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Dockerfile

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+# Use Python 3.11 for best library compatibility
+FROM python:3.13-slim
+
+WORKDIR /app  
+
+# Environment variables
+ENV DEBIAN_FRONTEND=noninteractive \
+    PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    TRANSFORMERS_CACHE=/app/cache \
+    HF_HOME=/app/cache \
+    NLTK_DATA=/app/nltk_data \
+    MPLCONFIGDIR=/app/.config/matplotlib
+
+# Copy files
+COPY requirements.txt requirements.txt 
+COPY static/ /app/static 
+COPY app.py app.py
+COPY medicationCategories/ /app/medicationCategories/ 
+COPY reports/ /app/reports/ 
+COPY . /app/
+
+# Install only the dependencies your app needs
+RUN apt-get update && apt-get install -y \
+    tesseract-ocr \
+    poppler-utils \
+    libgl1 \
+    curl \
+    ca-certificates \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Python packages
+RUN pip install --upgrade pip && pip install -r requirements.txt
+
+# Create app directories with permissions
+RUN mkdir -p /app/nltk_data /app/.config/matplotlib \
+ && mkdir -p /app/cache /app/data /app/logs /app/static /app/tmp \
+ && chmod -R 777 /app/cache /app/data /app/logs /app/static /app/medicationCategories /app/reports \
+ && chmod -R 777 /app
+
+# Flask environment
+ENV FLASK_APP=app.py \
+    FLASK_ENV=production
+
+EXPOSE 7860
+CMD ["python", "app.py"]

