feat: add AbstractDraft node

custom_nodes/research/__init__.py

@@ -13,7 +13,8 @@ class ResearchExtension(ComfyExtension):
         from custom_nodes.research.style_profile import StyleProfileExtract
         from custom_nodes.research.reference_paper_select import ReferencePaperSelect
         from custom_nodes.research.section_plan import SectionPlan
-        return [PaperSearch, PaperClaimExtract, ClaimEvidenceAssemble, StyleProfileExtract, ReferencePaperSelect, SectionPlan]
+        from custom_nodes.research.abstract_draft import AbstractDraft
+        return [PaperSearch, PaperClaimExtract, ClaimEvidenceAssemble, StyleProfileExtract, ReferencePaperSelect, SectionPlan, AbstractDraft]
 
 
 async def comfy_entrypoint() -> ComfyExtension:

custom_nodes/research/abstract_draft.py

@@ -0,0 +1,85 @@
+"""AbstractDraft node - generate abstract text."""
+import json
+from typing_extensions import override
+from comfy_api.latest import ComfyNode, io
+
+
+class AbstractDraft(io.ComfyNode):
+    """Generate an abstract draft based on claims and style profile."""
+
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="AbstractDraft",
+            display_name="Draft Abstract",
+            category="Research",
+            inputs=[
+                io.String.Input(
+                    "claims",
+                    display_name="Claims (JSON)",
+                    default="[]",
+                    multiline=True,
+                ),
+                io.String.Input(
+                    "style_profile",
+                    display_name="Style Profile (JSON)",
+                    default="{}",
+                    multiline=True,
+                ),
+                io.Int.Input(
+                    "max_words",
+                    display_name="Max Words",
+                    default=250,
+                    min=100,
+                    max=400,
+                    step=10,
+                ),
+            ],
+            outputs=[
+                io.String.Output(display_name="Abstract Text"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, claims: str, style_profile: str, max_words: int) -> io.NodeOutput:
+        try:
+            claims_list = json.loads(claims) if claims else []
+            style = json.loads(style_profile) if style_profile else {}
+        except json.JSONDecodeError:
+            claims_list = []
+            style = {}
+
+        # Extract key information from claims
+        performance_claims = [c for c in claims_list if c.get("type") == "performance"]
+        method_claims = [c for c in claims_list if c.get("type") == "method"]
+
+        sentences = []
+
+        # Background sentence
+        sentences.append("Medical image segmentation remains a critical challenge in clinical practice.")
+
+        # Method sentence
+        if method_claims:
+            method_text = method_claims[0].get("text", "We propose a novel approach.")
+            sentences.append(f"In this work, {method_text}")
+        else:
+            sentences.append("We present a deep learning framework for automated segmentation.")
+
+        # Results sentence
+        if performance_claims:
+            perf_text = performance_claims[0].get("text", "Our method achieves strong performance.")
+            sentences.append(f"Experimental results demonstrate that {perf_text}")
+        else:
+            sentences.append("Our approach achieves competitive performance on benchmark datasets.")
+
+        # Conclusion sentence
+        sentences.append("These findings suggest potential for clinical application.")
+
+        abstract = " ".join(sentences)
+
+        # Apply style if available
+        tone = style.get("tone_notes", "neutral")
+        if tone == "enthusiastic":
+            abstract = abstract.replace("competitive", "state-of-the-art").replace("strong", "exceptional")
+
+        return io.NodeOutput(abstract_text=abstract)