Update app.py

app.py

@@ -1,58 +1,83 @@
-# app.py
-"""
-Streamlit frontend application for the Tensorus platform.
-New UI structure with top navigation and Nexus Dashboard.
-"""
+import asyncio
+import json
 
+import pandas as pd
 import streamlit as st
-import json
-import time
-import requests  # Needed for ui_utils functions if integrated
-import logging  # Needed for ui_utils functions if integrated
-import torch  # Needed for integrated tensor utils
-import os
-from pathlib import Path
-from typing import List, Dict, Any, Optional, Union, Tuple  # Needed for integrated tensor utils
-from pages.pages_shared_utils import get_api_status, get_agent_status, get_datasets  # Updated imports
-
-# Work around a Streamlit bug where inspecting `torch.classes` during module
-# watching can raise a `RuntimeError`. Removing the module from `sys.modules`
-# prevents Streamlit's watcher from trying to access it.
-import sys
-if "torch.classes" in sys.modules:
-    del sys.modules["torch.classes"]
-
-# Ensure Streamlit uses a writable configuration directory.
-# Some container environments set $HOME to '/' which is read-only.
-# Redirect to '/tmp' and create the '.streamlit' directory if needed.
-home = Path(os.environ.get("HOME", "/tmp"))
-if home == Path("/"):
-    home = Path("/tmp")
-    os.environ["HOME"] = str(home)
-(home / ".streamlit").mkdir(parents=True, exist_ok=True)
-
-# --- Page Configuration ---
-st.set_page_config(
-    page_title="Tensorus Platform",
-    page_icon="🧊",
-    layout="wide",
-    initial_sidebar_state="collapsed" # Collapse sidebar as nav is now at top
-)
-
-# --- Configure Logging (Optional but good practice) ---
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# --- Integrated Tensor Utilities (Preserved) ---
-
-def _validate_tensor_data(data: List[Any], shape: List[int]):
-    """
-    Validates if the nested list structure of 'data' matches the 'shape'.
-    Raises ValueError on mismatch. (Optional validation)
-    """
-    if not shape:
-        if not isinstance(data, (int, float)): raise ValueError("Scalar tensor data must be a single number.")
-        return True
-    if not isinstance(data, list): raise ValueError(f"Data for shape {shape} must be a list.")
-    expected_len = shape[0]
-    if len(data) != expected_len: raise ValueError(f"Dimension 0 mismatch: Expected {expected_len}, got {len(data)} for shape {shape}.")
+from tensorus.mcp_client import TensorusMCPClient, DEFAULT_MCP_URL
+
+st.title("Tensorus MCP Client Demo")
+st.markdown("Interact with a Tensorus MCP server without writing any code.")
+
+mcp_url = st.text_input("MCP server URL", DEFAULT_MCP_URL)
+
+
+def run_async(coro):
+    return asyncio.run(coro)
+
+
+st.header("Datasets")
+if st.button("List datasets"):
+
+    async def _list():
+        async with TensorusMCPClient.from_http(url=mcp_url) as client:
+            return await client.list_datasets()
+
+    result = run_async(_list())
+    if result:
+        st.write(pd.DataFrame(result.datasets, columns=["Datasets"]))
+
+create_name = st.text_input("New dataset name")
+if st.button("Create dataset") and create_name:
+
+    async def _create():
+        async with TensorusMCPClient.from_http(url=mcp_url) as client:
+            return await client.create_dataset(create_name)
+
+    res = run_async(_create())
+    if res:
+        st.success(res.message or "Dataset created")
+
+st.header("Ingest Tensor")
+with st.form("ingest"):
+    ingest_ds = st.text_input("Dataset", key="ingest_ds")
+    tensor_shape = st.text_input("Tensor shape", value="2,2")
+    tensor_dtype = st.text_input("Tensor dtype", value="float32")
+    tensor_data = st.text_area("Tensor data (JSON)", value="[[0, 0], [1, 1]]")
+    metadata = st.text_area("Metadata (JSON)", value="{}")
+    submitted = st.form_submit_button("Ingest")
+
+if submitted:
+    try:
+        shape = [int(x) for x in tensor_shape.split(",") if x.strip()]
+        data = json.loads(tensor_data)
+        meta = json.loads(metadata) if metadata.strip() else None
+
+        async def _ingest():
+            async with TensorusMCPClient.from_http(url=mcp_url) as client:
+                return await client.ingest_tensor(
+                    dataset_name=ingest_ds,
+                    tensor_shape=shape,
+                    tensor_dtype=tensor_dtype,
+                    tensor_data=data,
+                    metadata=meta,
+                )
+
+        response = run_async(_ingest())
+        st.write(response)
+        st.success(f"Ingested tensor {response.id}")
+    except Exception as e:
+        st.error(f"Failed to ingest: {e}")
+
+st.header("Run NQL Query")
+query = st.text_input("Query", key="nql_query")
+if st.button("Execute") and query:
+
+    async def _query():
+        async with TensorusMCPClient.from_http(url=mcp_url) as client:
+            return await client.execute_nql_query(query)
+
+    result = run_async(_query())
+    if isinstance(result.results, list):
+        st.write(pd.DataFrame(result.results))
+    else:
+        st.json(result.results)