solution

autotests/app/Dockerfile

@@ -0,0 +1,12 @@
+FROM grafana/xk6 as xk6_builder
+RUN xk6 build --output k6 --with github.com/szkiba/xk6-faker@latest \
+    --with github.com/grafana/xk6-output-prometheus-remote@latest 
+
+
+FROM grafana/k6
+
+WORKDIR /app/
+COPY src/main.js /app/
+COPY --from=xk6_builder /xk6/k6 /usr/bin/k6 
+
+ENTRYPOINT k6 run -o xk6-prometheus-rw main.js

autotests/app/src/main.js

@@ -107,4 +107,4 @@ export function post_radom() {
         "model 5 score valid": res => res.json().jy46604790.score > 0,
         "model 5 label valid": res => res.json().jy46604790.label === "LABEL_0" || res.json().jy46604790.label === "LABEL_1",
     });
-}
+}

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: Khush Patel
+    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/.dockerignore

@@ -0,0 +1,23 @@
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+env/
+venv/
+pip-log.txt
+pip-delete-this-directory.txt
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.log
+.git
+.gitignore
+.mypy_cache
+.pytest_cache
+.hypothesis
+.idea

solution/Dockerfile

@@ -0,0 +1,11 @@
+FROM python:3.10.11-slim-bullseye
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+COPY ./app.py /code/app.py
+
+RUN pip install -r requirements.txt 
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

