updating python example

fern/docs/pages/sdks.mdx

@@ -32,41 +32,42 @@ You can find the SDK docs and package information using this link.
 #### Python Code Example
 
 ```python
-import predictionguard as pg
-
-# Initialize a Prediction Guard client.
-client = Client(token=<your access token>)
-
-# Create some examples illustrating the kind of predictions you
-# want to make (domain/ use case specific).
-examples = [
- {
-   "input": {
-     "phrase": "I'm so excited about Prediction Guard. It's gonna be awesome!"
-   },
-   "output": {
-     "sentiment": "POS"
-   }
- },
- {
-   "input": {
-     "phrase": "AI development without Prediction Guard is bad. It's really terrible."
-   },
-   "output": {
-     "sentiment": "NEG"
-   }
- }
+import json
+import os
+
+from predictionguard import PredictionGuard
+
+# You can set you Prediction Guard API Key as an env variable,
+# or when creating the client object
+os.environ["PREDICTIONGUARD_API_KEY"]
+
+client = PredictionGuard(
+    api_key="<your Prediction Guard API Key"
+)
+
+messages = [
+    {
+        "role": "system",
+        "content": "You are a helpful chatbot that helps people learn."
+    },
+    {
+        "role": "user",
+        "content": "What is a good way to learn to code?"
+    }
 ]
 
-# Create a prediction "proxy." This proxy will save your examples, evaluate
-# SOTA models to find the best one for your use case, and expose the best model
-# at an endpoint corresponding to the proxy.
-client.create_proxy(task='sentiment', name='my-sentiment-proxy', examples=examples)
+result = client.chat.completions.create(
+    "model": "Hermes-2-Pro-Llama-3-8B",
+    "messages": messages,
+    "max_tokens": 100
+)
 
-# Now your ready to start getting reliable, future proof predictions. No fuss!
-client.predict(name='test-client-sentiment4', data={
- "phrase": "Isn't this great! I'm so happy I'm using Prediction Guard"
-})
+print(json.dumps(
+    result,
+    sort_keys=True,
+    indent=4,
+    separators=(',', ': ')
+))
 ```
 
 #### More Python Examples