[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/page/playground/chat.py

@@ -0,0 +1,123 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import streamlit as st
+from modules.api import llama_stack_api
+
+# Sidebar configurations
+with st.sidebar:
+    st.header("Configuration")
+    available_models = llama_stack_api.client.models.list()
+    available_models = [model.identifier for model in available_models]
+    selected_model = st.selectbox(
+        "Choose a model",
+        available_models,
+        index=0,
+    )
+
+    temperature = st.slider(
+        "Temperature",
+        min_value=0.0,
+        max_value=1.0,
+        value=0.0,
+        step=0.1,
+        help="Controls the randomness of the response. Higher values make the output more creative and unexpected, lower values make it more conservative and predictable",
+    )
+
+    top_p = st.slider(
+        "Top P",
+        min_value=0.0,
+        max_value=1.0,
+        value=0.95,
+        step=0.1,
+    )
+
+    max_tokens = st.slider(
+        "Max Tokens",
+        min_value=0,
+        max_value=4096,
+        value=512,
+        step=1,
+        help="The maximum number of tokens to generate",
+    )
+
+    repetition_penalty = st.slider(
+        "Repetition Penalty",
+        min_value=1.0,
+        max_value=2.0,
+        value=1.0,
+        step=0.1,
+        help="Controls the likelihood for generating the same word or phrase multiple times in the same sentence or paragraph. 1 implies no penalty, 2 will strongly discourage model to repeat words or phrases.",
+    )
+
+    stream = st.checkbox("Stream", value=True)
+    system_prompt = st.text_area(
+        "System Prompt",
+        value="You are a helpful AI assistant.",
+        help="Initial instructions given to the AI to set its behavior and context",
+    )
+
+    # Add clear chat button to sidebar
+    if st.button("Clear Chat", use_container_width=True):
+        st.session_state.messages = []
+        st.rerun()
+
+
+# Main chat interface
+st.title("🦙 Chat")
+
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display chat messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Chat input
+if prompt := st.chat_input("Example: What is Llama Stack?"):
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    # Display user message
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # Display assistant response
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+        full_response = ""
+
+        response = llama_stack_api.client.inference.chat_completion(
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": prompt},
+            ],
+            model_id=selected_model,
+            stream=stream,
+            sampling_params={
+                "temperature": temperature,
+                "top_p": top_p,
+                "max_tokens": max_tokens,
+                "repetition_penalty": repetition_penalty,
+            },
+        )
+
+        if stream:
+            for chunk in response:
+                if chunk.event.event_type == "progress":
+                    full_response += chunk.event.delta
+                message_placeholder.markdown(full_response + "▌")
+            message_placeholder.markdown(full_response)
+        else:
+            full_response = response
+            message_placeholder.markdown(full_response.completion_message.content)
+
+        st.session_state.messages.append(
+            {"role": "assistant", "content": full_response}
+        )