solution/app.py

@@ -0,0 +1,181 @@
+# Import required libraries
+from fastapi import FastAPI
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig  
+import numpy as np  
+from scipy.special import softmax 
+from fastapi import Request, Body
+# Initialize a FastAPI app instance
+app = FastAPI()
+
+# Define a default route
+@app.get('/')
+async def home():
+    html = (
+        'Mlops Challenge'
+    )
+    return html.format(format)
+
+
+# Load the trained models
+print("Model Loading Started...")
+
+# CardiffNLP Model
+cardiffnlp_MODEL = f"cardiffnlp/twitter-xlm-roberta-base-sentiment"
+cardiffnlp_tokenizer = AutoTokenizer.from_pretrained(cardiffnlp_MODEL) 
+cardiffnlp_model = AutoModelForSequenceClassification.from_pretrained(cardiffnlp_MODEL)
+print("cardiffnlp Model Loaded!")
+
+# ivanlau Model
+ivanlau_MODEL = f"ivanlau/language-detection-fine-tuned-on-xlm-roberta-base"
+ivanlau_tokenizer = AutoTokenizer.from_pretrained(ivanlau_MODEL)
+ivanlau_config = AutoConfig.from_pretrained(ivanlau_MODEL)
+ivanlau_model = AutoModelForSequenceClassification.from_pretrained(ivanlau_MODEL)
+print("ivanlau Model Loaded!")
+
+# svalabs Model
+svalabs_MODEL = f"svalabs/twitter-xlm-roberta-crypto-spam"
+svalabs_tokenizer = AutoTokenizer.from_pretrained(svalabs_MODEL) 
+svalabs_model = AutoModelForSequenceClassification.from_pretrained(svalabs_MODEL)
+print("svalabs Model Loaded!")
+
+# EIStakovskii Model
+EIStakovskii_MODEL = f"EIStakovskii/xlm_roberta_base_multilingual_toxicity_classifier_plus"
+EIStakovskii_tokenizer = AutoTokenizer.from_pretrained(EIStakovskii_MODEL)
+EIStakovskii_model = AutoModelForSequenceClassification.from_pretrained(EIStakovskii_MODEL)
+print("EIStakovskii Model Loaded!")
+
+# jy46604790 Model
+jy46604790_MODEL = f"jy46604790/Fake-News-Bert-Detect"
+jy46604790_tokenizer = AutoTokenizer.from_pretrained(jy46604790_MODEL)
+jy46604790_model = AutoModelForSequenceClassification.from_pretrained(jy46604790_MODEL)
+print("jy46604790 Model Loaded!")
+
+
+def preprocess(text: str) -> str:
+    new_text = []
+    for t in text.split(" "):
+        t = '@user' if t.startswith('@') and len(t) > 1 else t
+        t = 'http' if t.startswith('http') else t
+        new_text.append(t)
+    return " ".join(new_text)
+
+
+# Define a function for each of the trained models to preprocess the input and return the prediction
+def cardiffnlp_precessing(text: str) -> dict:
+    encoded_input = cardiffnlp_tokenizer(text, return_tensors='pt')
+    outputs = cardiffnlp_model(**encoded_input)
+    outputs = outputs.logits.detach().numpy()
+
+    scores = softmax(outputs)[0] 
+
+    max_index = scores.argmax() 
+
+    id2label = ["NEGATIVE","NEUTRAL","POSITIVE"]
+
+    l = id2label[max_index]
+    s = scores[max_index]
+    result = {
+    "score": float(s),
+    "label": l
+    } 
+    return result
+
+def ivanlau_precessing(text: str) -> dict:
+    encoded_input = ivanlau_tokenizer(text, return_tensors='pt')
+    outputs = ivanlau_model(**encoded_input)
+    outputs = outputs.logits.detach().numpy()
+
+    scores = softmax(outputs)[0] 
+
+    max_index = scores.argmax() 
+
+
+    l = ivanlau_config.id2label[max_index]
+    s = scores[max_index]
+    result = {
+            "score": float(s),
+            "label": l
+        } 
+    return result
+
+def svalabs_precessing(text: str) -> dict:
+    encoded_input = svalabs_tokenizer(text, return_tensors='pt')
+    outputs = svalabs_model(**encoded_input)
+    outputs = outputs.logits.detach().numpy()
+
+    scores = softmax(outputs)[0] 
+
+    id2label = ["HAM","SPAM"]
+
+    max_index = scores.argmax() 
+
+    l = id2label[max_index]
+    s = scores[max_index]
+    result = {
+    "score": float(s),
+    "label": l
+    } 
+    return result
+
+def EIStakovskii_precessing(text: str) -> dict:
+    encoded_input = EIStakovskii_tokenizer(text, return_tensors='pt')
+    outputs = EIStakovskii_model(**encoded_input)
+    outputs = outputs.logits.detach().numpy()
+
+    scores = softmax(outputs)[0] 
+
+    max_index = scores.argmax() 
+
+    id2label = ["LABEL_0","LABEL_1"]
+
+    l = id2label[max_index]
+    s = scores[max_index]
+    result = {
+    "score": float(s),
+    "label": l
+    } 
+
+    return result
+
+def jy46604790_precessing(text: str) -> dict:
+    encoded_input = jy46604790_tokenizer(text, return_tensors='pt')
+    outputs = jy46604790_model(**encoded_input)
+    outputs = outputs.logits.detach().numpy()
+
+    scores = softmax(outputs)[0] 
+
+    max_index = scores.argmax() 
+
+
+    id2label = ["LABEL_1","LABEL_0"]
+
+    l = id2label[max_index]
+    s = scores[max_index]
+    result = {
+    "score": float(s),
+    "label": l
+    } 
+
+    return result
+
+# Define a process route
+@app.post("/process")
+async def predict(request: Request): 
+    # start = time.time()
+    text = preprocess((await request.body()).decode())
+    results_cardiffnlp =  cardiffnlp_precessing(text)
+    results_ivanlau = ivanlau_precessing(text)
+    results_svalabs = svalabs_precessing(text)
+    results_EIStakovskii = EIStakovskii_precessing(text)
+    results_jy46604790 = jy46604790_precessing(text)
+    # end = time.time()
+    # total_time = end - start
+    # print(f"Total inference time with preprocessing: {total_time}")
+    result = {
+        "cardiffnlp": results_cardiffnlp,
+        "ivanlau":results_ivanlau,
+        "svalabs":results_svalabs,
+        "EIStakovskii":results_EIStakovskii,
+        "jy46604790":results_jy46604790,
+        }
+    return result

solution/requirements.txt

@@ -0,0 +1,8 @@
+sentencepiece 
+transformers
+torch
+scipy
+fastapi
+uvicorn[standard]
+pydantic   
+protobuf==3.13.0