move playground ui to llama-stack repo (#536)

# What does this PR do?

- Move Llama Stack Playground UI to llama-stack repo under
llama_stack/distribution
- Original PR in llama-stack-apps:
https://github.com/meta-llama/llama-stack-apps/pull/127

## Test Plan
```
cd llama-stack/llama_stack/distribution/ui
streamlit run app.py
```


## 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/app.py

@@ -0,0 +1,173 @@
+# 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 json
+
+import pandas as pd
+
+import streamlit as st
+
+from modules.api import LlamaStackEvaluation
+
+from modules.utils import process_dataset
+
+EVALUATION_API = LlamaStackEvaluation()
+
+
+def main():
+    # Add collapsible sidebar
+    with st.sidebar:
+        # Add collapse button
+        if "sidebar_state" not in st.session_state:
+            st.session_state.sidebar_state = True
+
+        if st.session_state.sidebar_state:
+            st.title("Navigation")
+            page = st.radio(
+                "Select a Page",
+                ["Application Evaluation"],
+                index=0,
+            )
+        else:
+            page = "Application Evaluation"  # Default page when sidebar is collapsed
+
+    # Main content area
+    st.title("🦙 Llama Stack Evaluations")
+
+    if page == "Application Evaluation":
+        application_evaluation_page()
+
+
+def application_evaluation_page():
+    # File uploader
+    uploaded_file = st.file_uploader("Upload Dataset", type=["csv", "xlsx", "xls"])
+
+    if uploaded_file is None:
+        st.error("No file uploaded")
+        return
+
+    # Process uploaded file
+    df = process_dataset(uploaded_file)
+    if df is None:
+        st.error("Error processing file")
+        return
+
+    # Display dataset information
+    st.success("Dataset loaded successfully!")
+
+    # Display dataframe preview
+    st.subheader("Dataset Preview")
+    st.dataframe(df)
+
+    # Select Scoring Functions to Run Evaluation On
+    st.subheader("Select Scoring Functions")
+    scoring_functions = EVALUATION_API.list_scoring_functions()
+    scoring_functions = {sf.identifier: sf for sf in scoring_functions}
+    scoring_functions_names = list(scoring_functions.keys())
+    selected_scoring_functions = st.multiselect(
+        "Choose one or more scoring functions",
+        options=scoring_functions_names,
+        help="Choose one or more scoring functions.",
+    )
+
+    available_models = EVALUATION_API.list_models()
+    available_models = [m.identifier for m in available_models]
+
+    scoring_params = {}
+    if selected_scoring_functions:
+        st.write("Selected:")
+        for scoring_fn_id in selected_scoring_functions:
+            scoring_fn = scoring_functions[scoring_fn_id]
+            st.write(f"- **{scoring_fn_id}**: {scoring_fn.description}")
+            new_params = None
+            if scoring_fn.params:
+                new_params = {}
+                for param_name, param_value in scoring_fn.params.to_dict().items():
+                    if param_name == "type":
+                        new_params[param_name] = param_value
+                        continue
+
+                    if param_name == "judge_model":
+                        value = st.selectbox(
+                            f"Select **{param_name}** for {scoring_fn_id}",
+                            options=available_models,
+                            index=0,
+                            key=f"{scoring_fn_id}_{param_name}",
+                        )
+                        new_params[param_name] = value
+                    else:
+                        value = st.text_area(
+                            f"Enter value for **{param_name}** in {scoring_fn_id} in valid JSON format",
+                            value=json.dumps(param_value, indent=2),
+                            height=80,
+                        )
+                        try:
+                            new_params[param_name] = json.loads(value)
+                        except json.JSONDecodeError:
+                            st.error(
+                                f"Invalid JSON for **{param_name}** in {scoring_fn_id}"
+                            )
+
+                st.json(new_params)
+            scoring_params[scoring_fn_id] = new_params
+
+        # Add run evaluation button & slider
+        total_rows = len(df)
+        num_rows = st.slider("Number of rows to evaluate", 1, total_rows, total_rows)
+
+        if st.button("Run Evaluation"):
+            progress_text = "Running evaluation..."
+            progress_bar = st.progress(0, text=progress_text)
+            rows = df.to_dict(orient="records")
+            if num_rows < total_rows:
+                rows = rows[:num_rows]
+
+            # Create separate containers for progress text and results
+            progress_text_container = st.empty()
+            results_container = st.empty()
+            output_res = {}
+            for i, r in enumerate(rows):
+                # Update progress
+                progress = i / len(rows)
+                progress_bar.progress(progress, text=progress_text)
+
+                # Run evaluation for current row
+                score_res = EVALUATION_API.run_scoring(
+                    r,
+                    scoring_function_ids=selected_scoring_functions,
+                    scoring_params=scoring_params,
+                )
+
+                for k in r.keys():
+                    if k not in output_res:
+                        output_res[k] = []
+                    output_res[k].append(r[k])
+
+                for fn_id in selected_scoring_functions:
+                    if fn_id not in output_res:
+                        output_res[fn_id] = []
+                    output_res[fn_id].append(score_res.results[fn_id].score_rows[0])
+
+                # Display current row results using separate containers
+                progress_text_container.write(
+                    f"Expand to see current processed result ({i+1}/{len(rows)})"
+                )
+                results_container.json(
+                    score_res.to_json(),
+                    expanded=2,
+                )
+
+            progress_bar.progress(1.0, text="Evaluation complete!")
+
+            # Display results in dataframe
+            if output_res:
+                output_df = pd.DataFrame(output_res)
+                st.subheader("Evaluation Results")
+                st.dataframe(output_df)
+
+
+if __name__ == "__main__":
+    main()