Initial FastAPI deployment

.dockerignore

@@ -0,0 +1,7 @@
+venv
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.env
+.model_cache

.gitignore

@@ -0,0 +1,6 @@
+venv/
+model/
+__pycache__/
+*.pyc
+.DS_Store
+.env

Dockerfile

@@ -0,0 +1,23 @@
+# Dockerfile
+FROM python:3.10-slim
+
+WORKDIR /app
+
+# system deps
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential curl git && rm -rf /var/lib/apt/lists/*
+
+# copy reqs and install packages
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# copy app and download model during build
+COPY download_model.py .
+COPY main.py .
+
+# run download script to save model into the image
+RUN python download_model.py
+
+# expose and run
+EXPOSE 8000
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

README.md

@@ -1,12 +1,2 @@
----
-title: Complaint Classification Ai
-emoji: 🏢
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 5.49.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Complaint-Classification-AI-
+This is for my FYP "Complaint Ecosystem App". To classify my users' complaints. 

download_model.py

@@ -0,0 +1,14 @@
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+model_name = "joeddav/xlm-roberta-large-xnli"
+save_path = "./models/xlm-roberta-large-xnli"
+
+print(f"Downloading model {model_name}...")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+print(f"Saving model to {save_path} ...")
+tokenizer.save_pretrained(save_path)
+model.save_pretrained(save_path)
+print("Model saved locally. You can now use it offline!")
+

main.py

@@ -0,0 +1,24 @@
+# main.py
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import pipeline
+import os
+
+app = FastAPI(title="Complaint Classifier")
+
+MODEL_PATH = os.getenv("MODEL_PATH", "./model")
+print("Loading model from", MODEL_PATH)
+classifier = pipeline("zero-shot-classification", model=MODEL_PATH)
+
+class Request(BaseModel):
+    text: str
+    labels: list[str]
+
+@app.post("/classify")
+def classify(req: Request):
+    result = classifier(req.text, candidate_labels=req.labels)
+    return {
+        "label": result["labels"][0],
+        "score": float(result["scores"][0]),
+        "all": list(zip(result["labels"], [float(s) for s in result["scores"]]))
+    }

requirements.txt

@@ -0,0 +1,7 @@
+fastapi
+uvicorn[standard]
+transformers
+sentencepiece
+protobuf
+torch==2.2.2+cpu
+--extra-index-url https://download.pytorch.org/whl/cpu