feat: deploy with docker completed

deployment/Dockerfile.fastapi

@@ -0,0 +1,22 @@
+# Dockerfile for FastAPI
+FROM python:3.11-slim
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1
+ENV PYTHONPATH=/app
+
+# Set working directory
+WORKDIR /app
+
+# Copy the requirements and install dependencies
+COPY deployment/requirements/fastapi-requirements.txt .
+RUN pip install --no-cache-dir -r fastapi-requirements.txt
+
+# Copy code only for production (comment this out if using only volume mounting)
+# COPY . .
+
+# Expose the port FastAPI will run on
+EXPOSE 8000
+
+# Command to run FastAPI
+CMD ["uvicorn", "deployment.api:app", "--host", "0.0.0.0", "--port", "8000", "--reload"]

deployment/Dockerfile.streamlit

@@ -0,0 +1,22 @@
+# Dockerfile for Streamlit
+FROM python:3.11-slim
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1
+ENV PYTHONPATH=/app
+
+# Set working directory
+WORKDIR /app
+
+# Copy the requirements and install dependencies
+COPY deployment/requirements/streamlit-requirements.txt .
+RUN pip install --no-cache-dir -r streamlit-requirements.txt
+
+# Copy application code 
+COPY ./deployment/app.py /deployment/app.py
+
+# Expose the port Streamlit will run on
+EXPOSE 8501
+
+# Command to run Streamlit
+CMD ["streamlit", "run", "deployment/app.py", "--server.port=8501", "--server.address=0.0.0.0"]

deployment/api/api.py → deployment/api.py

@@ -41,7 +41,7 @@ async def lifespan(app: FastAPI):
     import torch
     from models.ssam.simple_sentiment_analysis_model import SentimentAnalysisModel
     model = SentimentAnalysisModel()
-    model.load_state_dict(torch.load('models/ssam/model_weights.pth'))
+    model.load_state_dict(torch.load('models/ssam/model_weights.pth', map_location=config.device))
     logging.info(f"Model {config.model_name} loaded successfully at startup.")
 
     tokenizer = data.get_tokenizer(config.tokenizer_name)

deployment/app/app.py → deployment/app.py

@@ -8,7 +8,7 @@
 tweet = st.text_area("Enter the tweet you want to analyze:", "", height=150)
 
 # API endpoint URL (adjust this URL based on where your FastAPI is hosted)
-api_url = "http://127.0.0.1:8000/predict-sentiment/"
+api_url = "http://fastapi:8000/predict-sentiment/"
 
 # Create a button to send the request
 if st.button("Predict Sentiment"):

deployment/docker-compose.yml

@@ -0,0 +1,33 @@
+version: '3.8'
+
+services:
+  fastapi:
+    build:
+      context: ..
+      dockerfile: deployment/Dockerfile.fastapi
+    environment:
+      - PYTHONPATH=/app  # Set project root as PYTHONPATH
+    volumes:
+      - ../:/app  # Mount the project root directory
+    ports:
+      - "8000:8000"
+    networks:
+      - app-network
+
+  streamlit:
+    build:
+      context: ..
+      dockerfile: deployment/Dockerfile.streamlit
+    environment:
+      - PYTHONPATH=/app  # Set project root as PYTHONPATH
+      - FASTAPI_URL=http://fastapi:8000  # Connects Streamlit to FastAPI
+    volumes:
+      - ../:/app  # Mount the project root directory
+    ports:
+      - "8501:8501"
+    networks:
+      - app-network
+
+networks:
+  app-network:
+    driver: bridge

deployment/requirements/fastapi-requirements.txt

@@ -0,0 +1,10 @@
+datasets==3.0.0
+mlflow==2.16.2
+mlflow_skinny==2.16.2
+pandas==2.2.3
+scikit_learn==1.5.2
+torch==2.4.1
+transformers==4.44.2
+pydantic==1.10.2
+fastapi
+uvicorn

deployment/requirements/streamlit-requirements.txt

@@ -0,0 +1,2 @@
+requests
+streamlit