PR5: Write a Kubernetes deployment YAML (Deployment, Service) for you…

…r model's UI (Streamlit, Gradio).

homework_9/pr4/requirements.txt

@@ -3,9 +3,6 @@ numpy==1.26.4
 pandas==2.2.1
 torch==2.2.1
 -f https://download.pytorch.org/whl/cpu
-streamlit==1.39.0
-pytest==8.3.0
-gradio==4.44.1
 fastapi[standard]==0.114.0
 pydantic==2.8.2
 uvicorn==0.30.5

homework_9/pr5/Dockerfile

@@ -0,0 +1,18 @@
+# Use official Python image
+FROM python:3.11
+
+# Set working directory
+WORKDIR /app
+
+# Copy files
+COPY . .
+
+# Install dependencies
+RUN pip install -r requirements.txt
+
+# Expose the port Streamlit runs on
+EXPOSE 8501
+
+# Command to run the application
+#CMD ["streamlit", "run", "main.py", "--server.address=0.0.0.0"]
+CMD ["streamlit", "run", "main.py"]

homework_9/pr5/README.md

@@ -0,0 +1,90 @@
+## correct home directory
+
+```bash
+cd homework_9/pr5
+```
+
+## start minikube
+
+```bash
+minikube start
+eval $(minikube -p minikube docker-env)
+```
+
+
+## build docker image
+
+```bash
+docker build -t streamlit-app:latest .
+```
+
+## deploy to minikube
+
+```bash
+kubectl apply -f k8s_deployment.yaml
+```
+
+## get url
+
+```bash
+minikube service streamlit-service --url
+```
+
+
+## test predict
+
+```bash
+new url: http://192.168.99.100:30000/ (or other)
+```
+
+
+
+
+In Kubernetes, **`type: NodePort`** is used in a Service when you want to access your application from outside the Kubernetes cluster (like your laptop or local browser).
+
+Here’s why you might use it in simple terms:
+
+---
+
+### **1. Kubernetes Runs on Its Own Network**
+- Kubernetes creates an internal network for all the Pods.
+- By default, this network isn’t accessible from the outside (e.g., your computer).
+
+---
+
+### **2. Services Expose Pods**
+- A **Service** connects your app (running in Pods) to the outside world.
+- **`type: NodePort`** exposes your app on a specific port on every node in your cluster.
+
+---
+
+### **3. Why Use `NodePort`?**
+- When you set `type: NodePort`, Kubernetes assigns a port (like `30000-32767`) on the node's IP address.
+- You can now access your app by visiting:
+  ```
+  http://<node-ip>:<node-port>
+  ```
+  For example:
+  ```
+  http://192.168.99.100:30000
+  ```
+  Here, `192.168.99.100` is the Minikube node's IP, and `30000` is the NodePort.
+
+---
+
+### **4. Why Not Use ClusterIP?**
+- By default, Services use **`type: ClusterIP`**, which only allows access *within* the Kubernetes cluster.
+- This is useful for internal communication between apps but not for external access.
+
+---
+
+### **5. Why NodePort is Good for Minikube**
+- In Minikube, you're running Kubernetes on your local machine.
+- Using `NodePort` is a quick and simple way to test and access your app from your browser or other devices on the same network.
+
+---
+
+### **In Summary**
+- **`type: NodePort`** makes your app accessible outside Kubernetes on a specific port.
+- This is great for testing or development, especially in Minikube. 
+- Later, in production, you might use other Service types (like `LoadBalancer` or `Ingress`) for more advanced routing.

homework_9/pr5/k8s_deployment.yaml

@@ -0,0 +1,35 @@
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: streamlit-app
+  labels:
+    app: streamlit-app
+spec:
+  replicas: 2
+  selector:
+    matchLabels:
+      app: streamlit-app
+  template:
+    metadata:
+      labels:
+        app: streamlit-app
+    spec:
+      containers:
+      - name: streamlit-app
+        image: streamlit-app:latest
+        imagePullPolicy: Never
+        ports:
+        - containerPort: 8501
+---
+apiVersion: v1
+kind: Service
+metadata:
+  name: streamlit-service
+spec:
+  selector:
+    app: streamlit-app
+  ports:
+    - protocol: TCP
+      port: 8501
+      targetPort: 8501
+  type: NodePort

homework_9/pr5/main.py

@@ -0,0 +1,28 @@
+import streamlit as st
+from utils import Model
+
+model = Model(model_name='distilbert-base-uncased-finetuned-sst-2-english')
+
+# Create the Streamlit app title
+st.title('Sentiment Analysis with DistilBERT')
+
+# Create a text input for user's sentence
+user_input = st.text_area("Enter text to analyze:", "I love this app!")
+
+# Create analyze button
+analyze_button = st.button("Analyze Sentiment")
+
+# Make prediction when button is clicked
+if analyze_button and user_input:
+    # Get the prediction
+    label = model.predict(user_input)
+    score = model.predict_proba(user_input)
+
+    # Display results
+    st.write("### Results:")
+    label_color = "green" if label == "POSITIVE" else "red"
+    st.markdown(f"Label: **:{label_color}[{label}]**")
+    st.write(f"Confidence: **{score:.4f}** ({score*100:.2f}%)")
+
+    # Create a progress bar for the confidence score
+    st.progress(score)

homework_9/pr5/requirements.txt

@@ -0,0 +1,9 @@
+transformers==4.42.3
+numpy==1.26.4
+pandas==2.2.1
+torch==2.2.1
+-f https://download.pytorch.org/whl/cpu
+streamlit==1.39.0
+pytest==8.3.0
+pydantic==2.8.2
+requests==2.32.2

homework_9/pr5/utils.py

@@ -0,0 +1,26 @@
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+import torch
+
+class Model:
+    def __init__(self, model_name="distilbert-base-uncased-finetuned-sst-2-english"):
+        self.tokenizer = DistilBertTokenizer.from_pretrained(model_name)
+        self.model = DistilBertForSequenceClassification.from_pretrained(model_name)
+        self.model.eval()
+
+    def predict(self, text):
+        inputs = self.tokenizer(
+            text, return_tensors="pt", truncation=True, padding=True
+        )
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+        predicted_class_id = torch.argmax(outputs.logits, dim=1).item()
+        return self.model.config.id2label[predicted_class_id]
+
+    def predict_proba(self, text):
+        inputs = self.tokenizer(
+            text, return_tensors="pt", truncation=True, padding=True
+        )
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+        probabilities = torch.softmax(outputs.logits, dim=1)
+        return probabilities.squeeze().max().item()