Create grammar_agent.py

agents/grammar_agent.py

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+from typing import Optional
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+import difflib
+
+
+class GrammarAgent:
+    def __init__(
+        self,
+        model_name: str = "vennify/t5-base-grammar-correction",
+        device: Optional[str] = None,
+    ):
+        """
+        Grammar Agent
+        - model_name: HF model id
+        - 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 corrected text.
+        """
+        prefixed = "grammar: " + text  # T5-style task prefix
+
+        inputs = self.tokenizer(
+            prefixed,
+            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,
+            )
+
+        corrected = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return corrected.strip()
+
+    def _diff_explanation(self, original: str, corrected: str):
+        """
+        Create a simple, human-readable explanation of changes.
+        Returns a list of {type, from, to}.
+        """
+        diff = list(difflib.ndiff(original.split(), corrected.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):
+        """
+        Very simple heuristic for change type.
+        You can later improve this with more logic.
+        """
+        if deleted_tokens and not added_tokens:
+            return "deletion"
+        if added_tokens and not deleted_tokens:
+            return "insertion"
+        return "replacement"
+
+    def correct(self, text: str) -> dict:
+        """
+        Main method your system will call.
+        Returns a dict:
+        {
+            "original": ...,
+            "corrected": ...,
+            "changes": [ {type, from, to}, ... ],
+            "confidence": float,
+            "agent": "grammar"
+        }
+        """
+        corrected = self._generate(text)
+        changes = self._diff_explanation(text, corrected)
+
+        # simple heuristic confidence based on how much was changed
+        change_ratio = len(changes) / max(len(text.split()), 1)
+        confidence = max(0.3, 1.0 - change_ratio)
+
+        return {
+            "original": text,
+            "corrected": corrected,
+            "changes": changes,
+            "confidence": round(confidence, 2),
+            "agent": "grammar",
+        }