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app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import pipeline
+from datetime import datetime
+import os
+
+app = FastAPI()
+
+# Load model
+classifier = pipeline(
+    "zero-shot-classification",
+    model="MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli"
+)
+
+# Pydantic input model
+class EmailRequest(BaseModel):
+    email: str
+
+# Endpoint to classify email
+@app.post("/classify")
+def classify_email(request: EmailRequest):
+    labels = ["Approved", "Rejected", "Unclear"]
+    result = classifier(request.email, candidate_labels=labels)
+    top_label = result['labels'][0]
+    confidence = round(result['scores'][0], 2)
+
+    # Logging
+    log_line = f"{datetime.now()} | {top_label:<9} ({confidence}) | {request.email}\n"
+    os.makedirs("logs", exist_ok=True)
+    with open("logs/inputs.log", "a", encoding="utf-8") as f:
+        f.write(log_line)
+
+    return {"label": top_label, "confidence": confidence}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run("app:app", host="0.0.0.0", port=7860)

requirements.txt

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+fastapi
+uvicorn
+transformers
+torch