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Dockerfile

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+# Use an official Python runtime as the base image
+FROM python:3.9-slim-buster
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the requirements file into the container
+COPY ./requirements.txt /app/requirements.txt
+
+# Install the Python dependencies
+RUN pip install --no-cache-dir -r /app/requirements.txt
+
+# Copy the FastAPI app and PyTorch Transformer files into the container
+COPY . /app
+
+# Expose the port that FastAPI app will run on
+EXPOSE 8000
+
+# Define the command to run the FastAPI app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

app.py

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+from fastapi import FastAPI, Request
+import uvicorn
+import re
+from model.zero_shot_classification import ZeroShotClassifier
+
+app = FastAPI()
+
+
+@app.post('/predict')
+def predict(request:Request):
+    try:
+        response = request.json()
+        data = response['data']
+        
+        if data == None or data == "":
+            return {
+                'status':False,
+                'result':'Data is empty'
+            }
+        
+        result = ZeroShotClassifier.Predict(data)
+
+        return {
+            'status':True,
+            'result':result
+        }
+    
+    except Exception as err:
+        return {
+            'status':False,
+            'result':
+            err
+        }
+
+
+if __name__ == '__main__':
+    uvicorn.run(app, debug=True)

requirements.txt

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+fastapi
+uvicorn[standard]
+torch
+transformers
+requests