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Dockerfile

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+# Use an official Python base image
+FROM python:3.10-slim
+# Set working directory
+WORKDIR /code
+# Install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+# Copy app files
+COPY main.py .
+# Expose port for Hugging Face Spaces
+EXPOSE 7860
+# Start the FastAPI app using uvicorn
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+import os
+os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf-cache"
+os.environ["HF_HOME"] = "/tmp/hf-home"
+
+from fastapi import FastAPI, Request
+from pydantic import BaseModel
+from transformers import pipeline
+
+# Define FastAPI app
+app = FastAPI()
+
+# Load zero-shot classification pipeline
+classifier = pipeline(
+    "zero-shot-classification",
+    model="MoritzLaurer/deberta-v3-large-zeroshot-v2.0"
+)
+
+
+# Define input schema
+class InputText(BaseModel):
+    text: str
+
+@app.post("/classify")
+async def classify_text(data: InputText):
+    candidate_labels = ["contains electronic components", "does not contain electronic components"]
+    result = classifier(data.text, candidate_labels, multi_label=False)
+    return {
+        "input": data.text,
+        "label": result["labels"][0],
+        "score": result["scores"][0]
+    }

requirements.txt

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+fastapi
+uvicorn
+transformers[sentencepiece]
+torch