app.py

# app.py
"""
Base Streamlit frontend structure for the Tensorus platform.
Does not include direct backend API call implementations.
"""

import streamlit as st
import json
import time
# import requests # No longer needed directly if API calls are removed/placeholdered
import logging
import torch
from typing import List, Dict, Any, Optional, Union, Tuple

# --- Page Configuration ---
st.set_page_config(
    page_title="Tensorus Dashboard",
    page_icon="🧱",
    layout="wide",
    initial_sidebar_state="expanded"
)

# --- Configure Logging (Optional but good practice) ---
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# --- Integrated Tensor Utilities (Kept from app.py) ---

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}.")
    if len(shape) > 1:
        for item in data: _validate_tensor_data(item, shape[1:])
    elif len(shape) == 1:
        if not all(isinstance(x, (int, float)) for x in data): raise ValueError("Innermost list elements must be numbers.")
    return True

def list_to_tensor(shape: List[int], dtype_str: str, data: Union[List[Any], int, float]) -> torch.Tensor:
    """
    Converts a Python list (potentially nested) or scalar into a PyTorch tensor
    with the specified shape and dtype.
    """
    try:
        dtype_map = {
            'float32': torch.float32, 'float': torch.float,
            'float64': torch.float64, 'double': torch.double,
            'int32': torch.int32, 'int': torch.int,
            'int64': torch.int64, 'long': torch.long,
            'bool': torch.bool
        }
        torch_dtype = dtype_map.get(dtype_str.lower())
        if torch_dtype is None: raise ValueError(f"Unsupported dtype string: {dtype_str}")

        tensor = torch.tensor(data, dtype=torch_dtype)

        if list(tensor.shape) != shape:
            logger.debug(f"Initial tensor shape {list(tensor.shape)} differs from target {shape}. Attempting reshape.")
            try:
                tensor = tensor.reshape(shape)
            except RuntimeError as reshape_err:
                raise ValueError(f"Created tensor shape {list(tensor.shape)} != requested {shape} and reshape failed: {reshape_err}") from reshape_err

        return tensor
    except (TypeError, ValueError) as e:
        logger.error(f"Error converting list to tensor: {e}. Shape: {shape}, Dtype: {dtype_str}")
        raise ValueError(f"Failed tensor conversion: {e}") from e
    except Exception as e:
        logger.exception(f"Unexpected error during list_to_tensor: {e}")
        raise ValueError(f"Unexpected tensor conversion error: {e}") from e

def tensor_to_list(tensor: torch.Tensor) -> Tuple[List[int], str, List[Any]]:
    """
    Converts a PyTorch tensor back into its shape, dtype string, and nested list representation.
    """
    if not isinstance(tensor, torch.Tensor):
        raise TypeError("Input must be a torch.Tensor")
    shape = list(tensor.shape)
    dtype_str = str(tensor.dtype).split('.')[-1]
    data = tensor.tolist()
    return shape, dtype_str, data

# --- Placeholder UI Utilities (Implementations Removed) ---

# Define the base URL (might be needed if placeholders call a dummy endpoint)
# API_BASE_URL = "https://tensorus-api.hf.space" # Keep if needed, remove if not

def get_api_status():
    """Placeholder: Checks if the backend API is reachable."""
    logger.warning("Placeholder function 'get_api_status' called. Returning dummy data.")
    # Simulate success for UI structure
    return True, {"message": "Tensorus API is reachable.", "version": "?.?.?"}
    # Or simulate failure:
    # return False, {"error": "Tensorus API connection failed."}

def get_agent_status():
    """Placeholder: Fetches status for all agents from the backend."""
    logger.warning("Placeholder function 'get_agent_status' called. Returning dummy data.")
    # Return dummy structure matching expected output
    return {
        "dummy_agent_1": {
            "name": "Dummy Agent One",
            "running": False,
            "config": {"param": "value"},
            "logs": ["Log line 1", "Log line 2"]
        },
         "dummy_agent_2": {
            "name": "Dummy Agent Two",
            "running": True,
            "config": {"interval": 10},
            "logs": ["Agent started", "Processing..."]
        }
    }
    # Or return None to simulate fetch error:
    # return None

