}</p>
+#   <p><b>Reference:</b> <a href="{diag.get("reference","")}" target="_blank">{diag.get("reference","")}</a></p>
+# """
+#             explicit_ans = diag.get("answer_explicit", "N/A")
+#             explicit_rat = diag.get("rationale_explicit", "")
+#             implicit_ans = diag.get("answer_implicit", "N/A")
+#             implicit_rat = diag.get("rationale_implicit", "")
+
+#             if explicit_ans.lower() == "yes":
+#                 md += f"<p><b>Explicit:</b> {explicit_ans} — {explicit_rat}</p>"
+#             else:
+#                 md += f"<p><b>Implicit:</b> {implicit_ans} — {implicit_rat}</p>"
+
+#             md += f"""
+#   <p><b>Clinical Status:</b> {diag.get("clinical_status","N/A")}</p>
+#   <p><b>Status Rationale:</b> {diag.get("status_rationale","")}</p>
+# """
+#             if "tests" in diag:
+#                 md += "<details><summary><b>🧪 Tests & Procedures</b></summary><ul>"
+#                 tests = diag["tests"]
+#                 if "vitals" in tests:
+#                     md += "<li><b>Vitals:</b><ul>"
+#                     for k, v in tests["vitals"].items():
+#                         md += f"<li>{k}: {v}</li>"
+#                     md += "</ul></li>"
+#                 if "procedures" in tests:
+#                     md += "<li><b>Procedures:</b><ul>"
+#                     for k, v in tests["procedures"].items():
+#                         md += f"<li>{k}: {v}</li>"
+#                     md += "</ul></li>"
+#                 if "lab_test" in tests:
+#                     md += "<li><b>Lab Tests:</b><ul>"
+#                     for k, v in tests["lab_test"].items():
+#                         md += f"<li>{k}: {v}</li>"
+#                     md += "</ul></li>"
+#                 md += "</ul></details>"
+
+#             if "meat" in diag:
+#                 md += "<details><summary><b>🍖 MEAT Validation</b></summary><ul>"
+#                 for k, v in diag["meat"].items():
+#                     emoji = "✅" if v else "❌"
+#                     md += f"<li>{k.capitalize()}: {emoji}</li>"
+#                 md += "</ul>"
+#                 md += f"<p><b>MEAT Rationale:</b> {diag.get('meat_rationale','')}</p>"
+#                 md += "</details>"
+
+#             if "comorbidities" in diag and diag["comorbidities"]:
+#                 md += "<details><summary><b>🩺 Comorbidities</b></summary><ul>"
+#                 for c in diag["comorbidities"]:
+#                     emoji = "✅" if c.get("is_present") else "❌"
+#                     md += f"<li>{emoji} <b>{c.get('condition')}</b><br/><i>{c.get('rationale')}</i></li>"
+#                 md += "</ul></details>"
+
+#             md += "</div>"
+
+#         return md
+
+#     except Exception as e:
+#         return f"<div style='color:red; font-weight:bold;'>⚠️ Error rendering report: {e}</div>"
+
+
+# # ---------- Processing Pipeline with Gradio Progress ----------
+# def process_pipeline(pdf_file, hcc_code, model_version, csv_path=CSV_PATH, output_folder="outputs", progress=gr.Progress()):
+#     try:
+#         start = time.time()
+#         step = 0
+#         total_steps = 7
+
+#         def log(msg, step_progress=None):
+#             elapsed = time.time() - start
+#             progress_html = ""
+#             if step_progress is not None:
+#                 progress_html = f"""
+#                 <div style="width:100%; background:#eee; border-radius:6px; margin:5px 0;">
+#                     <div style="width:{step_progress*100}%; background:#1e40af; height:12px; border-radius:6px;"></div>
+#                 </div>
+#                 """
+#             return f"{progress_html}{msg}<br/><small>⏳ Elapsed: {elapsed:.1f} sec</small>"
+
+#         if pdf_file is None:
+#             yield log("⚠️ Please upload a patient chart PDF.", 0)
+#             return
+#         hcc_code_str = str(hcc_code or "").strip()
+#         if not hcc_code_str:
+#             yield log("⚠️ Please enter a valid HCC Code before running validation.", 0)
+#             return
+
+#         os.makedirs(output_folder, exist_ok=True)
+#         pdf_path = pdf_file.name
+#         patient_name = os.path.splitext(os.path.basename(pdf_path))[0]