README.md

@@ -1,12 +1,12 @@
----
-title: Health Doc
-emoji: 🔥
-colorFrom: indigo
-colorTo: purple
-sdk: docker
-pinned: false
-license: unknown
-short_description: nothing
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Health Dac Assist
+emoji: ⚡
+colorFrom: pink
+colorTo: blue
+sdk: docker
+pinned: false
+license: other
+short_description: doctor assistance
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,668 @@
+#!/usr/bin/env python3
+# app.py - Health Reports processing agent (PDF -> cleaned text -> structured JSON)
+# nothing
+import os
+import json
+import logging
+import re
+from pathlib import Path
+from typing import List, Dict, Any
+from werkzeug.utils import secure_filename
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+from dotenv import load_dotenv
+from unstructured.partition.pdf import partition_pdf
+from flask import send_from_directory, abort
+
+# Bloatectomy class (as per the source you provided)
+from bloatectomy import bloatectomy
+
+# LLM / agent
+from langchain_groq import ChatGroq
+from langgraph.prebuilt import create_react_agent
+
+# LangGraph imports
+from langgraph.graph import StateGraph, START, END
+from typing_extensions import TypedDict, NotRequired
+
+# --- Logging ---------------------------------------------------------------
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+logger = logging.getLogger("health-agent")
+
+# --- Environment & config -------------------------------------------------
+load_dotenv()
+from pathlib import Path
+REPORTS_ROOT = Path(os.getenv("REPORTS_ROOT", "reports")).resolve()   # e.g. /app/reports/<patient_id>/<file.pdf>
+SSRI_FILE = Path(os.getenv("SSRI_FILE", "app/medicationCategories/SSRI_list.txt")).resolve()
+MISC_FILE = Path(os.getenv("MISC_FILE", "app/medicationCategories/MISC_list.txt")).resolve()
+GROQ_API_KEY = os.getenv("GROQ_API_KEY", None)
+ALLOWED_EXTENSIONS = {"pdf"}
+# --- LLM setup -------------------------------------------------------------
+llm = ChatGroq(
+    model=os.getenv("LLM_MODEL", "meta-llama/llama-4-scout-17b-16e-instruct"),
+    temperature=0.0,
+    max_tokens=None,
+)
+
+# Top-level strict system prompt for report JSON pieces (each node will use a more specific prompt)
+NODE_BASE_INSTRUCTIONS = """
+You are HealthAI — a clinical assistant producing JSON for downstream processing.
+Produce only valid JSON (no extra text). Follow field types exactly. If missing data, return empty strings or empty arrays.
+Be conservative: do not assert diagnoses; provide suggestions and ask physician confirmation where needed.
+"""
+
+# Build a generic agent and a JSON resolver agent (to fix broken JSON from LLM)
+agent = create_react_agent(model=llm, tools=[], prompt=NODE_BASE_INSTRUCTIONS)
+agent_json_resolver = create_react_agent(model=llm, tools=[], prompt="""
+You are a JSON fixer. Input: a possibly-malformed JSON-like text. Output: valid JSON only (enclosed in triple backticks).
+Fix missing quotes, trailing commas, unescaped newlines, stray assistant labels, and ensure schema compliance.
+""")
+
+# -------------------- JSON extraction / sanitizer ---------------------------
+def extract_json_from_llm_response(raw_response: str) -> dict:
+    try:
+        # --- 1) Pull out the JSON code-block if present ---
+        md = re.search(r"```(?:json)?\s*([\s\S]*?)\s*```", raw_response)
+        json_string = md.group(1).strip() if md else raw_response
+
+        # --- 2) Trim to the outermost { … } so we drop any prefix/suffix junk ---
+        first, last = json_string.find('{'), json_string.rfind('}')
+        if 0 <= first < last:
+            json_string = json_string[first:last+1]
+
+        # --- 3) PRE-CLEANUP: remove rogue assistant labels, fix boolean quotes ---
+        json_string = re.sub(r'\b\w+\s*{', '{', json_string)
+        json_string = re.sub(r'"assistant"\s*:', '', json_string)
+        json_string = re.sub(r'\b(false|true)"', r'\1', json_string)
+
+        # --- 4) Escape embedded quotes in long string fields (best-effort) ---
+        def _esc(m):
+            prefix, body = m.group(1), m.group(2)
+            return prefix + body.replace('"', r'\"')
+        json_string = re.sub(
+            r'("logic"\s*:\s*")([\s\S]+?)(?=",\s*"[A-Za-z_]\w*"\s*:\s*)',
+            _esc,
+            json_string
+        )
+
+        # --- 5) Remove trailing commas before } or ] ---
+        json_string = re.sub(r',\s*(?=[}\],])', '', json_string)
+        json_string = re.sub(r',\s*,', ',', json_string)
+
+        # --- 6) Balance braces if obvious excess ---
+        ob, cb = json_string.count('{'), json_string.count('}')
+        if cb > ob:
+            excess = cb - ob
+            json_string = json_string.rstrip()[:-excess]
+
+        # --- 7) Escape literal newlines inside strings so json.loads can parse ---
+        def _escape_newlines_in_strings(s: str) -> str:
+            return re.sub(
+                r'"((?:[^"\\]|\\.)*?)"',
+                lambda m: '"' + m.group(1).replace('\n', '\\n').replace('\r', '\\r') + '"',
+                s,
+                flags=re.DOTALL
+            )
+        json_string = _escape_newlines_in_strings(json_string)
+
+        # Final parse
+        return json.loads(json_string)
+    except Exception as e:
+        logger.error(f"Failed to extract JSON from LLM response: {e}")
+        raise
+
+# -------------------- Utility: Bloatectomy wrapper ------------------------
+def clean_notes_with_bloatectomy(text: str, style: str = "remov") -> str:
+    try:
+        b = bloatectomy(text, style=style, output="html")
+        tokens = getattr(b, "tokens", None)
+        if not tokens:
+            return text
+        return "\n".join(tokens)
+    except Exception:
+        logger.exception("Bloatectomy cleaning failed; returning original text")
+        return text
+
+# --------------- Utility: medication extraction (adapted) -----------------
+def readDrugs_from_file(path: Path):
+    try:
+        if not path.exists():
+            return {}, []
+        txt = path.read_text(encoding="utf-8", errors="ignore")
+        generics = re.findall(r"^(.*?)\|", txt, re.MULTILINE)
+        generics = [g.lower() for g in generics if g]
+        lines = [ln.strip().lower() for ln in txt.splitlines() if ln.strip()]
+        return dict(zip(generics, lines)), generics
+    except Exception:
+        logger.exception(f"Failed to read drugs from file: {path}")
+        return {}, []
+
+def addToDrugs_line(line: str, drugs_flags: List[int], listing: Dict[str,str], genList: List[str]) -> List[int]:
+    try:
+        gen_index = {g:i for i,g in enumerate(genList)}
+        for generic, pattern_line in listing.items():
+            try:
+                if re.search(pattern_line, line, re.I):
+                    idx = gen_index.get(generic)
+                    if idx is not None:
+                        drugs_flags[idx] = 1
+            except re.error:
+                continue
