Add FastAPI intent classifier app

README.md

@@ -1,12 +1,8 @@
 ---
-title: Suplyd Intent Api
-emoji: 💬
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 5.0.1
-app_file: app.py
-pinned: false
----
-
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+title: SYPLYD MARBERT API
+emoji: 🌍
+colorFrom: green
+colorTo: blue
+sdk: docker
+app_port: 8000
+---

app.py

@@ -1,29 +1,32 @@
-from fastapi import FastAPI, HTTPException
+from fastapi import FastAPI
 from pydantic import BaseModel
 from transformers import pipeline
 
-app = FastAPI()
+# Define the data model for the request body
+class RequestModel(BaseModel):
+    text: str
 
-classifier = pipeline("text-classification", model="ShazaAly/suplyd-intent-classifier")
+# Initialize the FastAPI app
+app = FastAPI()
 
-class IntentRequest(BaseModel):
-    text: str
+# Load the model pipeline once when the app starts
+# This is efficient as it doesn't reload the model on every request
+try:
+    classifier = pipeline("text-classification", model="ShazaAly/syplyd-marbert-1")
+    print("Model loaded successfully!")
+except Exception as e:
+    classifier = None
+    print(f"Error loading model: {e}")
 
-class IntentResponse(BaseModel):
-    label: str
-    confidence: float
+@app.get("/")
+def read_root():
+    return {"status": "online", "model": "ShazaAly/syplyd-marbert-1"}
 
-@app.post("/classify", response_model=IntentResponse)
-def classify_intent(req: IntentRequest):
-    if not req.text.strip():
-        return {"label": "غير ذلك", "confidence": 1.0}
+@app.post("/classify")
+def classify_intent(request: RequestModel):
+    if not classifier:
+        return {"error": "Model could not be loaded."}, 500
 
-    try:
-        results = classifier(req.text)
-        top = results[0]
-        return {
-            "label": top["label"],
-            "confidence": top["score"]
-        }
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+    # The text is in request.text
+    results = classifier(request.text)
+    return results[0] # Return the first (and only) result dictionary

requirements.txt

@@ -2,3 +2,6 @@ fastapi
 uvicorn
 transformers
 torch
+torchvision
+torchaudio
+sentencepiece # MARBERT dependency