Initial FastAPI topic predictor

Dockerfile

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+FROM python:3.10
+
+WORKDIR /code
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI
+from transformers import pipeline
+
+app = FastAPI()
+
+# Load model at startup (only once)
+classifier = pipeline(
+    "zero-shot-classification",
+    model="MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33"
+)
+
+subject_labels = [
+    "Physics", "Chemistry", "Biology", "Astronomy",
+    "Earth Science", "Environmental Science",
+    "Algebra", "Geometry", "Calculus", "Statistics",
+    "Probability", "Number Theory",
+    "English Language", "English Literature",
+    "Tamil Language", "Tamil Literature",
+    "History", "Geography", "Political Science", "Economics",
+    "Sociology", "Psychology", "Philosophy",
+    "Computer Science", "Data Science", "Artificial Intelligence",
+    "Robotics", "Biotechnology", "Engineering",
+    "Fine Arts", "Music", "Dance", "Theater",
+    "Business Studies", "Accountancy", "Entrepreneurship",
+    "Physical Education", "Health Science"
+]
+
+@app.post("/predict/")
+async def predict_topic(text: str):
+    result = classifier(
+        text,
+        candidate_labels=subject_labels,
+        hypothesis_template="This text is about {}."
+    )
+    predicted_topic = result["labels"][0]
+    return {"topic": predicted_topic}

requirements.txt

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+fastapi
+uvicorn
+transformers
+torch