def start_agent(agent_id: str):
    """Placeholder: Sends a request to start an agent."""
    logger.warning(f"Placeholder function 'start_agent' called for {agent_id}.")
    return {"success": True, "message": f"Placeholder: Start signal sent to {agent_id}."}

def stop_agent(agent_id: str):
    """Placeholder: Sends a request to stop an agent."""
    logger.warning(f"Placeholder function 'stop_agent' called for {agent_id}.")
    return {"success": True, "message": f"Placeholder: Stop signal sent to {agent_id}."}

def configure_agent(agent_id: str, config: dict):
    """Placeholder: Sends a request to configure an agent."""
    logger.warning(f"Placeholder function 'configure_agent' called for {agent_id} with config: {config}.")
    return {"success": True, "message": f"Placeholder: Configuration sent to {agent_id}."}

def post_nql_query(query: str):
    """Placeholder: Sends an NQL query to the backend."""
    logger.warning(f"Placeholder function 'post_nql_query' called with query: {query}")
    # Simulate a simple response
    return {
        "query": query,
        "response_text": f"Placeholder: Processed query '{query}'. Found 2 results.",
        "results": [
            {"id": "dummy_id_1", "data": "[1, 2, 3]", "metadata": {"source": "dummy"}},
            {"id": "dummy_id_2", "data": "[4, 5, 6]", "metadata": {"source": "dummy"}}
        ],
        "error": None
    }
    # Or simulate an error:
    # return {"query": query, "response_text": "Placeholder: Error processing query.", "error": "Syntax error"}


def get_datasets():
    """Placeholder: Fetches the list of available datasets."""
    logger.warning("Placeholder function 'get_datasets' called.")
    return ["dummy_dataset_A", "dummy_dataset_B", "rl_experiences_dummy"]

def get_dataset_preview(dataset_name: str, limit: int = 5):
    """Placeholder: Fetches preview data for a specific dataset."""
    logger.warning(f"Placeholder function 'get_dataset_preview' called for {dataset_name}.")
    if dataset_name.startswith("dummy"):
        return {
            "dataset": dataset_name,
            "record_count": 100, # Dummy count
            "preview": [
                {"record_id": f"rec_{i}", "shape": "[10]", "dtype": "float32", "metadata_preview": "{'source': 'dummy'}"}
                for i in range(limit)
            ]
        }
    else:
        return None # Simulate dataset not found or error

def operate_explorer(dataset: str, operation: str, index: int, params: dict):
    """Placeholder: Sends an operation request to the data explorer."""
    logger.warning(f"Placeholder function 'operate_explorer' called: ds={dataset}, op={operation}, idx={index}, params={params}")
    # Simulate a result based on operation
    result_data = f"Placeholder result for {operation} on {dataset}[{index}]"
    if operation == "info":
        result_data = {"shape": [10, 5], "dtype": "float32", "numel": 50}
    elif operation == "head":
        result_data = [[1.0, 2.0], [3.0, 4.0]] # Dummy data
    elif operation == "sum":
        result_data = 123.45 # Dummy scalar result

    return {
        "success": True,
        "metadata": {
            "operation": operation,
            "dataset": dataset,
            "index": index,
            "params": params,
            "status": "Placeholder Success"
        },
        "result_data": result_data
    }


# --- Initialize Session State ---
# (Kept identical to app.py)
if 'agent_status' not in st.session_state:
    st.session_state.agent_status = None
if 'datasets' not in st.session_state:
    st.session_state.datasets = []
if 'selected_dataset' not in st.session_state:
    st.session_state.selected_dataset = None
if 'dataset_preview' not in st.session_state:
    st.session_state.dataset_preview = None
if 'explorer_result' not in st.session_state:
    st.session_state.explorer_result = None
if 'nql_response' not in st.session_state:
    st.session_state.nql_response = None


# --- Sidebar ---
# (Kept identical to app.py, but uses placeholder functions)
with st.sidebar:
    
    app_mode = ""
    
    st.title("Tensorus")
    st.markdown("---")

    # API Status Check (uses placeholder)
    st.subheader("API Status")
    api_ok, api_info = get_api_status() # Uses placeholder function
    if api_ok:
        st.success(f"API Status: {api_info.get('message', 'OK')}")
    else:
        st.error(f"API Connection Failed: {api_info.get('error', 'Unknown error')}")
        st.warning("This is a placeholder status. Implement API connection check.")
        # st.stop() # Don't stop in base version

