SECTION_ORDER added

handler.py

@@ -9,6 +9,7 @@ PULSE ECG Handler - Deterministic ECG Analysis Model (app.py uyumlu)
 """
 
 import os
+import re
 import datetime
 import torch
 import hashlib
@@ -146,11 +147,7 @@ def get_conv_vote_filename():
 def vote_last_response(state, vote_type, model_selector):
     try:
         with open(get_conv_vote_filename(), "a") as fout:
-            data = {
-                "type": vote_type,
-                "model": model_selector,
-                "state": state,
-            }
+            data = {"type": vote_type, "model": model_selector, "state": state}
             fout.write(json.dumps(data) + "\n")
         _safe_upload(get_conv_vote_filename())
     except Exception as e:
@@ -158,7 +155,7 @@ def vote_last_response(state, vote_type, model_selector):
 
 # Yalın uzantı listeleri (sorunlu formatlar çıkarıldı)
 IMAGE_EXTS = {"jpg", "jpeg", "png", "bmp", "gif", "tiff", "webp", "jfif"}
-# HEIC/HEIF: pillow-heif yoksa desteklemeyelim
+# HEIC/HEIF: pillow-heif yoksa destekleme
 try:
     import pillow_heif  # noqa: F401
     IMAGE_EXTS.update({"heic", "heif"})
@@ -234,6 +231,73 @@ def process_image_input(image_input):
         return process_base64_image(image_input["image"])
     raise ValueError("Unsupported image input format")
 
+# ---------- Şablon dayatma (post-format) ----------
+
+SECTION_ORDER = [
+    "Step 1: Rhythm Analysis",
+    "Step 2: Heart Rate Analysis",
+    "Step 3: Cardiac Axis Analysis",
+    "Step 4: P Wave Analysis",
+    "Step 5: PR Interval Analysis",
+    "Step 6: QRS Complex Analysis",
+    "Step 7: ST Segment Analysis",
+    "Step 8: T Wave Analysis",
+    "Step 9: QT/QTc Interval Analysis",
+    "Structured Clinical Impression:",
+]
+
+_SECTION_RE = re.compile(
+    r"(Step\s*1:\s*Rhythm Analysis|"
+    r"Step\s*2:\s*Heart Rate Analysis|"
+    r"Step\s*3:\s*Cardiac Axis Analysis|"
+    r"Step\s*4:\s*P Wave Analysis|"
+    r"Step\s*5:\s*PR Interval Analysis|"
+    r"Step\s*6:\s*QRS Complex Analysis|"
+    r"Step\s*7:\s*ST Segment Analysis|"
+    r"Step\s*8:\s*T Wave Analysis|"
+    r"Step\s*9:\s*QT/QTc Interval Analysis|"
+    r"Structured Clinical Impression:)",
+    flags=re.IGNORECASE
+)
+
+def _enforce_section_template(text: str) -> str:
+    """
+    Model çıktısını yakalayıp Step 1–9 + Structured başlıklarını sırayla ve eksiksiz
+    döndürecek şekilde biçimler. Eksik bölümler 'Normal...' notuyla doldurulur.
+    """
+    pieces = _SECTION_RE.split(text)
+    found = {}
+    prefix = None
+
+    if pieces:
+        if not _SECTION_RE.match(pieces[0] or ""):
+            prefix = (pieces[0] or "").strip()
+
+        i = 1
+        while i + 1 < len(pieces):
+            heading = pieces[i].strip()
+            content = pieces[i + 1].strip()
+            for canonical in SECTION_ORDER:
+                if heading.lower().startswith(canonical.lower().rstrip(":")):
+                    found[canonical] = content
+                    break
+            i += 2
+
+    filled = []
+    for sec in SECTION_ORDER:
+        val = (found.get(sec, "") or "").strip()
+        if not val:
+            if sec.startswith("Step"):
+                val = "Normal. No definite abnormality detected in this section based on the provided ECG image."
+            else:
+                val = "Overall impression: No acute life-threatening abnormality identified. Correlate clinically."
+        filled.append(f"{sec}\n{val}")
+
+    if prefix:
+        filled[0] = filled[0] + f"\n\n(Additional notes captured before Step 1): {prefix}"
+
+    return "\n\n".join(filled)
+
 # ---------- Oturum / Konuşma ----------
 
 class InferenceDemo(object):
@@ -311,7 +375,6 @@ def _build_prompt(chatbot, user_text: str) -> str:
         use_wrap = False
 
     if use_wrap:
-        # <im_start><image></im_end>\n + metin
         inp = f"{DEFAULT_IM_START_TOKEN}{DEFAULT_IMAGE_TOKEN}{DEFAULT_IM_END_TOKEN}\n{user_text}"
     else:
         inp = f"{DEFAULT_IMAGE_TOKEN}\n{user_text}"
@@ -335,17 +398,14 @@ def generate_response(message_text,
     if not LLAVA_AVAILABLE:
         return {"error": "LLaVA modules not available"}
 
-    # Zorunlu girişler
     if not message_text or not image_input:
         return {"error": "Both message text and image are required"}
 
-    # Chatbot al
     chatbot = chat_manager.get_chatbot(
         args, args.model_path if args else "PULSE-ECG/PULSE-7B",
         tokenizer, model, image_processor, context_len
     )
 
-    # İsteğe bağlı conv override
     if conv_mode_override and conv_mode_override in conv_templates:
         chatbot.conversation = conv_templates[conv_mode_override].copy()
     else:
@@ -381,11 +441,10 @@ def generate_response(message_text,
         processed = process_images([image], chatbot.image_processor, chatbot.model.config)
 
         if isinstance(processed, torch.Tensor):
-            # (C,H,W) / (B,C,H,W) / (B,T,C,H,W)
             if processed.ndim == 3:
                 image_tensor = processed.unsqueeze(0)  # (1,C,H,W)
             elif processed.ndim == 4:
-                image_tensor = processed  # (B,C,H,W)
+                image_tensor = processed              # (B,C,H,W)
             elif processed.ndim == 5:
                 b, t, c, h, w = processed.shape
                 image_tensor = processed.reshape(b * t, c, h, w)  # (B*T,C,H,W)
@@ -436,7 +495,7 @@ def generate_response(message_text,
                 images=image_tensor,
                 do_sample=False,                  # deterministik
                 max_new_tokens=int(max_output_tokens),
-                min_new_tokens=600,               # en az bu kadar üret (step başlıkları garanti)
+                min_new_tokens=800,               # en az bu kadar üret (step başlıkları garanti)
                 repetition_penalty=float(repetition_penalty),
                 use_cache=False,
                 pad_token_id=eos_id,
@@ -449,6 +508,9 @@ def generate_response(message_text,
         gen = outputs[0][input_ids.shape[1]:]
         response = chatbot.tokenizer.decode(gen, skip_special_tokens=True)
 
+        # ŞABLON ZORLAMA: Step1–9 + Structured
+        response = _enforce_section_template(response)
+
         # Konuşmaya yerleştir
         if chatbot.conversation.messages and isinstance(chatbot.conversation.messages[-1], list):
             chatbot.conversation.messages[-1][-1] = response
@@ -609,7 +671,7 @@ def initialize_model():
                 self.load_8bit = bool(int(os.getenv("LOAD_8BIT", "0")))
                 self.load_4bit = bool(int(os.getenv("LOAD_4BIT", "0")))
                 self.debug = bool(int(os.getenv("DEBUG", "0")))
-        # args globaline ata
+
         globals()["args"] = Args()
 
         model_name = get_model_name_from_path(args.model_path)