initial commit

autotests/helm/values.yaml

@@ -25,8 +25,8 @@ global:
     activeDeadlineSeconds: 3600 # 1h
 
   env:
-    PARTICIPANT_NAME: <REPLACE_WITH_USERNAME>
-    api_host: http://inca-smc-mlops-challenge-solution.default.svc.cluster.local/<REPLACE_WITH_ENDPOINT>
+    PARTICIPANT_NAME: AssanaliAbu
+    api_host: http://inca-smc-mlops-challenge-solution.default.svc.cluster.local/process
 
     # K6, do not edit!
     K6_PROMETHEUS_RW_SERVER_URL: http://kube-prometheus-stack-prometheus.monitoring.svc.cluster.local:9090/api/v1/write

solution/helm/Dockerfile

@@ -0,0 +1,23 @@
+# Use a base image
+FROM python:3.8-slim-buster
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy project files into the Docker image
+COPY requirements.txt /app
+COPY app.py /app
+COPY inference.py /app
+COPY models.py /app
+
+# Install dependencies
+RUN pip install -r requirements.txt
+
+# Expose the port on which the application will run
+EXPOSE 8000
+
+# Set the command to run the application when the container starts
+# CMD ["python", "app.py"]
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]
+
+

solution/helm/app.py

@@ -0,0 +1,47 @@
+from fastapi import FastAPI, Request
+from models import initialize_models, models
+from inference import run_inference
+import asyncio
+
+app = FastAPI()
+
+@app.on_event("startup")
+async def startup_event():
+    try:
+        await initialize_models()
+    except RuntimeError as e:
+        # Handle the initialization error and return an error response if needed
+        return {"error": str(e)}
+
+
+@app.post("/process")
+async def process_text(request: Request):
+    try:
+        data = await request.json()
+        text = data.strip('"')
+
+        tasks = []
+        for model_name in models.keys():
+            task = asyncio.create_task(run_inference(model_name, text))
+            tasks.append(task)
+
+        results = await asyncio.gather(*tasks)
+
+        response = {}
+        for model_name, result in zip(models.keys(), results):
+            response[model_name] = {
+                "score": result["score"],
+                "label": result["label"]
+            }
+
+        return response
+
+    except Exception as e:
+        # Handle any exceptions and return an error response
+        return {"error": str(e)}
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="localhost", port=8000)

solution/helm/inference.py

@@ -0,0 +1,12 @@
+import asyncio
+from models import initialized_models, models
+from typing import Dict
+from transformers import pipeline
+
+# Run inference on a specific model
+async def run_inference(model_name: str, text: str) -> Dict[str, float]:
+    model = initialized_models[model_name]
+    output = await asyncio.to_thread(model, text)
+    score = output[0]['score']
+    label = output[0]['label']
+    return {"score": score, "label": label}

solution/helm/models.py

@@ -0,0 +1,22 @@
+import asyncio
+from transformers import pipeline
+
+# Define the models and their corresponding tasks
+models = {
+    "cardiffnlp": ("sentiment-analysis", "cardiffnlp/twitter-xlm-roberta-base-sentiment"),
+    "ivanlau": ("text-classification", "ivanlau/language-detection-fine-tuned-on-xlm-roberta-base"),
+    "svalabs": ("text-classification", "svalabs/twitter-xlm-roberta-crypto-spam"),
+    "EIStakovskii": ("text-classification", "EIStakovskii/xlm_roberta_base_multilingual_toxicity_classifier_plus"),
+    "jy46604790": ("text-classification", "jy46604790/Fake-News-Bert-Detect")
+}
+
+# Initialize the models
+initialized_models = {}
+
+async def initialize_models():
+    try:
+        for model_name, (task, model) in models.items():
+            initialized_models[model_name] = pipeline(task, model=model)
+    except Exception as e:
+        # Handle any exceptions and raise an error
+        raise RuntimeError(f"Failed to initialize models: {str(e)}")

solution/helm/requirements.txt

@@ -0,0 +1,6 @@
+fastapi==0.68.0
+transformers==4.27.3
+uvicorn==0.15.0
+asyncio>=3.4.3
+torch==1.9.0
+