    # st.markdown("---")
    



# --- Main Page Content ---
# (Kept identical to app.py, but uses placeholder functions)

if app_mode == "Dashboard":
    st.title("📊 Operations Dashboard")
    st.warning("Live WebSocket dashboard view is best accessed directly via the backend's `/dashboard` HTML page or a dedicated JS frontend. This is a simplified view.")
    # You might want to remove or change this link if the backend isn't running
    # st.markdown(f"Access the basic live dashboard here: <a href='{API_BASE_URL}/dashboard' target='_blank'>Backend Dashboard</a>", unsafe_allow_html=True)
    st.info("This Streamlit view doesn't currently support live WebSocket updates.")


elif app_mode == "Agent Control":
    st.title("🤖 Agent Control Panel")

    if st.button("Refresh Agent Status"):
        st.session_state.agent_status = get_agent_status() # Uses placeholder function

    if st.session_state.agent_status:
        agents = st.session_state.agent_status
        agent_ids = list(agents.keys())

        if not agent_ids:
            st.warning("No agents reported (Placeholder).")
        else:
            selected_agent_id = st.selectbox("Select Agent", agent_ids)

            if selected_agent_id:
                agent_info = agents[selected_agent_id]
                st.subheader(f"Agent: {agent_info.get('name', selected_agent_id)}")

                col1, col2 = st.columns(2)
                with col1:
                    st.metric("Status", "Running" if agent_info.get('running') else "Stopped")
                    st.write("**Configuration:**")
                    st.json(agent_info.get('config', {}))
                with col2:
                    st.write("**Recent Logs:**")
                    st.code('\n'.join(agent_info.get('logs', [])), language='log')

                st.write("**Actions:**")
                btn_col1, btn_col2, btn_col3 = st.columns(3)
                with btn_col1:
                    if st.button("Start Agent", key=f"start_{selected_agent_id}", disabled=agent_info.get('running')):
                        result = start_agent(selected_agent_id) # Uses placeholder function
                        st.toast(result.get("message", "Request sent."))
                        st.session_state.agent_status = get_agent_status() # Refresh status (placeholder)
                        st.rerun()
                with btn_col2:
                    if st.button("Stop Agent", key=f"stop_{selected_agent_id}", disabled=not agent_info.get('running')):
                        result = stop_agent(selected_agent_id) # Uses placeholder function
                        st.toast(result.get("message", "Request sent."))
                        st.session_state.agent_status = get_agent_status() # Refresh status (placeholder)
                        st.rerun()
                # Configure button removed as configure_agent was removed
                # with btn_col3:
                #     st.write("Configure (Not Implemented in Placeholder)")


    else:
        st.info("Click 'Refresh Agent Status' to load placeholder agent information.")


elif app_mode == "NQL Chat":
    st.title("💬 Natural Language Query (NQL)")
    st.info("Ask questions about the data stored in Tensorus (e.g., 'show me tensors from rl_experiences', 'count records in sample_data').")

    user_query = st.text_input("Enter your query:", key="nql_query_input")

    if st.button("Submit Query", key="nql_submit"):
        if user_query:
            with st.spinner("Processing query..."):
                st.session_state.nql_response = post_nql_query(user_query) # Uses placeholder function
        else:
            st.warning("Please enter a query.")

    if st.session_state.nql_response:
        resp = st.session_state.nql_response
        st.markdown("---")
        st.write(f"**Query:** {resp.get('query')}")
        if resp.get("error"):
            st.error(f"Error: {resp.get('error')}")
        else:
            st.success(f"**Response:** {resp.get('response_text')}")
            if resp.get("results"):
                st.write("**Results Preview:**")
                st.json(resp.get("results"))
        # Clear response after displaying
        # st.session_state.nql_response = None # Keep it displayed until next query


elif app_mode == "Data Explorer":
    st.title("🔍 Data Explorer")

    if not st.session_state.datasets or st.button("Refresh Datasets"):
        st.session_state.datasets = get_datasets() # Uses placeholder function

