Level 2: Integrated real Sentiment Analysis model

Dockerfile

@@ -1,15 +1,15 @@
-# Step 1: Use a lightweight Python base image
 FROM python:3.9-slim
 
-# Step 2: Set the working directory inside the container
 WORKDIR /app
 
-# Step 3: Copy your requirements and install them
+# Install dependencies first (to cache this layer)
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 
-# Step 4: Copy the rest of your application code
+# Pre-download the model so it's baked into the image
+# This prevents the container from downloading it at runtime
+RUN python -c "from transformers import pipeline; pipeline('sentiment-analysis', model='distilbert-base-uncased-finetuned-sst-2-english')"
+
 COPY . .
 
-# Step 5: Command to run the API using Uvicorn
 CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

main.py

@@ -1,14 +1,22 @@
 from fastapi import FastAPI
+from transformers import pipeline
 
 app = FastAPI()
 
+# Load the model once when the server starts
+# This is "Sentiment Analysis" by default
+classifier = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+
 @app.get("/")
 def home():
-    return {"status": "Mastered Level 1", "engine": "FastAPI", "mode": "Docker"}
+    return {"status": "Level 2 Live", "model": "DistilBERT Sentiment"}
 
 @app.post("/predict")
 def predict(data: dict):
-    # This is where your model logic would go later
-    features = data.get("features", [])
-    prediction = sum(features) # Dummy logic: summing numbers
-    return {"prediction": prediction}
+    text = data.get("text", "")
+    if not text:
+        return {"error": "No text provided"}
+    
+    # Run the model
+    result = classifier(text)
+    return {"input": text, "result": result[0]}

requirements.txt

@@ -1,2 +1,4 @@
 fastapi
-uvicorn
+uvicorn
+transformers
+torch