+#         print(f"[PROCESSING] {patient_name}")
+
+#         # Step 1: Diagnoses
+#         step += 1
+#         progress((step, total_steps), desc="Extracting diagnoses")
+#         yield log(f"🔍 Step {step}/{total_steps}: Extracting possible HCC Diagnoses...", step/total_steps)
+#         diagnoses = HCCDiagnosisListEngine(hcc_code_str, model_version, csv_path).run()
+#         if not diagnoses:
+#             yield log(f"❌ No diagnoses found for HCC {hcc_code_str}.", step/total_steps)
+#             return
+
+#         # Step 2: Chart checking
+#         step += 1
+#         progress((step, total_steps), desc="Checking chart")
+#         yield log(f"📄 Step {step}/{total_steps}: Checking diagnoses in patient chart...", step/total_steps)
+#         all_checked_results = ChartDiagnosisChecker(pdf_path).run(diagnoses)
+#         confirmed_diagnoses = [
+#             d for d in all_checked_results
+#             if d.get("answer_explicit", "").lower() == "yes"
+#             or d.get("answer_implicit", "").lower() == "yes"
+#         ]
+#         if not confirmed_diagnoses:
+#             yield log(f"❌ No confirmed diagnoses for HCC {hcc_code_str} in {patient_name}.", step/total_steps)
+#             return
+
+#         # Step 3: Tests
+#         step += 1
+#         progress((step, total_steps), desc="Finding tests")
+#         yield log(f"🧪 Step {step}/{total_steps}: Finding relevant tests...", step/total_steps)
+#         diagnoses_with_tests = TestFindingAgent(hcc_code=hcc_code_str, model_version=model_version).run(confirmed_diagnoses)
+
+#         # Step 4: Clinical Status
+#         step += 1
+#         progress((step, total_steps), desc="Determining clinical status")
+#         yield log(f"⚕️ Step {step}/{total_steps}: Determining clinical status...", step/total_steps)
+#         diagnoses_with_status = ClinicalStatusAgent().run(diagnoses_with_tests)
+#         active_diagnoses = [d for d in diagnoses_with_status if d.get("clinical_status") == "ACTIVE"]
+
+#         # Step 5: MEAT
+#         step += 1
+#         progress((step, total_steps), desc="Validating MEAT")
+#         if active_diagnoses:
+#             yield log(f"🍖 Step {step}/{total_steps}: Validating MEAT...", step/total_steps)
+#             validated_meat_diagnoses = MEATValidatorAgent().run(active_diagnoses)
+#         else:
+#             validated_meat_diagnoses = []
+#             yield log("ℹ️ No ACTIVE diagnoses found. Skipping MEAT/Comorbidity.", step/total_steps)
+
+#         # Step 6: Comorbidities
+#         step += 1
+#         progress((step, total_steps), desc="Checking comorbidities")
+#         diagnoses_passed_meat = [d for d in validated_meat_diagnoses if any(d.get("meat", {}).values())]
+#         if diagnoses_passed_meat:
+#             yield log(f"🤝 Step {step}/{total_steps}: Checking comorbidities...", step/total_steps)
+#             comorbidity_results = ComorbidityCheckerAgent(pdf_path, hcc_code_str, model_version).run(diagnoses_passed_meat)
+#         else:
+#             comorbidity_results = []
+
+#         # Step 7: Final Report
+#         step += 1
+#         progress((step, total_steps), desc="Generating report")
+#         yield log(f"✅ Step {step}/{total_steps}: Generating final report...", step/total_steps)
+
+#         # Merge results
+#         status_map = {d["diagnosis"]: d for d in diagnoses_with_status}
+#         meat_map = {d["diagnosis"]: d for d in validated_meat_diagnoses}
+#         comorbidity_map = {d["diagnosis"]: d for d in comorbidity_results}
+
+#         final_analysis = []
+#         for entry in all_checked_results:
+#             diag_name = entry["diagnosis"]
+#             updated_entry = entry.copy()
+#             if diag_name in status_map:
+#                 updated_entry.update(status_map[diag_name])
+#             if diag_name in meat_map:
+#                 updated_entry.update(meat_map[diag_name])
+#             if diag_name in comorbidity_map:
+#                 updated_entry.update(comorbidity_map[diag_name])
+#             final_analysis.append(updated_entry)
+
+#         filtered_final_analysis = [
+#             e for e in final_analysis
