Update agents/style_agent.py

agents/style_agent.py

@@ -1,104 +1,126 @@
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import torch
-import difflib
-
-class StyleAgent:
-    def __init__(self, model_name: str = "rajistics/informal_formal_style_transfer", device: str | None = None):
-        """
-        Style Agent
-        - model_name: HF model id for informal -> formal style transfer
-        - device: "cuda" or "cpu" (auto-detect if None)
-        """
-        self.model_name = model_name
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
-        self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-
-        if device is None:
-            self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        else:
-            self.device = device
-
-        self.model.to(self.device)
-
-    def _generate(self, text: str, max_length: int = 128, num_beams: int = 5) -> str:
-        """
-        Internal helper to call the model and get a more formal / professional version.
-        """
-        # Many style-transfer T5 models work directly on raw text
-        inputs = self.tokenizer(
-            text,
-            return_tensors="pt",
-            truncation=True,
-            max_length=max_length
-        ).to(self.device)
-
-        with torch.no_grad():
-            outputs = self.model.generate(
-                **inputs,
-                max_length=max_length,
-                num_beams=num_beams,
-                early_stopping=True
-            )
-
-        styled = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-        return styled.strip()
-
-    def _diff_explanation(self, original: str, styled: str):
-        """
-        Compare original vs styled sentence and return simple word-level changes.
-        """
-        diff = list(difflib.ndiff(original.split(), styled.split()))
-        changes = []
-        current_del = []
-        current_add = []
-
-        for token in diff:
-            if token.startswith("- "):
-                current_del.append(token[2:])
-            elif token.startswith("+ "):
-                current_add.append(token[2:])
-            elif token.startswith("  "):
-                if current_del or current_add:
-                    changes.append({
-                        "from": " ".join(current_del) if current_del else None,
-                        "to": " ".join(current_add) if current_add else None,
-                        "type": self._infer_change_type(current_del, current_add)
-                    })
-                    current_del, current_add = [], []
-
-        if current_del or current_add:
-            changes.append({
-                "from": " ".join(current_del) if current_del else None,
-                "to": " ".join(current_add) if current_add else None,
-                "type": self._infer_change_type(current_del, current_add)
-            })
-
-        changes = [c for c in changes if c["from"] or c["to"]]
-        return changes
-
-    @staticmethod
-    def _infer_change_type(deleted_tokens, added_tokens):
-        if deleted_tokens and not added_tokens:
-            return "deletion"
-        if added_tokens and not deleted_tokens:
-            return "insertion"
-        return "replacement"
-
-    def stylize(self, text: str) -> dict:
-        """
-        Main method for TextDoctor.
-
-        Returns:
-        {
-            "original": ...,
-            "styled": ...,
-            "changes": [ {type, from, to}, ... ]
-        }
-        """
-        styled = self._generate(text)
-        changes = self._diff_explanation(text, styled)
-        return {
-            "original": text,
-            "styled": styled,
-            "changes": changes
-        }
+from typing import Optional
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+import difflib
+
+
+class StyleAgent:
+    def __init__(
+        self,
+        model_name: str = "rajistics/informal_formal_style_transfer",
+        device: Optional[str] = None,
+    ):
+        """
+        Style Agent
+        - model_name: HF model id for informal -> formal style transfer
+        - device: "cuda" or "cpu" (auto-detect if None)
+        """
+        self.model_name = model_name
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+        if device is None:
+            self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        else:
+            self.device = device
+
+        self.model.to(self.device)
+
+    def _generate(self, text: str, max_length: int = 256, num_beams: int = 5) -> str:
+        """
+        Internal helper to call the model and get a more formal / professional version.
+        """
+        # Many style-transfer T5 models work directly on raw text.
+        # If the model card suggests a prefix, add it here, e.g.:
+        # text = "formal: " + text
+
+        inputs = self.tokenizer(
+            text,
+            return_tensors="pt",
+            truncation=True,
+            max_length=max_length,
+        ).to(self.device)
+
+        with torch.no_grad():
+            outputs = self.model.generate(
+                **inputs,
+                max_length=max_length,
+                num_beams=num_beams,
+                early_stopping=True,
+            )
+
+        styled = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return styled.strip()
+
+    def _diff_explanation(self, original: str, styled: str):
+        """
+        Compare original vs styled sentence and return simple word-level changes.
+        """
+        diff = list(difflib.ndiff(original.split(), styled.split()))
+        changes = []
+        current_del = []
+        current_add = []
+
+        for token in diff:
+            if token.startswith("- "):
+                current_del.append(token[2:])
+            elif token.startswith("+ "):
+                current_add.append(token[2:])
+            elif token.startswith("  "):
+                if current_del or current_add:
+                    changes.append(
+                        {
+                            "from": " ".join(current_del) if current_del else None,
+                            "to": " ".join(current_add) if current_add else None,
+                            "type": self._infer_change_type(current_del, current_add),
+                        }
+                    )
+                    current_del, current_add = [], []
+
+        if current_del or current_add:
+            changes.append(
+                {
+                    "from": " ".join(current_del) if current_del else None,
+                    "to": " ".join(current_add) if current_add else None,
+                    "type": self._infer_change_type(current_del, current_add),
+                }
+            )
+
+        changes = [c for c in changes if c["from"] or c["to"]]
+        return changes
+
+    @staticmethod
+    def _infer_change_type(deleted_tokens, added_tokens):
+        if deleted_tokens and not added_tokens:
+            return "deletion"
+        if added_tokens and not deleted_tokens:
+            return "insertion"
+        return "replacement"
+
+    def stylize(self, text: str) -> dict:
+        """
+        Main method for TextDoctor.
+
+        Returns:
+        {
+            "original": ...,
+            "styled": ...,
+            "changes": [ {type, from, to}, ... ],
+            "confidence": float,
+            "agent": "style"
+        }
+        """
+        styled = self._generate(text)
+        changes = self._diff_explanation(text, styled)
+
+        # simple heuristic confidence
+        change_ratio = len(changes) / max(len(text.split()), 1)
+        confidence = max(0.3, 1.0 - change_ratio)
+
+        return {
+            "original": text,
+            "styled": styled,
+            "changes": changes,
+            "confidence": round(confidence, 2),
+            "agent": "style",
+        }