chore(rename): move llama_stack.distribution to llama_stack.core (#2975)

We would like to rename the term `template` to `distribution`. To
prepare for that, this is a precursor.

cc @leseb

llama_stack/core/ui/modules/__init__.py

@@ -0,0 +1,5 @@
+# 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.

llama_stack/core/ui/modules/api.py

@@ -0,0 +1,32 @@
+# 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 os
+
+from llama_stack_client import LlamaStackClient
+
+
+class LlamaStackApi:
+    def __init__(self):
+        self.client = LlamaStackClient(
+            base_url=os.environ.get("LLAMA_STACK_ENDPOINT", "http://localhost:8321"),
+            provider_data={
+                "fireworks_api_key": os.environ.get("FIREWORKS_API_KEY", ""),
+                "together_api_key": os.environ.get("TOGETHER_API_KEY", ""),
+                "sambanova_api_key": os.environ.get("SAMBANOVA_API_KEY", ""),
+                "openai_api_key": os.environ.get("OPENAI_API_KEY", ""),
+                "tavily_search_api_key": os.environ.get("TAVILY_SEARCH_API_KEY", ""),
+            },
+        )
+
+    def run_scoring(self, row, scoring_function_ids: list[str], scoring_params: dict | None):
+        """Run scoring on a single row"""
+        if not scoring_params:
+            scoring_params = dict.fromkeys(scoring_function_ids)
+        return self.client.scoring.score(input_rows=[row], scoring_functions=scoring_params)
+
+
+llama_stack_api = LlamaStackApi()

llama_stack/core/ui/modules/utils.py

@@ -0,0 +1,42 @@
+# 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 base64
+import os
+
+import pandas as pd
+import streamlit as st
+
+
+def process_dataset(file):
+    if file is None:
+        return "No file uploaded", None
+
+    try:
+        # Determine file type and read accordingly
+        file_ext = os.path.splitext(file.name)[1].lower()
+        if file_ext == ".csv":
+            df = pd.read_csv(file)
+        elif file_ext in [".xlsx", ".xls"]:
+            df = pd.read_excel(file)
+        else:
+            return "Unsupported file format. Please upload a CSV or Excel file.", None
+
+        return df
+
+    except Exception as e:
+        st.error(f"Error processing file: {str(e)}")
+        return None
+
+
+def data_url_from_file(file) -> str:
+    file_content = file.getvalue()
+    base64_content = base64.b64encode(file_content).decode("utf-8")
+    mime_type = file.type
+
+    data_url = f"data:{mime_type};base64,{base64_content}"
+
+    return data_url