Delete debug_app.py

debug_app.py

@@ -1,187 +0,0 @@
-import json
-import os
-import traceback
-import uuid
-from datetime import datetime
-from typing import Dict
-
-import pandas as pd
-import streamlit as st
-from datasets import load_dataset
-from dotenv import load_dotenv
-
-# Only import if file exists
-try:
-    from langgraph_agent import DataAnalystAgent
-
-    AGENT_AVAILABLE = True
-except ImportError as e:
-    AGENT_AVAILABLE = False
-    IMPORT_ERROR = str(e)
-
-# Load environment variables
-load_dotenv()
-
-# Set up page config
-st.set_page_config(
-    page_title="🤖 LangGraph Data Analyst Agent (Debug)",
-    layout="wide",
-    page_icon="🤖",
-    initial_sidebar_state="expanded",
-)
-
-
-def check_environment():
-    """Check the deployment environment and dependencies."""
-    st.markdown("## 🔍 Environment Debug Info")
-
-    # Check Python version
-    import sys
-
-    st.write(f"**Python Version:** {sys.version}")
-
-    # Check if running on Hugging Face
-    is_hf_space = os.environ.get("SPACE_ID") is not None
-    st.write(f"**Running on Hugging Face Spaces:** {is_hf_space}")
-    if is_hf_space:
-        st.write(f"**Space ID:** {os.environ.get('SPACE_ID', 'Unknown')}")
-
-    # Check API key availability
-    nebius_key = os.environ.get("NEBIUS_API_KEY")
-    openai_key = os.environ.get("OPENAI_API_KEY")
-    st.write(f"**Nebius API Key Available:** {'Yes' if nebius_key else 'No'}")
-    st.write(f"**OpenAI API Key Available:** {'Yes' if openai_key else 'No'}")
-
-    if nebius_key:
-        st.write(f"**Nebius Key Length:** {len(nebius_key)} characters")
-    if openai_key:
-        st.write(f"**OpenAI Key Length:** {len(openai_key)} characters")
-
-    # Check agent import
-    st.write(
-        f"**LangGraph Agent Import:** {'✅ Success' if AGENT_AVAILABLE else '❌ Failed'}"
-    )
-    if not AGENT_AVAILABLE:
-        st.error(f"Import Error: {IMPORT_ERROR}")
-
-    # Check required packages
-    required_packages = [
-        "langchain",
-        "langchain_core",
-        "langchain_openai",
-        "langgraph",
-        "datasets",
-        "pandas",
-    ]
-
-    st.markdown("### 📦 Package Availability")
-    for package in required_packages:
-        try:
-            __import__(package)
-            st.write(f"✅ {package}")
-        except ImportError as e:
-            st.write(f"❌ {package} - {str(e)}")
-
-
-def test_simple_agent():
-    """Test basic agent functionality."""
-    if not AGENT_AVAILABLE:
-        st.error("Cannot test agent - import failed")
-        return
-
-    st.markdown("## 🧪 Agent Test")
-
-    # Get API key
-    api_key = os.environ.get("NEBIUS_API_KEY") or os.environ.get("OPENAI_API_KEY")
-    if not api_key:
-        st.error("No API key found!")
-        return
-
-    st.write("**API Key:** ✅ Available")
-
-    # Test agent creation
-    try:
-        st.write("**Creating Agent...**")
-        agent = DataAnalystAgent(api_key=api_key)
-        st.write("✅ Agent created successfully")
-
-        # Test simple query
-        if st.button("🧪 Test Simple Query"):
-            with st.spinner("Testing agent with simple query..."):
-                try:
-                    result = agent.invoke("Hello, are you working?", "debug_test")
-                    st.success("✅ Agent responded successfully!")
-
-                    st.markdown("**Response Messages:**")
-                    for i, msg in enumerate(result.get("messages", [])):
-                        st.write(
-                            f"{i+1}. {type(msg).__name__}: {getattr(msg, 'content', 'No content')[:100]}..."
-                        )
-
-                except Exception as e:
-                    st.error(f"❌ Agent test failed: {str(e)}")
-                    st.code(traceback.format_exc())
-
-    except Exception as e:
-        st.error(f"❌ Agent creation failed: {str(e)}")
-        st.code(traceback.format_exc())
-
-
-def test_dataset_loading():
-    """Test dataset loading."""
-    st.markdown("## 📊 Dataset Test")
-
-    try:
-        with st.spinner("Loading dataset..."):
-            dataset = load_dataset(
-                "bitext/Bitext-customer-support-llm-chatbot-training-dataset"
-            )
-            df = pd.DataFrame(dataset["train"])
-            st.success(f"✅ Dataset loaded: {len(df):,} records")
-            st.dataframe(df.head(3))
-    except Exception as e:
-        st.error(f"❌ Dataset loading failed: {str(e)}")
-        st.code(traceback.format_exc())
-
-
-def main():
-    st.title("🔧 LangGraph Agent Debug Tool")
-    st.markdown("This tool helps diagnose issues with the LangGraph agent deployment.")
-
-    # Environment check
-    check_environment()
-
-    st.markdown("---")
-
-    # Dataset test
-    test_dataset_loading()
-
-    st.markdown("---")
-
-    # Agent test
-    test_simple_agent()
-
-    st.markdown("---")
-
-    st.markdown("## 💡 Common Solutions")
-    st.markdown(
-        """
-    **If agent creation fails:**
-    - Check API key is correctly set as Space secret
-    - Verify all dependencies are in requirements.txt
-    - Check for import errors above
-    
-    **If agent hangs on 'thinking':**
-    - API key might be invalid/expired
-    - Network connectivity issues to API endpoint
-    - Unhandled exceptions in LangGraph workflow
-    
-    **If dataset loading fails:**
-    - Network connectivity issues
-    - Hugging Face datasets library not properly installed
-    """
-    )
-
-
-if __name__ == "__main__":
-    main()