    if not st.session_state.datasets:
        st.warning("No datasets found (Placeholder).")
    else:
        st.session_state.selected_dataset = st.selectbox(
            "Select Dataset",
            st.session_state.datasets,
            index=st.session_state.datasets.index(st.session_state.selected_dataset) if st.session_state.selected_dataset in st.session_state.datasets else 0
        )

        if st.session_state.selected_dataset:
            if st.button("Show Preview", key="preview_btn"):
                with st.spinner(f"Fetching preview for {st.session_state.selected_dataset}..."):
                    st.session_state.dataset_preview = get_dataset_preview(st.session_state.selected_dataset) # Uses placeholder function

            if st.session_state.dataset_preview:
                st.subheader(f"Preview: {st.session_state.dataset_preview.get('dataset')}")
                st.write(f"Total Records: {st.session_state.dataset_preview.get('record_count')}")
                # Adjust dataframe display based on placeholder structure
                preview_df_data = st.session_state.dataset_preview.get('preview', [])
                if preview_df_data:
                    st.dataframe(preview_df_data)
                else:
                    st.write("No preview data available.")
                st.markdown("---")
            elif st.session_state.selected_dataset and 'preview_btn' in st.session_state and st.session_state.preview_btn:
                 # If button was clicked but preview is None/empty
                 st.warning(f"Could not fetch preview for {st.session_state.selected_dataset} (Placeholder).")


            st.subheader("Perform Operation")
            # Use dummy record count if preview failed
            record_count = st.session_state.dataset_preview.get('record_count', 1) if st.session_state.dataset_preview else 1
            tensor_index = st.number_input("Select Tensor Index", min_value=0, max_value=max(0, record_count - 1), value=0, step=1)

            operations = ["info", "head", "slice", "sum", "mean", "reshape", "transpose"]
            selected_op = st.selectbox("Select Operation", operations)

            params = {}
            # Dynamic parameter inputs (kept identical)
            if selected_op == "head":
                params['count'] = st.number_input("Count", min_value=1, value=5, step=1)
            elif selected_op == "slice":
                params['dim'] = st.number_input("Dimension (dim)", value=0, step=1)
                params['start'] = st.number_input("Start Index", value=0, step=1)
                params['end'] = st.number_input("End Index (optional)", value=None, step=1, format="%d")
                params['step'] = st.number_input("Step (optional)", value=None, step=1, format="%d")
            elif selected_op in ["sum", "mean"]:
                dim_input = st.text_input("Dimension(s) (optional, e.g., 0 or 0,1)")
                if dim_input:
                    try: params['dim'] = [int(x.strip()) for x in dim_input.split(',')] if ',' in dim_input else int(dim_input)
                    except ValueError: st.warning("Invalid dimension format.")
                params['keepdim'] = st.checkbox("Keep Dimensions (keepdim)", value=False)
            elif selected_op == "reshape":
                shape_input = st.text_input("Target Shape (comma-separated, e.g., 2,3,5)")
                if shape_input:
                    try: params['shape'] = [int(x.strip()) for x in shape_input.split(',')]
                    except ValueError: st.warning("Invalid shape format.")
            elif selected_op == "transpose":
                params['dim0'] = st.number_input("Dimension 0", value=0, step=1)
                params['dim1'] = st.number_input("Dimension 1", value=1, step=1)

            if st.button("Run Operation", key="run_op_btn"):
                valid_request = True
                if selected_op == "reshape" and not params.get('shape'):
                    st.error("Target Shape is required for reshape.")
                    valid_request = False
                # Add other validation if needed

                if valid_request:
                    with st.spinner(f"Running {selected_op} on {st.session_state.selected_dataset}[{tensor_index}]..."):
                        st.session_state.explorer_result = operate_explorer( # Uses placeholder function
                            st.session_state.selected_dataset,
                            selected_op,
                            tensor_index,
                            params
                        )

            if st.session_state.explorer_result:
                st.markdown("---")
                st.subheader("Operation Result")
                st.write("**Metadata:**")
                st.json(st.session_state.explorer_result.get("metadata", {}))
                st.write("**Result Data:**")
                # Display result data appropriately
                result_data = st.session_state.explorer_result.get("result_data", "No data returned.")
                # Try to display as JSON, fallback to text
                try:
                    st.json(result_data)
                except Exception:
                    st.text(result_data)
                # Clear result after displaying
                # st.session_state.explorer_result = None # Keep displayed until next op