+#             if e.get("answer_explicit", "").lower() == "yes"
+#             or e.get("answer_implicit", "").lower() == "yes"
+#         ]
+
+#         output_data = {
+#             "patient_id": patient_name,
+#             "hcc_code": hcc_code_str,
+#             "model_version": model_version,
+#             "final_analysis": filtered_final_analysis
+#         }
+
+#         elapsed = time.time() - start
+#         yield json_to_markdown(output_data) + f"<br/><div style='color:green;'>✅ Completed in {elapsed:.1f} sec</div>"
+
+#     except Exception as e:
+#         print(f"[ERROR] {e}")
+#         yield f"<div style='color:red; font-weight:bold;'>⚠️ Error: {e}</div>"
+
+
+# # ---------- Gradio Theme ----------
+# simple_theme = gr.themes.Soft(
+#     primary_hue=gr.themes.colors.blue,
+#     secondary_hue=gr.themes.colors.slate,
+#     neutral_hue=gr.themes.colors.slate,
+# ).set(
+#     button_primary_background_fill="#1e40af",
+#     button_primary_background_fill_hover="#1d4ed8",
+#     button_primary_text_color="white",
+#     background_fill_primary="white",
+#     background_fill_secondary="#f8fafc",
+# )
+
+
+# # ---------- Helper for Sample PDF ----------
+# def load_sample_pdf():
+#     if not os.path.exists(SAMPLE_PDF):
+#         raise FileNotFoundError(f"Sample PDF not found at {SAMPLE_PDF}")
+#     class PDFWrapper:
+#         def __init__(self, path):
+#             self.name = path
+#     return PDFWrapper(SAMPLE_PDF)
+
+
+# # ---------- Helper for PDF Preview ----------
+# def pdf_to_iframe(file):
+#     if file is None:
+#         return "<p style='color:orange;'>No PDF uploaded.</p>"
+#     try:
+#         with open(file.name, "rb") as f:
+#             pdf_bytes = f.read()
+#         encoded = base64.b64encode(pdf_bytes).decode("utf-8")
+#         return f"""
+#         <iframe
+#             src="data:application/pdf;base64,{encoded}"
+#             width="100%" height="600px"
+#             style="border:1px solid #ccc;"
+#         ></iframe>
+#         """
+#     except Exception as e:
+#         return f"<p style='color:red;'>Failed to display PDF: {e}</p>"
+
+
+
+# # ---------- Gradio UI ----------
+# with gr.Blocks(theme=simple_theme, title=APP_TITLE) as interface:
+#     gr.HTML(f"""
+#         <h1 style='text-align:center;color:#1e40af;'> 🏩 {APP_TITLE}</h1>
+#         <p style='text-align:center;color:#64748b;'>
+#             Upload a chart, set HCC + model version, and validate MEAT criteria.
+#         </p>
+#     """)
+
+#     with gr.Row():
+#         pdf_upload = gr.File(label="Upload Patient Chart (PDF)", file_types=[".pdf"], scale=1)
+#         hcc_code = gr.Textbox(label="HCC Code (e.g., 12)", placeholder="Enter HCC code", scale=1)
+#         model_version = gr.Dropdown(choices=["V24", "V28"], label="Model Version", value="V24", scale=1)
+#         run_btn = gr.Button("🚀 Run Validation", variant="primary", scale=1)
+
+#     with gr.Row():
+#         with gr.Column(scale=2):
+#             pdf_preview = gr.HTML(label="📑 PDF Preview", value="<p>Upload a PDF to preview</p>")
+#         with gr.Column(scale=2):
+#             output_md = gr.Markdown(
+#                 label="Validation Report",
+#                 value="<div style='border:2px solid #1e40af; border-radius:12px; padding:15px; background-color:#f0f9ff;'>📄 Upload a PDF and click <b>Run Validation</b> to start.</div>",
+#             )
+
+#     # Connect PDF upload to preview
+#     pdf_upload.change(fn=pdf_to_iframe, inputs=pdf_upload, outputs=pdf_preview)
+
+#     # Connect run button with progress
+#     run_btn.click(
+#         fn=process_pipeline,
+#         inputs=[pdf_upload, hcc_code, model_version],
+#         outputs=[output_md],
+#     )
+
+#     gr.Examples(
+#         examples=[[SAMPLE_PDF]],
+#         inputs=[pdf_upload],
+#         outputs=[output_md],
+#         fn=lambda x: process_pipeline(load_sample_pdf(), hcc_code="12", model_version="V24"),
+#         cache_examples=False
+#    )
+
+
+
+# if __name__ == "__main__":
+#     interface.queue().launch(
+#         server_name="0.0.0.0",
+#         server_port=int(os.environ.get("PORT", 7860))
+#     )
+
+