Update app.py

app.py

@@ -123,7 +123,8 @@ def json_to_markdown(data) -> str:
 
 
 # ---------- Processing Pipeline with Gradio Progress ----------
-def process_pipeline(pdf_file, hcc_code, model_version, csv_path=CSV_PATH, output_folder="outputs", progress=gr.Progress()):
+# --- MODIFICATION: Removed the `progress=gr.Progress()` argument ---
+def process_pipeline(pdf_file, hcc_code, model_version, csv_path=CSV_PATH, output_folder="outputs"):
     try:
         start = time.time()
         step = 0
@@ -138,7 +139,7 @@ def process_pipeline(pdf_file, hcc_code, model_version, csv_path=CSV_PATH, outpu
                 else: color = "#e5e7eb"
                 bar_html += f'<div style="flex-grow: 1; background-color: {color}; border-radius: 3px;"></div>'
             bar_html += '</div>'
-            return f"{msg}{bar_html}<small>⏳ Elapsed: {elapsed:.1f} sec</small>"
+            return f"<div style='padding-top:10px;'>{msg}{bar_html}<small>⏳ Elapsed: {elapsed:.1f} sec</small></div>"
 
         if pdf_file is None:
             yield log("⚠️ Please upload a patient chart PDF.", 0)
@@ -153,16 +154,15 @@ def process_pipeline(pdf_file, hcc_code, model_version, csv_path=CSV_PATH, outpu
         file_name = os.path.splitext(os.path.basename(pdf_path))[0]
         print(f"[PROCESSING] {file_name}")
 
-        # --- MODIFICATION: Extract and show demographics first ---
         # Step 1: Extract Demographics
         step += 1
-        progress((step, total_steps), desc="Extracting demographics")
-        yield log(f"🧍 Step {step}/{total_steps}: Extracting patient demographics...", step)
+        # --- MODIFICATION: Removed progress() call ---
+        initial_progress_msg = log(f"🧍 Step {step}/{total_steps}: Extracting patient demographics...", step)
+        yield initial_progress_msg # Show initial message before demographics appear
         demographics_engine = PatientInfoExtractionEngine(pdf_path)
         demographics_info = demographics_engine.run()
         print(f"[DEMOGRAPHICS] Extracted: {demographics_info}")
 
-        # Create and yield the initial report with only demographics
         initial_report_data = {
             "file_name": file_name,
             "hcc_code": hcc_code_str,
@@ -172,14 +172,10 @@ def process_pipeline(pdf_file, hcc_code, model_version, csv_path=CSV_PATH, outpu
         }
         demographics_md = json_to_markdown(initial_report_data)
         yield demographics_md
-        time.sleep(1) # Pause for a moment to show the demographics
-        # --- END OF MODIFICATION ---
-        
-        # Subsequent steps will append progress below the initial demographic display
-        
+        time.sleep(0.5)
+
         # Step 2: Diagnoses
         step += 1
-        progress((step, total_steps), desc="Extracting diagnoses")
         yield demographics_md + log(f"🔍 Step {step}/{total_steps}: Extracting possible HCC Diagnoses...", step)
         diagnoses = HCCDiagnosisListEngine(hcc_code_str, model_version, csv_path).run()
         if not diagnoses:
@@ -188,7 +184,6 @@ def process_pipeline(pdf_file, hcc_code, model_version, csv_path=CSV_PATH, outpu
 
         # Step 3: Chart checking
         step += 1
-        progress((step, total_steps), desc="Checking chart")
         yield demographics_md + log(f"📄 Step {step}/{total_steps}: Checking diagnoses in patient chart...", step)
         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"]
@@ -198,20 +193,17 @@ def process_pipeline(pdf_file, hcc_code, model_version, csv_path=CSV_PATH, outpu
 
         # Step 4: Tests
         step += 1
-        progress((step, total_steps), desc="Finding tests")
         yield demographics_md + log(f"🧪 Step {step}/{total_steps}: Finding relevant tests...", step)
         diagnoses_with_tests = TestFindingAgent(hcc_code=hcc_code_str, model_version=model_version).run(confirmed_diagnoses)
 
         # Step 5: Clinical Status
         step += 1
-        progress((step, total_steps), desc="Determining clinical status")
         yield demographics_md + log(f"⚕️ Step {step}/{total_steps}: Determining clinical status...", step)
         diagnoses_with_status = ClinicalStatusAgent().run(diagnoses_with_tests)
         active_diagnoses = [d for d in diagnoses_with_status if d.get("clinical_status") == "ACTIVE"]
 
         # Step 6: MEAT
         step += 1
-        progress((step, total_steps), desc="Validating MEAT")
         if active_diagnoses:
             yield demographics_md + log(f"🍖 Step {step}/{total_steps}: Validating MEAT...", step)
             validated_meat_diagnoses = MEATValidatorAgent().run(active_diagnoses)
@@ -221,7 +213,6 @@ def process_pipeline(pdf_file, hcc_code, model_version, csv_path=CSV_PATH, outpu
 
         # Step 7: 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 demographics_md + log(f"🤝 Step {step}/{total_steps}: Checking comorbidities...", step)
@@ -231,7 +222,6 @@ def process_pipeline(pdf_file, hcc_code, model_version, csv_path=CSV_PATH, outpu
 
         # Step 8: Final Report
         step += 1
-        progress((step, total_steps), desc="Generating report")
         yield demographics_md + log(f"✅ Step {step}/{total_steps}: Generating final report...", step)
 