+        return drugs_flags
+    except Exception:
+        logger.exception("Error in addToDrugs_line")
+        return drugs_flags
+
+def extract_medications_from_text(text: str) -> List[str]:
+    try:
+        ssri_map, ssri_generics = readDrugs_from_file(SSRI_FILE)
+        misc_map, misc_generics = readDrugs_from_file(MISC_FILE)
+        combined_map = {**ssri_map, **misc_map}
+        combined_generics = []
+        if ssri_generics:
+            combined_generics.extend(ssri_generics)
+        if misc_generics:
+            combined_generics.extend(misc_generics)
+
+        flags = [0]* len(combined_generics)
+        meds_found = set()
+        for ln in text.splitlines():
+            ln = ln.strip()
+            if not ln:
+                continue
+            if combined_map:
+                flags = addToDrugs_line(ln, flags, combined_map, combined_generics)
+            m = re.search(r"\b(Rx|Drug|Medication|Prescribed|Tablet)\s*[:\-]?\s*([A-Za-z0-9\-\s/\.]+)", ln, re.I)
+            if m:
+                meds_found.add(m.group(2).strip())
+            m2 = re.findall(r"\b([A-Z][a-z0-9\-]{2,}\s*(?:[0-9]{1,4}\s*(?:mg|mcg|g|IU))?)", ln)
+            for s in m2:
+                if re.search(r"\b(mg|mcg|g|IU)\b", s, re.I):
+                    meds_found.add(s.strip())
+        for i, f in enumerate(flags):
+            if f == 1:
+                meds_found.add(combined_generics[i])
+        return list(meds_found)
+    except Exception:
+        logger.exception("Failed to extract medications from text")
+        return []
+
+# -------------------- Node prompts --------------------------
+PATIENT_NODE_PROMPT = """
+You will extract patientDetails from the provided document texts.
+Return ONLY JSON with this exact shape:
+{ "patientDetails": {"name": "", "age": "", "sex": "", "pid": ""} }
+Fill fields using text evidence or leave empty strings.
+"""
+
+DOCTOR_NODE_PROMPT = """
+You will extract doctorDetails found in the documents.
+Return ONLY JSON with this exact shape:
+{ "doctorDetails": {"referredBy": ""} }
+"""
+
+TEST_REPORT_NODE_PROMPT = """
+You will extract per-test structured results from the documents.
+Return ONLY JSON with this exact shape:
+{
+ "reports": [
+   {
+     "testName": "",
+     "dateReported": "",
+     "timeReported": "",
+     "abnormalFindings": [
+       {"investigation": "", "result": 0, "unit": "", "status": "", "referenceValue": ""}
+     ],
+     "interpretation": "",
+     "trends": []
+   }
+ ]
+}
+- Include only findings that are outside reference ranges OR explicitly called 'abnormal' in the report.
+- For result numeric parsing, prefer numeric values; if not numeric, keep original string.
+- Use statuses: Low, High, Borderline, Positive, Negative, Normal.
+"""
+
+ANALYSIS_NODE_PROMPT = """
+You will create an overallAnalysis based on the extracted reports (the agent will give you the 'reports' JSON).
+Return ONLY JSON:
+{ "overallAnalysis": { "summary": "", "recommendations": "", "longTermTrends": "",""risk_prediction": "","drug_interaction": "" } }
+Be conservative, evidence-based, and suggest follow-up steps for physicians.
+"""
+
+CONDITION_LOOP_NODE_PROMPT = """
+Validation and condition node:
+Input: partial JSON (patientDetails, doctorDetails, reports, overallAnalysis).
+Task: Check required keys exist and that each report has at least testName and abnormalFindings list.
+Return ONLY JSON:
+{ "valid": true, "missing": [] }
+If missing fields, list keys in 'missing'. Do NOT modify content.
+"""
+
+# -------------------- Node helpers -------------------------
+def call_node_agent(node_prompt: str, payload: dict) -> dict:
+    """
+    Call the generic agent with a targeted node prompt and the payload.
+    Tries to parse JSON. If parsing fails, uses the JSON resolver agent once.
+    """
+    try:
+        content = {
+            "prompt": node_prompt,
+            "payload": payload
+        }
+        resp = agent.invoke({"messages": [{"role": "user", "content": json.dumps(content)}]})
+
+        # Extract raw text from AIMessage or other response types
+        raw = None
+        if isinstance(resp, str):
+            raw = resp
+        elif hasattr(resp, "content"):  # AIMessage or similar
+            raw = resp.content
+        elif isinstance(resp, dict):
+            msgs = resp.get("messages")
+            if msgs:
+                last_msg = msgs[-1]
+                if isinstance(last_msg, str):
+                    raw = last_msg
+                elif hasattr(last_msg, "content"):
+                    raw = last_msg.content
+                elif isinstance(last_msg, dict):
+                    raw = last_msg.get("content", "")
+                else:
+                    raw = str(last_msg)
+            else:
+                raw = json.dumps(resp)
+        else:
+            raw = str(resp)
+
+        parsed = extract_json_from_llm_response(raw)
+        return parsed
+
+    except Exception as e:
+        logger.warning("Node agent JSON parse failed: %s. Attempting JSON resolver.", e)
+        try:
+            resolver_prompt = f"Fix this JSON. Input:\n```json\n{raw}\n```\nReturn valid JSON only."
+            r = agent_json_resolver.invoke({"messages": [{"role": "user", "content": resolver_prompt}]})
+
+            rtxt = None
+            if isinstance(r, str):
+                rtxt = r
+            elif hasattr(r, "content"):
+                rtxt = r.content
+            elif isinstance(r, dict):
+                msgs = r.get("messages")
+                if msgs:
+                    last_msg = msgs[-1]
+                    if isinstance(last_msg, str):
+                        rtxt = last_msg
+                    elif hasattr(last_msg, "content"):
+                        rtxt = last_msg.content
+                    elif isinstance(last_msg, dict):
+                        rtxt = last_msg.get("content", "")
+                    else:
+                        rtxt = str(last_msg)
+                else:
+                    rtxt = json.dumps(r)
+            else:
+                rtxt = str(r)
+
+            corrected = extract_json_from_llm_response(rtxt)
+            return corrected
+        except Exception as e2:
+            logger.exception("JSON resolver also failed: %s", e2)
+            return {}
+
+# -------------------- Define LangGraph State schema -------------------------
+class State(TypedDict):
+    patient_meta: NotRequired[Dict[str, Any]]
+    patient_id: str
+    documents: List[Dict[str, Any]]
+    medications: List[str]
+    patientDetails: NotRequired[Dict[str, Any]]
+    doctorDetails: NotRequired[Dict[str, Any]]
+    reports: NotRequired[List[Dict[str, Any]]]
+    overallAnalysis: NotRequired[Dict[str, Any]]
+    valid: NotRequired[bool]
+    missing: NotRequired[List[str]]
+
+# -------------------- Node implementations as LangGraph nodes -------------------------
+def patient_details_node(state: State) -> dict:
+    payload = {
+        "patient_meta": state.get("patient_meta", {}),
+        "documents": state.get("documents", []),
+        "medications": state.get("medications", [])
+    }
+    logger.info("Running patient_details_node")
+    out = call_node_agent(PATIENT_NODE_PROMPT, payload)
+    return {"patientDetails": out.get("patientDetails", {}) if isinstance(out, dict) else {}}
+
+def doctor_details_node(state: State) -> dict:
+    payload = {
+        "documents": state.get("documents", []),
+        "medications": state.get("medications", [])
+    }
+    logger.info("Running doctor_details_node")
+    out = call_node_agent(DOCTOR_NODE_PROMPT, payload)
+    return {"doctorDetails": out.get("doctorDetails", {}) if isinstance(out, dict) else {}}
+
+def test_report_node(state: State) -> dict:
+    payload = {
+        "documents": state.get("documents", []),
+        "medications": state.get("medications", [])
+    }
+    logger.info("Running test_report_node")
+    out = call_node_agent(TEST_REPORT_NODE_PROMPT, payload)
+    return {"reports": out.get("reports", []) if isinstance(out, dict) else []}
+
+def analysis_node(state: State) -> dict:
+    payload = {
+        "patientDetails": state.get("patientDetails", {}),
+        "doctorDetails": state.get("doctorDetails", {}),
+        "reports": state.get("reports", []),
+        "medications": state.get("medications", [])
+    }
+    logger.info("Running analysis_node")
+    out = call_node_agent(ANALYSIS_NODE_PROMPT, payload)
+    return {"overallAnalysis": out.get("overallAnalysis", {}) if isinstance(out, dict) else {}}
+
+def condition_loop_node(state: State) -> dict:
+    payload = {
+        "patientDetails": state.get("patientDetails", {}),
+        "doctorDetails": state.get("doctorDetails", {}),
+        "reports": state.get("reports", []),
+        "overallAnalysis": state.get("overallAnalysis", {})
+    }
+    logger.info("Running condition_loop_node (validation)")
+    out = call_node_agent(CONDITION_LOOP_NODE_PROMPT, payload)
+    if isinstance(out, dict) and "valid" in out:
+        return {"valid": bool(out.get("valid")), "missing": out.get("missing", [])}
+    missing = []
+    if not state.get("patientDetails"):
+        missing.append("patientDetails")
+    if not state.get("reports"):
+        missing.append("reports")
+    return {"valid": len(missing) == 0, "missing": missing}
+
+# -------------------- Build LangGraph StateGraph -------------------------
+graph_builder = StateGraph(State)
+
+graph_builder.add_node("patient_details", patient_details_node)
+graph_builder.add_node("doctor_details", doctor_details_node)
+graph_builder.add_node("test_report", test_report_node)
+graph_builder.add_node("analysis", analysis_node)
+graph_builder.add_node("condition_loop", condition_loop_node)
+
+graph_builder.add_edge(START, "patient_details")
+graph_builder.add_edge("patient_details", "doctor_details")
+graph_builder.add_edge("doctor_details", "test_report")
+graph_builder.add_edge("test_report", "analysis")
+graph_builder.add_edge("analysis", "condition_loop")
+graph_builder.add_edge("condition_loop", END)
+
+graph = graph_builder.compile()
+
+# -------------------- Flask app & endpoints -------------------------------
+# -------------------- Flask app & endpoints -------------------------------
+BASE_DIR = Path(__file__).resolve().parent
+static_folder = BASE_DIR / "static"
+app = Flask(__name__, static_folder=str(static_folder), static_url_path="/static")
+CORS(app)  # dev convenience; lock down in production
+
+# serve frontend root
+@app.route("/", methods=["GET"])
+def serve_frontend():
+    try:
+        return app.send_static_file("frontend.html")
+    except Exception as e:
+        logger.error(f"Failed to serve frontend.html: {e}")
+        return "<h3>frontend.html not found in static/ — drop your frontend.html there.</h3>", 404
+
+@app.route("/process_reports", methods=["POST"])
+def process_reports():
+    try:
+        data = request.get_json(force=True)
+    except Exception as e:
+        logger.error(f"Failed to parse JSON request: {e}")
+        return jsonify({"error": "Invalid JSON request"}), 400
+
+    patient_id = data.get("patient_id")
+    filenames = data.get("filenames", [])
+    extra_patient_meta = data.get("patientDetails", {})
+
+    if not patient_id or not filenames:
+        return jsonify({"error": "missing patient_id or filenames"}), 400
+
+    patient_folder = REPORTS_ROOT / str(patient_id)
+    if not patient_folder.exists() or not patient_folder.is_dir():
+        return jsonify({"error": f"patient folder not found: {patient_folder}"}), 404
+
+    documents = []
+    combined_text_parts = []
+
+    for fname in filenames:
+        file_path = patient_folder / fname
+        if not file_path.exists():
+            logger.warning("file not found: %s", file_path)
+            continue
+        try:
+            elements = partition_pdf(filename=str(file_path))
+            page_text = "\n".join([el.text for el in elements if hasattr(el, "text") and el.text])
+        except Exception:
+            logger.exception(f"Failed to parse PDF {file_path}")
+            page_text = ""
+        try:
+            cleaned = clean_notes_with_bloatectomy(page_text, style="remov")
+        except Exception:
+            logger.exception("Failed to clean notes with bloatectomy")
+            cleaned = page_text
+        documents.append({
+            "filename": fname,
+            "raw_text": page_text,
+            "cleaned_text": cleaned
+        })
+        combined_text_parts.append(cleaned)
+
+    if not documents:
+        return jsonify({"error": "no valid documents found"}), 400
+
+    combined_text = "\n\n".join(combined_text_parts)
+    try:
+        meds = extract_medications_from_text(combined_text)
+    except Exception:
+        logger.exception("Failed to extract medications")
+        meds = []
+
+    initial_state = {
+        "patient_meta": extra_patient_meta,
+        "patient_id": patient_id,
+        "documents": documents,
+        "medications": meds
+    }
+
+    try:
+        result_state = graph.invoke(initial_state)
+
+        # Validate and fill placeholders if needed
+        if not result_state.get("valid", True):
+            missing = result_state.get("missing", [])
+            logger.info(f"Validation failed; missing keys: {missing}")
+            if "patientDetails" in missing:
+                result_state["patientDetails"] = extra_patient_meta or {"name": "", "age": "", "sex": "", "pid": patient_id}
+            if "reports" in missing:
+                result_state["reports"] = []
+            # Re-run analysis node to keep overallAnalysis consistent
+            result_state.update(analysis_node(result_state))
+            # Re-validate
+            cond = condition_loop_node(result_state)
+            result_state.update(cond)
+
+        safe_response = {
+            "patientDetails": result_state.get("patientDetails", {"name": "", "age": "", "sex": "", "pid": patient_id}),
+            "doctorDetails": result_state.get("doctorDetails", {"referredBy": ""}),
+            "reports": result_state.get("reports", []),
+            "overallAnalysis": result_state.get("overallAnalysis", {"summary": "", "recommendations": "", "longTermTrends": ""}),
+            "_pre_extracted_medications": result_state.get("medications", []),
+            "_validation": {
+                "valid": result_state.get("valid", True),
+                "missing": result_state.get("missing", [])
+            }
+        }
+        return jsonify(safe_response), 200
+
+    except Exception as e:
+        logger.exception("Node pipeline failed")
+        return jsonify({"error": "Node pipeline failed", "detail": str(e)}), 500
+
+
+import mimetypes
+import requests
+import contextlib
+from pathlib import Path
+
+REMOTE_UPLOAD_URL = "https://webashalarforml-patient-bot.hf.space/upload_reports"
+REMOTE_UPLOAD_HEADERS = {
+    # optional headers used by your curl example; update/remove as required
+    "Origin": "https://webashalarforml-patient-bot.hf.space",
+    "Accept": "*/*",
+    "User-Agent": "PatientReportsUploader/1.0"
+    }
+
+@app.route("/upload_reports", methods=["POST"])
+def upload_reports():
+    """
+    Upload one or more files for a patient (same behavior as before).
+    After saving locally under REPORTS_ROOT/<patient_id>, if files were saved
+    this will POST the same files and patient_id to REMOTE_UPLOAD_URL and
+    include the remote response in the returned JSON.
+    """
+    try:
+        patient_id = request.form.get("patient_id") or request.args.get("patient_id")
+        if not patient_id:
+            return jsonify({"error": "patient_id form field required"}), 400
+
+        uploaded_files = request.files.getlist("files")
+        if not uploaded_files:
+            single = request.files.get("file")
+            if single:
+                uploaded_files = [single]
+
+        if not uploaded_files:
+            return jsonify({"error": "no files uploaded (use form field 'files')"}), 400
+
+        patient_folder = REPORTS_ROOT / str(patient_id)
+        patient_folder.mkdir(parents=True, exist_ok=True)
+
+        saved = []
+        skipped = []
+
+        for file_storage in uploaded_files:
+            orig_name = getattr(file_storage, "filename", "") or ""
+            filename = secure_filename(orig_name)
+            if not filename:
+                skipped.append({"filename": orig_name, "reason": "invalid filename"})
+                continue
+
+            ext = filename.rsplit(".", 1)[1].lower() if "." in filename else ""
+            if ext not in ALLOWED_EXTENSIONS:
+                skipped.append({"filename": filename, "reason": f"extension '{ext}' not allowed"})
+                continue
+
+            dest = patient_folder / filename
+            if dest.exists():
+                base, dot, extension = filename.rpartition(".")
+                base = base or filename
+                i = 1
+                while True:
+                    candidate = f"{base}__{i}.{extension}" if extension else f"{base}__{i}"
+                    dest = patient_folder / candidate
+                    if not dest.exists():
+                        filename = candidate
+                        break
+                    i += 1
+
+            try:
+                file_storage.save(str(dest))
+                saved.append(filename)
+            except Exception as e:
+                logger.exception("Failed to save uploaded file %s: %s", filename, e)
+                skipped.append({"filename": filename, "reason": f"save failed: {e}"})
+
+        # ---- POST to remote app if we saved any files ----
+        remote_result = None
+        if saved:
+            saved_paths = [patient_folder / name for name in saved]
+            # Use ExitStack to ensure all file handles are closed after request
+            with contextlib.ExitStack() as stack:
+                files_payload = []
+                for p in saved_paths:
+                    # guess content type, fallback to octet-stream
+                    ctype, _ = mimetypes.guess_type(str(p))
+                    fh = stack.enter_context(open(p, "rb"))
+                    files_payload.append(("files", (p.name, fh, ctype or "application/octet-stream")))
+
+                try:
+                    resp = requests.post(
+                        REMOTE_UPLOAD_URL,
+                        files=files_payload,
+                        data={"patient_id": str(patient_id)},
+                        headers=REMOTE_UPLOAD_HEADERS,
+                        timeout=60  # adjust if needed
+                    )
+                    # keep remote status and a short snippet of body for debugging
+                    remote_result = {
+                        "status_code": resp.status_code,
+                        "ok": resp.ok,
+                        "body_snippet": resp.text[:200]  # don't bloat response
+                    }
+                except Exception as e:
+                    logger.exception("Failed to POST files to remote app: %s", e)
+                    remote_result = {"error": str(e)}
+
+        return jsonify({
+            "patient_id": str(patient_id),
+            "saved": saved,
+            "skipped": skipped,
+            "patient_folder": str(patient_folder),
+            "remote": remote_result
+        }), 200
+
+    except Exception as exc:
+        logger.exception("Upload failed: %s", exc)
+        return jsonify({"error": "upload failed", "detail": str(exc)}), 500
+
+@app.route("/<patient_id>/<filename>")
+def serve_report(patient_id, filename):
+    """
+    Serve a specific uploaded PDF (or other allowed file) for a patient.
+    URL format: /<patient_id>/<filename>
+    Example:   /p14562/report1.pdf
+    """
+    try:
+        patient_folder = REPORTS_ROOT / str(patient_id)
+
+        if not patient_folder.exists():
+            abort(404, description=f"Patient folder not found: {patient_id}")
+
+        # security check: only allow files with allowed extensions
+        ext = filename.rsplit(".", 1)[-1].lower() if "." in filename else ""
+        if ext not in ALLOWED_EXTENSIONS:
+            abort(403, description=f"Extension '{ext}' not allowed")
+
+        return send_from_directory(
+            directory=str(patient_folder),
+            path=filename,
+            as_attachment=False  # set True if you want download instead of inline view
+        )
+
+    except Exception as e:
+        logger.exception("Failed to serve file %s/%s: %s", patient_id, filename, e)
+        abort(500, description=f"Failed to serve file: {e}")
+
+@app.route("/ping", methods=["GET"])
+def ping():
+    return jsonify({"status": "ok"})
+
+if __name__ == "__main__":
+    port = int(os.getenv("PORT", 7860))
+    app.run(host="0.0.0.0", port=port, debug=True)
+

medicationCategories/LICENSE

@@ -0,0 +1,674 @@
+GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
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+
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+Public License instead of this License.  But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.

medicationCategories/README.md

@@ -0,0 +1,28 @@
+medicationCategories  
+====================  
+
+%--------------------------------  
+Written by Marzyeh Ghassemi, CSAIL, MIT  
+Sept 21, 2012 
+Updated 13 Nov, 2017
+Please contact the author with errors found.  
+mghassem {AT} mit {DOT} edu  
+%--------------------------------  
+
+Quick script to parse out medications from discharge summaries in MIMIC format. Note that this approach is brute force: it uses minimal NLP, and can be vastly improved. (hint, hint)  
+
+If you use this code, please cite the GitHub project (see below for Bibtex):  
+@misc{Ghassemi2012,  
+  author = {Ghassemi, Marzyeh},  
+  title = {Discharge Summary Based Pre-Admission (Home) Medication Parser},  
+  year = {2012},  
+  publisher = {GitHub},  
+  journal = {GitHub repository},  
+  howpublished = {\url{https://github.com/mghassem/medicationCategories}},  
+  commit = {PASTE THE COMMIT VERSION YOU'RE USING HERE}  
+}  
+
+The script is set up to run on the MIMIC `noteevents` table in a Postgres database. Please refer to the Jupyter Notebook (finddrugs.ipynb) for example usage. 
+
+Thanks!  
+Marzyeh

medicationCategories/SSRI_list.txt

@@ -0,0 +1,6 @@
+Citalopram|Celexa
+Escitalopram|Lexapro
+Fluoxetine|Prozac|Serafem|Selfemra
+Fluvoxamine|Luvox
+Paroxetine|Paxil|Pexeva
+Sertraline|Zoloft

medicationCategories/finddrugs.ipynb

@@ -0,0 +1,302 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Parse out medications from discharge summaries in MIMIC "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import psycopg2\n",
+    "import getpass\n",
+    "import re\n",
+    "\n",
+    "# Import the finddrugs module\n",
+    "import finddrugs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Create a database connection\n",
+    "user = 'postgres'\n",
+    "host = 'localhost'\n",
+    "dbname = 'mimic'\n",
+    "schema = 'mimiciii'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Connect to the database\n",
+    "con = psycopg2.connect(dbname=dbname, user=user, host=host, \n",
+    "                       password=getpass.getpass(prompt='Password:'.format(user)))\n",
+    "cur = con.cursor()\n",
+    "cur.execute('SET search_path to {}'.format(schema))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Get the notes to be analysed\n",
+    "# NOTE: we are limiting the query to 100 rows here\n",
+    "query = \\\n",
+    "\"\"\"\n",
+    "SELECT n.row_id, n.subject_id, n.hadm_id, n.category, n.description, n.text\n",
+    "FROM noteevents n\n",
+    "WHERE n.category like 'Discharge summary'\n",
+    "LIMIT 100;\n",
+    "\"\"\"\n",
+    "\n",
+    "data = pd.read_sql_query(query,con)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Search the notes\n",
+    "finddrugs.search(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ROW_ID</th>\n",
+       "      <th>SUBJECT_ID</th>\n",
+       "      <th>HADM_ID</th>\n",
+       "      <th>HIST_FOUND</th>\n",
+       "      <th>DEPRESSION</th>\n",
+       "      <th>ADMIT_FOUND</th>\n",
+       "      <th>DIS_FOUND</th>\n",
+       "      <th>GEN_DEPRESS_MEDS_FOUND</th>\n",
+       "      <th>GROUP</th>\n",
+       "      <th>SSRI</th>\n",
+       "      <th>MISC</th>\n",
+       "      <th>citalopram</th>\n",
+       "      <th>escitalopram</th>\n",
+       "      <th>fluoxetine</th>\n",
+       "      <th>fluvoxamine</th>\n",
+       "      <th>paroxetine</th>\n",
+       "      <th>sertraline</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>174</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>176</td>\n",
+       "      <td>13702</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>177</td>\n",
+       "      <td>13702</td>\n",
+       "      <td>196489</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
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+       "      <td>1</td>\n",
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+       "      <td>3</td>\n",
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+       "    </tr>\n",
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+       "      <th>4</th>\n",
+       "      <td>178</td>\n",
+       "      <td>26880</td>\n",
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+       "      <td>1</td>\n",
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+       "      <td>1</td>\n",
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+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ROW_ID  SUBJECT_ID  HADM_ID  HIST_FOUND  DEPRESSION  ADMIT_FOUND  \\\n",
+       "0     174       22532   167853           0           0            0   \n",
+       "1     175       13702   107527           1           0            1   \n",
+       "2     176       13702   167118           1           1            1   \n",
+       "3     177       13702   196489           1           1            1   \n",
+       "4     178       26880   135453           1           0            1   \n",
+       "\n",
+       "   DIS_FOUND  GEN_DEPRESS_MEDS_FOUND  GROUP  SSRI  MISC  citalopram  \\\n",
+       "0          0                       0      0     0     0           0   \n",
+       "1          1                       0      3     1     0           0   \n",
+       "2          1                       0      3     1     0           0   \n",
+       "3          1                       0      3     1     0           0   \n",
+       "4          1                       0      3     1     0           1   \n",
+       "\n",
+       "   escitalopram  fluoxetine  fluvoxamine  paroxetine  sertraline  \n",
+       "0             0           0            0           0           0  \n",
+       "1             1           0            0           0           0  \n",
+       "2             1           0            0           0           0  \n",
+       "3             1           0            0           0           0  \n",
+       "4             0           0            0           0           0  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# load the output to a dataframe\n",
+    "medications = pd.read_csv('output.csv')\n",
+    "medications.head()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

medicationCategories/finddrugs.py

@@ -0,0 +1,237 @@
+#!/usr/bin/python
+
+#--------------------------------
+# Written by Marzyeh Ghassemi, CSAIL, MIT 
+# Sept 21, 2012
+# Updated for Python 3, added Notebook, db connection
+# by Tom J. Pollard 13 Nov, 2017
+# Please contact the author with errors found. 
+# mghassem {AT} mit {DOT} edu
+#--------------------------------
+
+from __future__ import with_statement
+import nltk
+import os
+import os.path
+import re
+import string
+import sys
+import time
+
+def addToDrugs(line, drugs, listing, genList):
+    """
+    ###### function addToDrugs 
+    #   line:    line of text to search
+    #   drugs:   array to modify
+    #   listing: list of search terms in (generic:search list) form
+    #   genList: list of all generic keys being searched for
+    #
+    #   Searches the provided line for drugs that are listed. Inserts 
+    #   a 1 in the drugs array provided at the location which maps 
+    #   the found key to the generics list
+    """
+    genList = dict(enumerate(genList))
+    genList = dict((v,k) for k, v in genList.items())
+
+    for (generic, names) in listing.items():
+        if re.search(names, line, re.I):
+            drugs[genList[generic]] = 1
+    return drugs
+
+def readDrugs(f, genList):
+    """
+    ###### function readDrugs 
+    #   f:       file
+    #   genList: list of search terms in (generic:search list) form
+    #
+    #   Converts lines of the form "generic|brand1|brand2" to a
+    #   dictionary keyed by "generic" with value "generic|brand1|brand2
+    """
+    lines = f.read()
+    generics = re.findall("^(.*?)\|", lines, re.MULTILINE)
+    generics = [x.lower() for x in generics]
+    lines = lines.split("\n")
+    lines = [x.lower() for x in lines]
+    genList.append(generics)
+    return dict(zip(generics, lines))
+
+def search(NOTES, 
+           SSRI_FILE = os.path.join(os.getcwd(), "SSRI_list.txt"), 
+           MISC_FILE = os.path.join(os.getcwd(), "MISC_list.txt"),
+           SUMMARY_FILE = "output.csv",
+           VERBOSE = False):
+    """
+    ###### Search the notes
+    # NOTES: dataframe loaded from the noteevents table
+    # SSRI_FILE: list of SSRI drugs to search for
+    # MISC_FILE: list of additional drugs to search for
+    # 
+    # NB: files should have a line for each distinct drug type, 
+    #      and drugs should be separated by a vertical bar '|'
+    # 
+    # LIMIT FOR PARSING: max number of notes to search.
+    # OUTPUT: name of the output file.
+    """
+
+    if os.path.isfile(SUMMARY_FILE):
+        print('The output file already exists.\n\nRemove the following file or save with a different filename:')
+        print(os.path.join(os.getcwd(), SUMMARY_FILE))
+        return
+
+    starttime = time.time()
+    
+    # Keep a list of all generics we are looking for
+    genList = []
+
+    # Get the drugs into a structure we can use
+    with open(SSRI_FILE) as f:
+        SSRI = readDrugs(f, genList)
+        print("Using drugs from {}".format(SSRI_FILE))
+    try: 
+        with open(MISC_FILE) as f:
+            MISC = readDrugs(f, genList)
+            print("Using additional drugs from {}".format(MISC_FILE))
+    except:
+        MISC = None
+    flatList = [item for sublist in genList for item in sublist]
+
+    # Create indices for the flat list
+    # This allows us to understand which "types" are being used
+    lengths = [len(type) for type in genList]
+    prevLeng = 0
+    starts = []
+    ends = []
+    for leng in lengths:
+        starts.append(prevLeng)
+        ends.append(prevLeng + leng - 1)
+        prevLeng = prevLeng + leng
+
+    # Limit the analysis to discharge summaries
+    # Comment out because limitation is now in SQL query
+    # NOTES = NOTES[NOTES['category'] == 'Discharge summary']
+
+    # Write heads and notes to new doc
+    with open(SUMMARY_FILE, 'a') as f_out:
+        f_out.write('"ROW_ID","SUBJECT_ID","HADM_ID","HIST_FOUND","DEPRESSION","ADMIT_FOUND","DIS_FOUND","GEN_DEPRESS_MEDS_FOUND","GROUP","SSRI","MISC","' \
+            + '","'.join(flatList) + '"\n')
+
+        # Parse each patient record
+        print("Reading documents...")
+
+        for note in NOTES.itertuples():
+            if note.Index % 100 == 0:
+                print("...index: {}. row_id: {}. subject_id: {}. hadm_id: {}. \n".format(note.Index, note.row_id, note.subject_id, note.hadm_id))
+                sys.stdout.flush()
+            
+            # Reset some per-patient variables
+            section = ""
+            newSection = ""
+            admitFound = 0 # admission note found
+            dischargeFound = 0 # discharge summary found
+            histFound = 0 # medical history found
+            depressionHist = 0;
+            drugsAdmit = [0]*len(flatList)
+            drugsDis = [0]*len(flatList)
+            general_depression_drugs = 0
+
+            # Read through lines sequentially
+            # If this looks like a section header, start looking for drugs
+            for line in note.text.split("\n"): 
+
+                # Searches for a section header based on heuristics
+                m = re.search("""^((\d|[A-Z])(\.|\)))?\s*([a-zA-Z',\.\-\*\d\[\]\(\) ]+)(:| WERE | IS | ARE |INCLUDED|INCLUDING)""", line, re.I)
+                if m:
+                    newSection = ""
+                    # Past Medical History Section
+                    if re.search('med(ical)?\s+hist(ory)?', line, re.I):
+                        newSection = "hist"
+                        histFound = 1
+
+                    # Discharge Medication Section                                                        
+                    elif re.search('medication|meds', line, re.I) and re.search('disch(arge)?', line, re.I):
+                        newSection = "discharge"
+                        dischargeFound = 1
+
+                    # Admitting Medication Section
+                    elif re.search('admission|admitting|home|nh|nmeds|pre(\-|\s)?(hosp|op)|current|previous|outpatient|outpt|outside|^[^a-zA-Z]*med(ication)?(s)?', line, re.I) \
+                    and (section == "admit" or re.search('medication|meds', line, re.I)):
+                        newSection = "admit"
+                        admitFound = 1                                         
+                        
+                    # Med section ended, now in non-meds section                        
+                    if section != newSection:
+                        section = newSection
+                
+                # If in history section, search for depression
+                if 'hist' in section:
+                    if re.search('depression', line, re.I):
+                        depressionHist = 1
+
+                # If in meds section, look at each line for specific drugs
+                elif 'admit' in section:
+                    drugsAdmit = addToDrugs(line, drugsAdmit, SSRI, flatList)
+                    if MISC:
+                        drugsAdmit = addToDrugs(line, drugsAdmit, MISC, flatList)
+                    
+                    ## Section just has something like 'Depression meds' 
+                    if re.search('depression\s+med(ication)?(s)?', line, re.I):
+                        general_depression_drugs = 1
+                    
+                ## Already in meds section, look at each line for specific drugs
+                elif 'discharge' in section:
+                    drugsDis = addToDrugs(line, drugsDis, SSRI, flatList)
+                    if MISC:
+                        drugsDis = addToDrugs(line, drugsDis, MISC, flatList)                        
+                    
+                # A line with information which we are uncertain about... 
+                elif re.search('medication|meds', line, re.I) and re.search('admission|discharge|transfer', line, re.I):
+                    if VERBOSE:
+                        print('?? {}'.format(line))
+                    pass
+
+            group = 0
+            # Group 0: Patient has no medications on admission section (or no targeted meds) 
+            #          and medications on discharge from the list
+            if dischargeFound == 1 and (1 in drugsDis) and (admitFound == 0 or not(1 in drugsAdmit)):        
+                group = 0
+
+            # Group 1: Patient has a medications on admission section with no targeted meds
+            #          and no medications on discharge
+            elif admitFound == 1 and not(1 in drugsAdmit) and (dischargeFound == 0) and general_depression_drugs == 0:
+                group = 1
+
+            # Group 2: Patient has medications on admission section, but none from the list
+            #          and no medications on discharge from the list
+            elif admitFound == 1 and not(1 in drugsAdmit) and dischargeFound == 1 and not(1 in drugsDis) and general_depression_drugs == 0:
+                group = 2                                
+
+            # Group 3: Patient has medications on admission (at least one from the list)
+            elif (1 in drugsAdmit):
+                group = 3
+                                
+            else:
+                if VERBOSE:
+                    print('Uncertain about group type for row_id = {}'.format(note.row_id))
+                pass
+
+            if VERBOSE:
+                print('group is {}'.format(group))
+
+            # Combine the admit and discharge drugs lists
+            combined = [w or x for w, x in zip(drugsAdmit, drugsDis)]
+        
+            # Count the types of each drug
+            member = []
+            member = [int(1 in drugsAdmit[s:e+1]) for s, e in zip(starts, ends)]
+
+            # save items to csv
+            f_out.write(str(note.row_id) + "," + str(note.subject_id) + "," + str(note.hadm_id) + "," + str(histFound) + "," \
+                + str(depressionHist) + "," + str(admitFound) + "," + str(dischargeFound) + "," \
+                + str(general_depression_drugs) + "," + str(group) + "," + ",".join(map(str, member)) \
+                + "," + ",".join(map(str, drugsAdmit)) + "\n")
+
+    # Print summary of analysis
+    stoptime = time.time()
+    print("Done analyzing {} documents in {} seconds ({} docs/sec)".format(len(NOTES), 
+        round(stoptime - starttime, 2), round(len(NOTES) / (stoptime - starttime), 2)))
+    print("Summary file is in {}".format(os.getcwd()))

requirements.txt

@@ -0,0 +1,189 @@
+accelerate
+aiofiles
+aiohappyeyeballs
+aiohttp
+aiosignal
+annotated-types
+antlr4-python3-runtime
+anyio
+asgiref
+attrs
+backoff
+beautifulsoup4
+bidict
+blinker
+bloatectomy
+cachetools
+certifi
+cffi
+charset-normalizer
+click
+colorama
+coloredlogs
+contourpy
+cryptography
+cycler
+dataclasses-json
+Deprecated
+distro
+effdet
+emoji
+et_xmlfile
+faiss-cpu
+filelock
+filetype
+Flask
+flask-cors
+Flask-SocketIO
+flatbuffers
+fonttools
+frozenlist
+fsspec
+ftfy
+google-api-core
+google-auth
+google-cloud-vision
+googleapis-common-protos
+greenlet
+groq
+grpcio
+grpcio-status
+gunicorn
+h11
+html5lib
+httpcore
+httpx
+httpx-sse
+huggingface-hub
+humanfriendly
+idna
+itsdangerous
+Jinja2
+jiter
+joblib
+jsonpatch
+jsonpointer
+kiwisolver
+langchain
+langchain-community
+langchain-core
+langchain-experimental
+langchain-groq
+langchain-openai
+langchain-text-splitters
+langdetect
+langgraph
+langgraph-checkpoint
+langgraph-prebuilt
+langgraph-sdk
+langsmith
+lxml
+Markdown
+MarkupSafe
+marshmallow
+matplotlib
+mkl-service 
+mkl_random
+mpmath
+msoffcrypto-tool
+multidict
+mypy_extensions
+networkx
+nltk
+numpy
+olefile
+omegaconf
+onnx
+onnxruntime
+open_clip_torch
+openai
+opencv-python
+openpyxl
+orjson
+ormsgpack
+packaging
+pandas
+pdf2image
+pdfminer.six
+pi_heif
+pikepdf
+pillow
+propcache
+proto-plus
+protobuf
+psutil
+pyasn1
+pyasn1_modules
+pycocotools
+pycparser
+pydantic
+pydantic-settings
+pydantic_core
+pypandoc
+pyparsing
+pypdf
+PyPDF2
+pypdfium2
+pyreadline3
+pytesseract
+python-dateutil
+python-docx
+python-dotenv
+python-engineio
+python-iso639
+python-magic
+python-multipart
+python-oxmsg
+python-pptx
+python-socketio
+pytz
+PyYAML
+RapidFuzz
+regex
+requests
+requests-toolbelt
+rsa
+safetensors
+scikit-learn
+scipy
+sentence-transformers
+setuptools
+simple-websocket
+six
+sniffio
+soupsieve
+SpeechRecognition
+SQLAlchemy
+standard-aifc
+standard-chunk
+sympy
+tenacity
+threadpoolctl
+tiktoken
+timm
+tokenizers
+torch
+torchvision
+tqdm
+transformers
+typing-inspect
+typing-inspection
+typing_extensions
+tzdata
+unstructured
+unstructured-client
+unstructured-inference
+unstructured.pytesseract
+urllib3
+wcwidth
+webencodings
+Werkzeug
+wheel
+wrapt
+wsproto
+xlrd
+xlsxwriter
+xxhash
+yarl
+zstandard
+gunicorn

static/frontend.html

@@ -0,0 +1,155 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1" />
+  <title>Health Reports Processor</title>
+  <style>
+    body {
+      font-family: Arial, sans-serif;
+      margin: 2rem;
+      max-width: 800px;
+    }
+    label {
+      display: block;
+      margin-top: 1rem;
+      font-weight: bold;
+    }
+    input, textarea, button {
+      width: 100%;
+      padding: 0.5rem;
+      margin-top: 0.25rem;
+      box-sizing: border-box;
+      font-size: 1rem;
+    }
+    button {
+      margin-top: 1rem;
+      cursor: pointer;
+    }
+    pre {
+      background: #f4f4f4;
+      padding: 1rem;
+      overflow-x: auto;
+      white-space: pre-wrap;
+      word-wrap: break-word;
+      border: 1px solid #ccc;
+      margin-top: 1rem;
+    }
+    .error {
+      color: red;
+      margin-top: 1rem;
+    }
+  </style>
+</head>
+<body>
+  <h1>Health Reports Processor</h1>
+
+  <!-- Upload form -->
+  <form id="uploadForm" enctype="multipart/form-data">
+    <label for="uploadPatientId">Patient ID:</label>
+    <input type="text" id="uploadPatientId" name="patient_id" required />
+
+    <label for="files">Upload Reports:</label>
+    <input type="file" id="files" name="files" multiple required />
+
+    <button type="submit">Upload Reports</button>
+  </form>
+
+  <div id="uploadResult"></div>
+
+  <hr />
+
+  <!-- Existing process reports form -->
+  <form id="reportForm">
+    <label for="patientId">Patient ID:</label>
+    <input type="text" id="patientId" name="patientId" required />
+
+    <label for="filenames">Filenames (comma-separated):</label>
+    <input type="text" id="filenames" name="filenames" placeholder="e.g. cbc.pdf, thyroid.pdf" required />
+
+    <button type="submit">Process Reports</button>
+  </form>
+
+  <div id="result"></div>
+
+  <script>
+    // Upload form
+    const uploadForm = document.getElementById('uploadForm');
+    const uploadResult = document.getElementById('uploadResult');
+
+    uploadForm.addEventListener('submit', async (e) => {
+      e.preventDefault();
+      uploadResult.innerHTML = '';
+
+      const patientId = document.getElementById('uploadPatientId').value.trim();
+      const filesInput = document.getElementById('files');
+
+      if (!patientId || filesInput.files.length === 0) {
+        uploadResult.innerHTML = '<p class="error">Please enter a Patient ID and select at least one file.</p>';
+        return;
+      }
+
+      const formData = new FormData();
+      formData.append('patient_id', patientId);
+      for (let f of filesInput.files) {
+        formData.append('files', f);
+      }
+
+      try {
+        const response = await fetch('/upload_reports', {
+          method: 'POST',
+          body: formData
+        });
+
+        if (!response.ok) {
+          const errData = await response.json();
+          uploadResult.innerHTML = `<p class="error">Error: ${errData.error || response.statusText}</p>`;
+          return;
+        }
+
+        const data = await response.json();
+        uploadResult.innerHTML = `<h2>Upload Result</h2><pre>${JSON.stringify(data, null, 2)}</pre>`;
+      } catch (err) {
+        uploadResult.innerHTML = `<p class="error">Upload failed: ${err.message}</p>`;
+      }
+    });
+
+    // Process form
+    const form = document.getElementById('reportForm');
+    const resultDiv = document.getElementById('result');
+
+    form.addEventListener('submit', async (e) => {
+      e.preventDefault();
+      resultDiv.innerHTML = '';
+
+      const patientId = form.patientId.value.trim();
+      const filenamesRaw = form.filenames.value.trim();
+      if (!patientId || !filenamesRaw) {
+        resultDiv.innerHTML = '<p class="error">Please enter both Patient ID and filenames.</p>';
+        return;
+      }
+
+      const filenames = filenamesRaw.split(',').map(f => f.trim()).filter(f => f.length > 0);
+
+      try {
+        const response = await fetch('/process_reports', {
+          method: 'POST',
+          headers: { 'Content-Type': 'application/json' },
+          body: JSON.stringify({ patient_id: patientId, filenames: filenames })
+        });
+
+        if (!response.ok) {
+          const errorData = await response.json();
+          resultDiv.innerHTML = `<p class="error">Error: ${errorData.error || response.statusText}</p>`;
+          return;
+        }
+
+        const data = await response.json();
+        resultDiv.innerHTML = `<h2>Processed Result</h2><pre>${JSON.stringify(data, null, 2)}</pre>`;
+      } catch (err) {
+        resultDiv.innerHTML = `<p class="error">Request failed: ${err.message}</p>`;
+      }
+    });
+  </script>
+</body>
+</html>