Update app.py

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from typing import List, Dict, Any
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline, AutoModelForTokenClassification
+import torch
+import re
+
+# --- Load your fine-tuned model from Hugging Face Hub ---
+MODEL_REPO = "sathish2352/email-classifier-model"
+PII_MODEL = "Davlan/xlm-roberta-base-ner-hrl"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_REPO)
+ner_tokenizer = AutoTokenizer.from_pretrained(PII_MODEL)
+ner_model = AutoModelForTokenClassification.from_pretrained(PII_MODEL)
+ner_pipe = pipeline("token-classification", model=ner_model, tokenizer=ner_tokenizer, aggregation_strategy="simple")
+model.eval()
+
+app = FastAPI()
+
+class EmailInput(BaseModel):
+    input_email_body: str
+
+# --- PII Masking Function ---
+import re
+from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
+def mask_pii_multilingual(text: str):
+
+    # Load model only once globally if needed
+    model_name = "Davlan/xlm-roberta-base-ner-hrl"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForTokenClassification.from_pretrained(model_name)
+    ner_pipe = pipeline("token-classification", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
+
+    regex_patterns = {
+        "email": r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}\b",
+        "phone_number": r"(?:\+?\d{1,3})?[-.\s]?\(?\d{1,4}\)?[-.\s]?\d{2,4}[-.\s]?\d{2,4}[-.\s]?\d{2,4}",
+        "dob": r"\b(0?[1-9]|[12][0-9]|3[01])[-/](0?[1-9]|1[012])[-/](19[5-9]\d|20[0-3]\d)\b",
+        "aadhar_num": r"\b\d{4}[\s-]?\d{4}[\s-]?\d{4}\b",
+        "credit_debit_no": r"\b(?:\d{4}[\s-]?){3}\d{4}\b",
+        "cvv_no": r"\b\d{3,4}\b",
+        "expiry_no": r"\b(0[1-9]|1[0-2])[/-]?(?:\d{2}|\d{4})\b"
+    }
+
+    entities = []
+    masked_text = text
+    offsets = []
+
+    # Step 1: Apply regex PII masking first
+    for entity_type, pattern in regex_patterns.items():
+        for match in re.finditer(pattern, text):
+            start, end = match.start(), match.end()
+            if any(start < e[1] and end > e[0] for e in offsets):
+                continue
+            token = f"[{entity_type}]"
+            entity_val = text[start:end]
+            masked_text = masked_text[:start] + token + masked_text[end:]
+            offsets.append((start, end))
+            entities.append({
+                "position": [start, end],
+                "classification": entity_type,
+                "entity": entity_val
+            })
+
+    # Step 2: Run NER on updated masked_text to avoid overlap
+    ner_results = ner_pipe(masked_text)
+    for ent in ner_results:
+        start, end = ent["start"], ent["end"]
+        if ent["entity_group"] != "PER":
+            continue
+        if any(start < e[1] and end > e[0] for e in offsets):
+            continue
+        token = "[full_name]"
+        entity_val = text[start:end]
+        masked_text = masked_text[:start] + token + masked_text[end:]
+        entities.append({
+            "position": [start, end],
+            "classification": "full_name",
+            "entity": entity_val
+        })
+        offsets.append((start, end))
+
+    # Sort final result
+    entities.sort(key=lambda x: x["position"][0])
+    return masked_text, entities
+
+
+# --- API Endpoint ---
+@app.post("/classify")
+def classify_email(input: EmailInput):
+    original_text = input.input_email_body
+
+    # Step 1: Mask PII
+    masked_text, masked_entities = mask_pii_multilingual(original_text)
+
+    # Step 2: Classification
+    inputs = tokenizer(masked_text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        pred = torch.argmax(logits, dim=1).item()
+
+    label_map = {0: "Incident", 1: "Request", 2: "Change", 3: "Problem"}
+
+    return {
+        "input_email_body": original_text,
+        "list_of_masked_entities": masked_entities,
+        "masked_email": masked_text,
+        "category_of_the_email": label_map[pred]
+    }
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run("app:app", host="0.0.0.0", port=7860)
+