         # Merge results for final output
@@ -364,6 +354,352 @@ if __name__ == "__main__":
 # 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):
+#             file_name = data.get("file_name", "Unknown Patient")
+#             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", "")
+#         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>🧾 File Name:</b> {file_name}</p>
+#   <p><b>🏷️ HCC Code:</b> {hcc_code}</p>
+#   <p><b>⚙️ Model Version:</b> {model_version}</p>
+# """
+
+#         # 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 if they exist
+#         if analyses:
+#             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 = 8  # Total number of steps in the pipeline
+
+#         def log(msg, current_step=0):
+#             elapsed = time.time() - start
+#             bar_html = '<div style="display: flex; width: 100%; gap: 2px; height: 12px; margin: 8px 0 5px 0;">'
+#             for i in range(1, total_steps + 1):
+#                 if i < current_step: color = "#1e40af"
+#                 elif i == current_step: color = "#3b82f6"
+#                 else: color = "#e5e7eb"
+#                 bar_html += f'<div style="flex-grow: 1; background-color: {color}; border-radius: 3px;"></div>'
+#             bar_html += '</div>'
+#             return f"{msg}{bar_html}<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
+#         file_name = os.path.splitext(os.path.basename(pdf_path))[0]
+#         print(f"[PROCESSING] {file_name}")
+
+#         # --- MODIFICATION: Extract and show demographics first ---
+#         # Step 1: Extract Demographics
+#         step += 1
+#         progress((step, total_steps), desc="Extracting demographics")
+#         yield log(f"🧍 Step {step}/{total_steps}: Extracting patient demographics...", step)
+#         demographics_engine = PatientInfoExtractionEngine(pdf_path)
+#         demographics_info = demographics_engine.run()
+#         print(f"[DEMOGRAPHICS] Extracted: {demographics_info}")
+
+#         # Create and yield the initial report with only demographics
+#         initial_report_data = {
+#             "file_name": file_name,
+#             "hcc_code": hcc_code_str,
+#             "model_version": model_version,
+#             "final_analysis": [],
+#             **demographics_info
+#         }
+#         demographics_md = json_to_markdown(initial_report_data)
+#         yield demographics_md
+#         time.sleep(1) # Pause for a moment to show the demographics
+#         # --- END OF MODIFICATION ---
+        
+#         # Subsequent steps will append progress below the initial demographic display
+        
+#         # Step 2: Diagnoses
+#         step += 1
+#         progress((step, total_steps), desc="Extracting diagnoses")
+#         yield demographics_md + log(f"🔍 Step {step}/{total_steps}: Extracting possible HCC Diagnoses...", step)
+#         diagnoses = HCCDiagnosisListEngine(hcc_code_str, model_version, csv_path).run()
+#         if not diagnoses:
+#             yield demographics_md + log(f"❌ No diagnoses found for HCC {hcc_code_str}.", step)
+#             return
+
+#         # Step 3: Chart checking
+#         step += 1
+#         progress((step, total_steps), desc="Checking chart")
+#         yield demographics_md + log(f"📄 Step {step}/{total_steps}: Checking diagnoses in patient chart...", step)
+#         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 demographics_md + log(f"❌ No confirmed diagnoses for HCC {hcc_code_str} in {file_name}.", step)
+#             return
+
+#         # Step 4: Tests
+#         step += 1
+#         progress((step, total_steps), desc="Finding tests")
+#         yield demographics_md + log(f"🧪 Step {step}/{total_steps}: Finding relevant tests...", step)
+#         diagnoses_with_tests = TestFindingAgent(hcc_code=hcc_code_str, model_version=model_version).run(confirmed_diagnoses)
+
+#         # Step 5: Clinical Status
+#         step += 1
+#         progress((step, total_steps), desc="Determining clinical status")
+#         yield demographics_md + log(f"⚕️ Step {step}/{total_steps}: Determining clinical status...", step)
+#         diagnoses_with_status = ClinicalStatusAgent().run(diagnoses_with_tests)
+#         active_diagnoses = [d for d in diagnoses_with_status if d.get("clinical_status") == "ACTIVE"]
+
+#         # Step 6: MEAT
+#         step += 1
+#         progress((step, total_steps), desc="Validating MEAT")
+#         if active_diagnoses:
+#             yield demographics_md + log(f"🍖 Step {step}/{total_steps}: Validating MEAT...", step)
+#             validated_meat_diagnoses = MEATValidatorAgent().run(active_diagnoses)
+#         else:
+#             validated_meat_diagnoses = []
+#             yield demographics_md + log("ℹ️ No ACTIVE diagnoses found. Skipping MEAT/Comorbidity.", step)
+
+#         # Step 7: 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 demographics_md + log(f"🤝 Step {step}/{total_steps}: Checking comorbidities...", step)
+#             comorbidity_results = ComorbidityCheckerAgent(pdf_path, hcc_code_str, model_version).run(diagnoses_passed_meat)
+#         else:
+#             comorbidity_results = []
+
+#         # Step 8: Final Report
+#         step += 1
+#         progress((step, total_steps), desc="Generating report")
+#         yield demographics_md + log(f"✅ Step {step}/{total_steps}: Generating final report...", step)
+
+#         # Merge results for final output
+#         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 = {
+#             "file_name": file_name,
+#             "hcc_code": hcc_code_str,
+#             "model_version": model_version,
+#             "final_analysis": filtered_final_analysis,
+#             **demographics_info
+#         }
+
+#         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 and Helpers ----------
+# 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",
+# )
+
+# 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)
+
+# 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>",
+#             )
+
+#     pdf_upload.change(fn=pdf_to_iframe, inputs=pdf_upload, outputs=pdf_preview)
+
+#     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))
+#     )
+
+
+# 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
+
+# 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: