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backend.py

@@ -1,428 +1,428 @@
-"""
-Internal Medicine Discharge Letter Error-Check — Backend
-Prospective study: AI-assisted error detection in ED discharge letters
-
-Flow:
-1. Receive Croatian discharge letter from doctor
-2. Translate to English (Gemini 3.1 Flash Lite)
-3. Run concurrent error-detection analysis:
-   - DeepSeek Reasoner (via DeepSeek API)
-   - GPT-OSS-120B (via Groq)
-4. Parse structured output and return errors + suggestions
-"""
-
-import os
-import json
-import time
-from concurrent.futures import ThreadPoolExecutor
-from dataclasses import dataclass, field
-from typing import Optional
-from dotenv import load_dotenv
-from google import genai
-from openai import OpenAI
-from groq import Groq
-
-load_dotenv(dotenv_path=os.path.join(os.path.dirname(__file__), ".env"))
-
-# ---------------------------------------------------------------------------
-# API clients
-# ---------------------------------------------------------------------------
-
-def get_gemini_client() -> genai.Client:
-    key = os.environ.get("GOOGLE_API_KEY") or os.environ.get("GEMINI_API_KEY")
-    return genai.Client(api_key=key)
-
-
-def get_deepseek_client() -> OpenAI:
-    return OpenAI(
-        api_key=os.environ.get("DEEPSEEK_API_KEY"),
-        base_url="https://api.deepseek.com",
-    )
-
-
-def get_groq_client() -> Groq:
-    return Groq(api_key=os.environ.get("GROQ_API_KEY_OSS"))
-
-
-DEEPSEEK_TIMEOUT_SECONDS = 120
-DEEPSEEK_MAX_TOKENS = 8192
-DEEPSEEK_MAX_ATTEMPTS = 2
-DEEPSEEK_RETRY_SLEEP_SECONDS = 2
-
-
-def _log_deepseek(event: str, **kwargs):
-    parts = [f"{key}={value!r}" for key, value in kwargs.items()]
-    suffix = f" | {' | '.join(parts)}" if parts else ""
-    print(f"[DeepSeek] {event}{suffix}", flush=True)
-
-
-def _deepseek_response_meta(response) -> dict:
-    choice = response.choices[0]
-    message = choice.message
-    content = message.content or ""
-    reasoning = getattr(message, "reasoning_content", "") or ""
-    return {
-        "finish_reason": getattr(choice, "finish_reason", None),
-        "content_len": len(content),
-        "reasoning_len": len(reasoning),
-    }
-
-
-# ---------------------------------------------------------------------------
-# Prompts
-# ---------------------------------------------------------------------------
-
-TRANSLATION_PROMPT = """You are a medical translator. Translate the following Croatian clinical discharge letter to English.
-Preserve ALL medical terminology, values, units, drug names, dosages, and clinical details exactly.
-Output ONLY the English translation, nothing else.
-
-Croatian text:
-{text}"""
-
-ERROR_CHECK_SYSTEM_PROMPT = """You are an expert internal medicine physician reviewing emergency department discharge letters for errors and quality issues.
-
-Your task: carefully analyze the discharge letter and identify up to 3 ERRORS and up to 2 IMPROVEMENT SUGGESTIONS.
-The goal is precision, not forcing findings.
-
-ERRORS are factual, clinical, or documentation mistakes present in the letter, such as:
-- Medication errors (wrong drug, wrong dose, drug interactions, contraindications)
-- Diagnostic errors (incorrect diagnosis given the findings, missed diagnosis)
-- Dosing errors (incorrect dose for patient weight/age/renal function)
-- Lab interpretation errors (misinterpreted lab values, missed abnormal results)
-- Documentation errors (inconsistencies, contradictions within the letter)
-- Omissions (critical missing information that should be documented)
-
-SUGGESTIONS are general quality improvements that are NOT necessarily errors, such as:
-- Documentation completeness improvements
-- Clinical workflow recommendations
-- Patient safety enhancements
-- Follow-up care suggestions
-
-For every suggestion you MUST:
-- Identify the specific part of the letter that could be improved
-- Quote the relevant original text (or note what is missing)
-- Provide the exact rewritten version or additional text you would use instead
-This makes every suggestion concrete and immediately usable rather than vague or generic.
-
-CRITICAL RULES:
-- Only report genuine errors you are confident about. Do NOT fabricate errors.
-- Do NOT force yourself to find 3 errors.
-- If you find fewer than 3 errors, report only what you find.
-- It is acceptable to find 0 errors. If no clear error is present, return "errors": [].
-- When uncertain, prefer returning no error rather than a speculative one.
-- You may still provide 0-2 useful improvement suggestions even when errors is empty.
-- Be specific: quote the relevant part of the letter for each error and suggestion.
-- Categorize each error and suggestion precisely.
-- For every suggestion, always include both the original quote and your exact suggested rewrite.
-
-You MUST respond in the following JSON format and NOTHING else:
-
-{
-  "errors": [
-    {
-      "description": "Clear description of the error",
-      "category": "medication_error|diagnostic_error|dosing_error|documentation_error|lab_interpretation_error|contraindication|omission|other",
-      "severity": "low|medium|high|critical",
-      "quote": "Exact quote from the letter where the error appears"
-    }
-  ],
-  "suggestions": [
-    {
-      "description": "Clear description of the improvement suggestion",
-      "category": "documentation_quality|clinical_workflow|patient_safety|completeness|other",
-      "quote": "Exact quote from the letter (or 'N/A' if adding entirely new content)",
-      "suggested_rewrite": "Exactly how you would have written it differently - the full improved text you recommend"
-    }
-  ],
-  "summary": "One-sentence overall assessment of the discharge letter quality"
-}
-
-Valid zero-error example:
-{
-  "errors": [],
-  "suggestions": [
-    {
-      "description": "Make the follow-up plan more explicit and actionable for the patient and primary care provider.",
-      "category": "documentation_quality",
-      "quote": "Follow up with primary care in 1 week.",
-      "suggested_rewrite": "Please follow up with your primary care physician within 7 days for repeat labs and clinical reassessment. If you experience worsening shortness of breath, chest pain, or fever, return to the emergency department immediately or call the 24-hour advice line at (555) 123-4567."
-    }
-  ],
-  "summary": "No clear clinical or documentation errors were identified, but the discharge letter could be improved with more specific follow-up instructions."
-}"""
-
-ERROR_CHECK_USER_PROMPT = """Analyze the following internal medicine emergency department discharge letter for errors and quality issues.
-
-DISCHARGE LETTER:
-{clinical_text}
-
-Respond with the JSON format specified in your instructions.
-Remember:
-- up to 3 errors
-- up to 2 suggestions
-- only report genuine errors
-- if no clear errors are present, return `"errors": []` and optionally provide suggestions"""
-
-
-# ---------------------------------------------------------------------------
-# Data classes
-# ---------------------------------------------------------------------------
-
-@dataclass
-class ParsedError:
-    description: str
-    category: str
-    severity: str
-    quote: str
-
-
-@dataclass
-class ParsedSuggestion:
-    description: str
-    category: str
-    quote: str = ""
-    suggested_rewrite: str = ""
-
-
-@dataclass
-class ModelResult:
-    model_name: str
-    raw_response: str
-    errors: list = field(default_factory=list)
-    suggestions: list = field(default_factory=list)
-    summary: str = ""
-    success: bool = True
-    error_message: Optional[str] = None
-    latency_seconds: float = 0.0
-
-
-@dataclass
-class AnalysisResponse:
-    original_text: str
-    translated_text: str
-    model_a_result: ModelResult
-    model_b_result: ModelResult
-    translation_latency: float = 0.0
-
-
-# ---------------------------------------------------------------------------
-# Translation
-# ---------------------------------------------------------------------------
-
-def translate_to_english(text: str) -> str:
-    client = get_gemini_client()
-    response = client.models.generate_content(
-        model="gemini-3.1-flash-lite-preview",
-        contents=TRANSLATION_PROMPT.format(text=text),
-    )
-    return response.text
-
-
-# ---------------------------------------------------------------------------
-# JSON parsing helper
-# ---------------------------------------------------------------------------
-
-def parse_model_json(raw: str) -> dict:
-    """Extract JSON from model response, handling markdown code fences."""
-    text = raw.strip()
-    if text.startswith("```"):
-        first_newline = text.index("\n")
-        last_fence = text.rfind("```")
-        text = text[first_newline + 1 : last_fence].strip()
-    try:
-        return json.loads(text)
-    except json.JSONDecodeError:
-        start = text.find("{")
-        end = text.rfind("}") + 1
-        if start != -1 and end > start:
-            return json.loads(text[start:end])
-        raise
-
-
-# ---------------------------------------------------------------------------
-# Model calls
-# ---------------------------------------------------------------------------
-
-def _parse_to_result(model_label: str, raw: str, latency: float) -> ModelResult:
-    parsed = parse_model_json(raw)
-    errors = [
-        ParsedError(
-            description=e.get("description", ""),
-            category=e.get("category", "other"),
-            severity=e.get("severity", "medium"),
-            quote=e.get("quote", ""),
-        )
-        for e in parsed.get("errors", [])
-    ]
-    suggestions = [
-        ParsedSuggestion(
-            description=s.get("description", ""),
-            category=s.get("category", "other"),
-            quote=s.get("quote", ""),
-            suggested_rewrite=s.get("suggested_rewrite", ""),
-        )
-        for s in parsed.get("suggestions", [])
-    ]
-    return ModelResult(
-        model_name=model_label,
-        raw_response=raw,
-        errors=errors,
-        suggestions=suggestions,
-        summary=parsed.get("summary", ""),
-        success=True,
-        latency_seconds=round(latency, 2),
-    )
-
-
-def call_model_a(clinical_text: str) -> ModelResult:
-    """DeepSeek Reasoner via DeepSeek API."""
-    start = time.time()
-    client = get_deepseek_client()
-    last_error = None
-
-    for attempt in range(1, DEEPSEEK_MAX_ATTEMPTS + 1):
-        attempt_start = time.time()
-        try:
-            _log_deepseek("attempt_start", attempt=attempt)
-            response = client.chat.completions.create(
-                model="deepseek-reasoner",
-                messages=[
-                    {"role": "system", "content": ERROR_CHECK_SYSTEM_PROMPT},
-                    {
-                        "role": "user",
-                        "content": ERROR_CHECK_USER_PROMPT.format(
-                            clinical_text=clinical_text
-                        ),
-                    },
-                ],
-                max_tokens=DEEPSEEK_MAX_TOKENS,
-                timeout=DEEPSEEK_TIMEOUT_SECONDS,
-            )
-            meta = _deepseek_response_meta(response)
-            _log_deepseek("attempt_response", attempt=attempt, **meta)
-
-            raw = response.choices[0].message.content or ""
-            if not raw.strip():
-                raise ValueError(
-                    "DeepSeek returned an empty response body "
-                    f"(finish_reason={meta['finish_reason']}, "
-                    f"reasoning_len={meta['reasoning_len']})."
-                )
-
-            result = _parse_to_result("DeepSeek Reasoner", raw, time.time() - start)
-            _log_deepseek(
-                "attempt_success",
-                attempt=attempt,
-                elapsed_total=round(time.time() - start, 2),
-                errors_found=len(result.errors),
-                suggestions_found=len(result.suggestions),
-            )
-            return result
-        except Exception as exc:
-            last_error = exc
-            _log_deepseek(
-                "attempt_failed",
-                attempt=attempt,
-                elapsed_attempt=round(time.time() - attempt_start, 2),
-                error_type=type(exc).__name__,
-                error=str(exc),
-            )
-            if attempt < DEEPSEEK_MAX_ATTEMPTS:
-                time.sleep(DEEPSEEK_RETRY_SLEEP_SECONDS)
-
-    return ModelResult(
-        model_name="DeepSeek Reasoner",
-        raw_response="",
-        success=False,
-        error_message=(
-            f"{last_error} after {DEEPSEEK_MAX_ATTEMPTS} attempts"
-            if last_error
-            else "DeepSeek failed for an unknown reason."
-        ),
-        latency_seconds=round(time.time() - start, 2),
-    )
-
-
-def call_model_b(clinical_text: str) -> ModelResult:
-    """GPT-OSS-120B via Groq."""
-    start = time.time()
-    try:
-        client = get_groq_client()
-        response = client.chat.completions.create(
-            model="openai/gpt-oss-120b",
-            messages=[
-                {"role": "system", "content": ERROR_CHECK_SYSTEM_PROMPT},
-                {"role": "user", "content": ERROR_CHECK_USER_PROMPT.format(clinical_text=clinical_text)},
-            ],
-            temperature=0.2,
-            max_tokens=4096,
-        )
-        raw = response.choices[0].message.content
-        return _parse_to_result("GPT-OSS-120B", raw, time.time() - start)
-    except Exception as exc:
-        return ModelResult(
-            model_name="GPT-OSS-120B",
-            raw_response="",
-            success=False,
-            error_message=str(exc),
-            latency_seconds=round(time.time() - start, 2),
-        )
-
-
-# ---------------------------------------------------------------------------
-# Main pipeline
-# ---------------------------------------------------------------------------
-
-def run_error_check(croatian_text: str) -> AnalysisResponse:
-    """Full pipeline: translate, then run both models concurrently."""
-    t0 = time.time()
-    english_text = translate_to_english(croatian_text)
-    translation_latency = round(time.time() - t0, 2)
-
-    with ThreadPoolExecutor(max_workers=2) as pool:
-        future_a = pool.submit(call_model_a, english_text)
-        future_b = pool.submit(call_model_b, english_text)
-        result_a = future_a.result()
-        result_b = future_b.result()
-
-    return AnalysisResponse(
-        original_text=croatian_text,
-        translated_text=english_text,
-        model_a_result=result_a,
-        model_b_result=result_b,
-        translation_latency=translation_latency,
-    )
-
-
-# ---------------------------------------------------------------------------
-# CLI test
-# ---------------------------------------------------------------------------
-
-if __name__ == "__main__":
-    import sys, io
-    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace")
-
-    sample = """Bolesnik 68 godina, dolazi zbog bolova u prsištu.
-    Dijagnoza: STEMI prednje stijenke.
-    Terapija: Aspirin 100mg, Klopidogrel 75mg, Ramipril 5mg, Atorvastatin 40mg.
-    Preporučen kontrolni pregled za 7 dana."""
-
-    print("=" * 60)
-    print("ERROR CHECK TEST")
-    print("=" * 60)
-
-    result = run_error_check(sample)
-    print(f"\nTranslation ({result.translation_latency}s):")
-    print(result.translated_text)
-
-    for r in [result.model_a_result, result.model_b_result]:
-        print(f"\n{'=' * 60}")
-        print(f"{r.model_name} ({r.latency_seconds}s):")
-        if r.success:
-            print(f"Summary: {r.summary}")
-            for i, e in enumerate(r.errors, 1):
-                print(f"  Error {i}: [{e.category}/{e.severity}] {e.description}")
-            for i, s in enumerate(r.suggestions, 1):
-                print(f"  Suggestion {i}: [{s.category}] {s.description}")
-        else:
-            print(f"ERROR: {r.error_message}")
+"""
+Internal Medicine Discharge Letter Error-Check — Backend
+Prospective study: AI-assisted error detection in ED discharge letters
+
+Flow:
+1. Receive Croatian discharge letter from doctor
+2. Translate to English (Gemini 3.1 Flash Lite)
+3. Run concurrent error-detection analysis:
+   - DeepSeek Reasoner (via DeepSeek API)
+   - GPT-OSS-120B (via Groq)
+4. Parse structured output and return errors + suggestions
+"""
+
+import os
+import json
+import time
+from concurrent.futures import ThreadPoolExecutor
+from dataclasses import dataclass, field
+from typing import Optional
+from dotenv import load_dotenv
+from google import genai
+from openai import OpenAI
+from groq import Groq
+
+load_dotenv(dotenv_path=os.path.join(os.path.dirname(__file__), ".env"))
+
+# ---------------------------------------------------------------------------
+# API clients
+# ---------------------------------------------------------------------------
+
+def get_gemini_client() -> genai.Client:
+    key = os.environ.get("GOOGLE_API_KEY") or os.environ.get("GEMINI_API_KEY")
+    return genai.Client(api_key=key)
+
+
+def get_deepseek_client() -> OpenAI:
+    return OpenAI(
+        api_key=os.environ.get("DEEPSEEK_API_KEY"),
+        base_url="https://api.deepseek.com",
+    )
+
+
+def get_groq_client() -> Groq:
+    return Groq(api_key=os.environ.get("GROQ_API_KEY_OSS"))
+
+
+DEEPSEEK_TIMEOUT_SECONDS = 120
+DEEPSEEK_MAX_TOKENS = 8192
+DEEPSEEK_MAX_ATTEMPTS = 2
+DEEPSEEK_RETRY_SLEEP_SECONDS = 2
+
+
+def _log_deepseek(event: str, **kwargs):
+    parts = [f"{key}={value!r}" for key, value in kwargs.items()]
+    suffix = f" | {' | '.join(parts)}" if parts else ""
+    print(f"[DeepSeek] {event}{suffix}", flush=True)
+
+
+def _deepseek_response_meta(response) -> dict:
+    choice = response.choices[0]
+    message = choice.message
+    content = message.content or ""
+    reasoning = getattr(message, "reasoning_content", "") or ""
+    return {
+        "finish_reason": getattr(choice, "finish_reason", None),
+        "content_len": len(content),
+        "reasoning_len": len(reasoning),
+    }
+
+
+# ---------------------------------------------------------------------------
+# Prompts
+# ---------------------------------------------------------------------------
+
+TRANSLATION_PROMPT = """You are a medical translator. Translate the following Croatian clinical discharge letter to English.
+Preserve ALL medical terminology, values, units, drug names, dosages, and clinical details exactly.
+Output ONLY the English translation, nothing else.
+
+Croatian text:
+{text}"""
+
+ERROR_CHECK_SYSTEM_PROMPT = """You are an expert internal medicine physician reviewing emergency department discharge letters for errors and quality issues.
+
+Your task: carefully analyze the discharge letter and identify up to 3 ERRORS and up to 2 IMPROVEMENT SUGGESTIONS.
+The goal is precision, not forcing findings.
+
+ERRORS are factual, clinical, or documentation mistakes present in the letter, such as:
+- Medication errors (wrong drug, wrong dose, drug interactions, contraindications)
+- Diagnostic errors (incorrect diagnosis given the findings, missed diagnosis)
+- Dosing errors (incorrect dose for patient weight/age/renal function)
+- Lab interpretation errors (misinterpreted lab values, missed abnormal results)
+- Documentation errors (inconsistencies, contradictions within the letter)
+- Omissions (critical missing information that should be documented)
+
+SUGGESTIONS are general quality improvements that are NOT necessarily errors, such as:
+- Documentation completeness improvements
+- Clinical workflow recommendations
+- Patient safety enhancements
+- Follow-up care suggestions
+
+For every suggestion you MUST:
+- Identify the specific part of the letter that could be improved
+- Quote the relevant original text (or note what is missing)
+- Provide the exact rewritten version or additional text you would use instead
+This makes every suggestion concrete and immediately usable rather than vague or generic.
+
+CRITICAL RULES:
+- Only report genuine errors you are confident about. Do NOT fabricate errors.
+- Do NOT force yourself to find 3 errors.
+- If you find fewer than 3 errors, report only what you find.
+- It is acceptable to find 0 errors. If no clear error is present, return "errors": [].
+- When uncertain, prefer returning no error rather than a speculative one.
+- You may still provide 0-2 useful improvement suggestions even when errors is empty.
+- Be specific: quote the relevant part of the letter for each error and suggestion.
+- Categorize each error and suggestion precisely.
+- For every suggestion, always include both the original quote and your exact suggested rewrite.
+
+You MUST respond in the following JSON format and NOTHING else:
+
+{
+  "errors": [
+    {
+      "description": "Clear description of the error",
+      "category": "medication_error|diagnostic_error|dosing_error|documentation_error|lab_interpretation_error|contraindication|omission|other",
+      "severity": "low|medium|high|critical",
+      "quote": "Exact quote from the letter where the error appears"
+    }
+  ],
+  "suggestions": [
+    {
+      "description": "Clear description of the improvement suggestion",
+      "category": "documentation_quality|clinical_workflow|patient_safety|completeness|other",
+      "quote": "Exact quote from the letter (or 'N/A' if adding entirely new content)",
+      "suggested_rewrite": "Exactly how you would have written it differently - the full improved text you recommend"
+    }
+  ],
+  "summary": "One-sentence overall assessment of the discharge letter quality"
+}
+
+Valid zero-error example:
+{
+  "errors": [],
+  "suggestions": [
+    {
+      "description": "Make the follow-up plan more explicit and actionable for the patient and primary care provider.",
+      "category": "documentation_quality",
+      "quote": "Follow up with primary care in 1 week.",
+      "suggested_rewrite": "Please follow up with your primary care physician within 7 days for repeat labs and clinical reassessment. If you experience worsening shortness of breath, chest pain, or fever, return to the emergency department immediately or call the 24-hour advice line at (555) 123-4567."
+    }
+  ],
+  "summary": "No clear clinical or documentation errors were identified, but the discharge letter could be improved with more specific follow-up instructions."
+}"""
+
+ERROR_CHECK_USER_PROMPT = """Analyze the following internal medicine emergency department discharge letter for errors and quality issues.
+
+DISCHARGE LETTER:
+{clinical_text}
+
+Respond with the JSON format specified in your instructions.
+Remember:
+- up to 3 errors
+- up to 2 suggestions
+- only report genuine errors
+- if no clear errors are present, return `"errors": []` and optionally provide suggestions"""
+
+
+# ---------------------------------------------------------------------------
+# Data classes
+# ---------------------------------------------------------------------------
+
+@dataclass
+class ParsedError:
+    description: str
+    category: str
+    severity: str
+    quote: str
+
+
+@dataclass
+class ParsedSuggestion:
+    description: str
+    category: str
+    quote: str = ""
+    suggested_rewrite: str = ""
+
+
+@dataclass
+class ModelResult:
+    model_name: str
+    raw_response: str
+    errors: list = field(default_factory=list)
+    suggestions: list = field(default_factory=list)
+    summary: str = ""
+    success: bool = True
+    error_message: Optional[str] = None
+    latency_seconds: float = 0.0
+
+
+@dataclass
+class AnalysisResponse:
+    original_text: str
+    translated_text: str
+    model_a_result: ModelResult
+    model_b_result: ModelResult
+    translation_latency: float = 0.0
+
+
+# ---------------------------------------------------------------------------
+# Translation
+# ---------------------------------------------------------------------------
+
+def translate_to_english(text: str) -> str:
+    client = get_gemini_client()
+    response = client.models.generate_content(
+        model="gemini-3.1-flash-lite-preview",
+        contents=TRANSLATION_PROMPT.format(text=text),
+    )
+    return response.text
+
+
+# ---------------------------------------------------------------------------
+# JSON parsing helper
+# ---------------------------------------------------------------------------
+
+def parse_model_json(raw: str) -> dict:
+    """Extract JSON from model response, handling markdown code fences."""
+    text = raw.strip()
+    if text.startswith("```"):
+        first_newline = text.index("\n")
+        last_fence = text.rfind("```")
+        text = text[first_newline + 1 : last_fence].strip()
+    try:
+        return json.loads(text)
+    except json.JSONDecodeError:
+        start = text.find("{")
+        end = text.rfind("}") + 1
+        if start != -1 and end > start:
+            return json.loads(text[start:end])
+        raise
+
+
+# ---------------------------------------------------------------------------
+# Model calls
+# ---------------------------------------------------------------------------
+
+def _parse_to_result(model_label: str, raw: str, latency: float) -> ModelResult:
+    parsed = parse_model_json(raw)
+    errors = [
+        ParsedError(
+            description=e.get("description", ""),
+            category=e.get("category", "other"),
+            severity=e.get("severity", "medium"),
+            quote=e.get("quote", ""),
+        )
+        for e in parsed.get("errors", [])
+    ]
+    suggestions = [
+        ParsedSuggestion(
+            description=s.get("description", ""),
+            category=s.get("category", "other"),
+            quote=s.get("quote", ""),
+            suggested_rewrite=s.get("suggested_rewrite", ""),
+        )
+        for s in parsed.get("suggestions", [])
+    ]
+    return ModelResult(
+        model_name=model_label,
+        raw_response=raw,
+        errors=errors,
+        suggestions=suggestions,
+        summary=parsed.get("summary", ""),
+        success=True,
+        latency_seconds=round(latency, 2),
+    )
+
+
+def call_model_a(clinical_text: str) -> ModelResult:
+    """DeepSeek Reasoner via DeepSeek API."""
+    start = time.time()
+    client = get_deepseek_client()
+    last_error = None
+
+    for attempt in range(1, DEEPSEEK_MAX_ATTEMPTS + 1):
+        attempt_start = time.time()
+        try:
+            _log_deepseek("attempt_start", attempt=attempt)
+            response = client.chat.completions.create(
+                model="deepseek-reasoner",
+                messages=[
+                    {"role": "system", "content": ERROR_CHECK_SYSTEM_PROMPT},
+                    {
+                        "role": "user",
+                        "content": ERROR_CHECK_USER_PROMPT.format(
+                            clinical_text=clinical_text
+                        ),
+                    },
+                ],
+                max_tokens=DEEPSEEK_MAX_TOKENS,
+                timeout=DEEPSEEK_TIMEOUT_SECONDS,
+            )
+            meta = _deepseek_response_meta(response)
+            _log_deepseek("attempt_response", attempt=attempt, **meta)
+
+            raw = response.choices[0].message.content or ""
+            if not raw.strip():
+                raise ValueError(
+                    "DeepSeek returned an empty response body "
+                    f"(finish_reason={meta['finish_reason']}, "
+                    f"reasoning_len={meta['reasoning_len']})."
+                )
+
+            result = _parse_to_result("DeepSeek Reasoner", raw, time.time() - start)
+            _log_deepseek(
+                "attempt_success",
+                attempt=attempt,
+                elapsed_total=round(time.time() - start, 2),
+                errors_found=len(result.errors),
+                suggestions_found=len(result.suggestions),
+            )
+            return result
+        except Exception as exc:
+            last_error = exc
+            _log_deepseek(
+                "attempt_failed",
+                attempt=attempt,
+                elapsed_attempt=round(time.time() - attempt_start, 2),
+                error_type=type(exc).__name__,
+                error=str(exc),
+            )
+            if attempt < DEEPSEEK_MAX_ATTEMPTS:
+                time.sleep(DEEPSEEK_RETRY_SLEEP_SECONDS)
+
+    return ModelResult(
+        model_name="DeepSeek Reasoner",
+        raw_response="",
+        success=False,
+        error_message=(
+            f"{last_error} after {DEEPSEEK_MAX_ATTEMPTS} attempts"
+            if last_error
+            else "DeepSeek failed for an unknown reason."
+        ),
+        latency_seconds=round(time.time() - start, 2),
+    )
+
+
+def call_model_b(clinical_text: str) -> ModelResult:
+    """GPT-OSS-120B via Groq."""
+    start = time.time()
+    try:
+        client = get_groq_client()
+        response = client.chat.completions.create(
+            model="openai/gpt-oss-120b",
+            messages=[
+                {"role": "system", "content": ERROR_CHECK_SYSTEM_PROMPT},
+                {"role": "user", "content": ERROR_CHECK_USER_PROMPT.format(clinical_text=clinical_text)},
+            ],
+            temperature=0.2,
+            max_tokens=4096,
+        )
+        raw = response.choices[0].message.content
+        return _parse_to_result("GPT-OSS-120B", raw, time.time() - start)
+    except Exception as exc:
+        return ModelResult(
+            model_name="GPT-OSS-120B",
+            raw_response="",
+            success=False,
+            error_message=str(exc),
+            latency_seconds=round(time.time() - start, 2),
+        )
+
+
+# ---------------------------------------------------------------------------
+# Main pipeline
+# ---------------------------------------------------------------------------
+
+def run_error_check(croatian_text: str) -> AnalysisResponse:
+    """Full pipeline: translate, then run both models concurrently."""
+    t0 = time.time()
+    english_text = translate_to_english(croatian_text)
+    translation_latency = round(time.time() - t0, 2)
+
+    with ThreadPoolExecutor(max_workers=2) as pool:
+        future_a = pool.submit(call_model_a, english_text)
+        future_b = pool.submit(call_model_b, english_text)
+        result_a = future_a.result()
+        result_b = future_b.result()
+
+    return AnalysisResponse(
+        original_text=croatian_text,
+        translated_text=english_text,
+        model_a_result=result_a,
+        model_b_result=result_b,
+        translation_latency=translation_latency,
+    )
+
+
+# ---------------------------------------------------------------------------
+# CLI test
+# ---------------------------------------------------------------------------
+
+if __name__ == "__main__":
+    import sys, io
+    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace")
+
+    sample = """Bolesnik 68 godina, dolazi zbog bolova u prsištu.
+    Dijagnoza: STEMI prednje stijenke.
+    Terapija: Aspirin 100mg, Klopidogrel 75mg, Ramipril 5mg, Atorvastatin 40mg.
+    Preporučen kontrolni pregled za 7 dana."""
+
+    print("=" * 60)
+    print("ERROR CHECK TEST")
+    print("=" * 60)
+
+    result = run_error_check(sample)
+    print(f"\nTranslation ({result.translation_latency}s):")
+    print(result.translated_text)
+
+    for r in [result.model_a_result, result.model_b_result]:
+        print(f"\n{'=' * 60}")
+        print(f"{r.model_name} ({r.latency_seconds}s):")
+        if r.success:
+            print(f"Summary: {r.summary}")
+            for i, e in enumerate(r.errors, 1):
+                print(f"  Error {i}: [{e.category}/{e.severity}] {e.description}")
+            for i, s in enumerate(r.suggestions, 1):
+                print(f"  Suggestion {i}: [{s.category}] {s.description}")
+        else:
+            print(f"ERROR: {r.error_message}")