[llama stack ui] add native eval & inspect distro & playground pages (#541)

# What does this PR do?

New Pages Added: 

- (1) Inspect Distro
- (2) Evaluations: 
  - (a) native evaluations (including generation)
  - (b) application evaluations (no generation, scoring only)
- (3) Playground: 
  - (a) chat
  - (b) RAG  

## Test Plan

```
streamlit run app.py
```

#### Playground

https://github.com/user-attachments/assets/6ca617e8-32ca-49b2-9774-185020ff5204

#### Inspect

https://github.com/user-attachments/assets/01d52b2d-92af-4e3a-b623-a9b8ba22ba99


#### Evaluations (Generation + Scoring)

https://github.com/user-attachments/assets/345845c7-2a2b-4095-960a-9ae40f6a93cf

#### Evaluations (Scoring)

https://github.com/user-attachments/assets/6cc1659f-eba4-49ca-a0a5-7c243557b4f5


## Before submitting

- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
      Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.

llama_stack/distribution/ui/modules/api.py

@@ -11,7 +11,7 @@ from typing import Optional
 from llama_stack_client import LlamaStackClient
 
 
-class LlamaStackEvaluation:
+class LlamaStackApi:
     def __init__(self):
         self.client = LlamaStackClient(
             base_url=os.environ.get("LLAMA_STACK_ENDPOINT", "http://localhost:5000"),
@@ -22,14 +22,6 @@ class LlamaStackEvaluation:
             },
         )
 
-    def list_scoring_functions(self):
-        """List all available scoring functions"""
-        return self.client.scoring_functions.list()
-
-    def list_models(self):
-        """List all available judge models"""
-        return self.client.models.list()
-
     def run_scoring(
         self, row, scoring_function_ids: list[str], scoring_params: Optional[dict]
     ):
@@ -39,3 +31,6 @@ class LlamaStackEvaluation:
         return self.client.scoring.score(
             input_rows=[row], scoring_functions=scoring_params
         )
+
+
+llama_stack_api = LlamaStackApi()