graph_streaming.py

import asyncio
import ast
import os
import re
from typing import Annotated, Dict, List, Optional, Generator
from typing_extensions import TypedDict

from dotenv import find_dotenv, load_dotenv
from langchain.chat_models import init_chat_model
from langgraph.graph import END, START, StateGraph
from pydantic import BaseModel, Field

# Import your existing agent functionality
from agent import create_analysis_agent, FileInjectedPyodideSandbox, create_pyodide_eval_fn

load_dotenv(find_dotenv())

# Initialize the language model
model = init_chat_model(
    model="gpt-4.1-2025-04-14",
    api_key=os.getenv("OPENAI_API_KEY"),
)

# Import classes from original graph.py
from graph import FileExamination, CodeGuidance, CodeAnalysisState, validate_python_code, analyze_user_question

def streaming_analyze_file_with_guidance(file_path: str, analysis_query: str = None) -> Generator[str, None, str]:
    """
    Streaming version of guided file analysis that yields progress updates.
    
    Args:
        file_path: Path to the file to analyze
        analysis_query: Optional specific analysis request
    
    Yields:
        Progress updates as strings
        
    Returns:
        Final analysis results
    """
    try:
        yield "🔍 **Starting Guided File Analysis...**\n\n"
        yield f"📁 **File:** `{os.path.basename(file_path)}`\n"
        yield f"❓ **Question:** {analysis_query or 'Comprehensive analysis'}\n\n"
        
        # Step 1: File Examination
        yield "## 📋 Step 1: Examining File Structure\n\n"
        yield "Reading first 20 lines to understand file format and patterns...\n\n"
        
        file_examination = examine_file_structure_streaming(file_path)
        
        yield f"✅ **File Type Detected:** `{file_examination.file_type}`\n"
        yield f"✅ **Structure Pattern:** {file_examination.structure_pattern}\n"
        yield f"✅ **Data Format:** {file_examination.data_format}\n"
        yield f"✅ **Complexity:** {file_examination.complexity_level}\n"
        yield f"✅ **Key Patterns Found:** {len(file_examination.key_patterns)} patterns\n\n"
        
        # Show sample of what AI is analyzing
        if file_examination.sample_lines and len(file_examination.sample_lines) > 0:
            yield "🔍 **File Sample (First 5 lines):**\n"
            yield "```\n"
            for i, line in enumerate(file_examination.sample_lines[:5], 1):
                # Truncate very long lines
                display_line = line[:100] + "..." if len(line) > 100 else line
                yield f"{i:2d}: {display_line}\n"
            yield "```\n\n"
        
        # Step 2: Code Guidance Generation
        yield "## 🎯 Step 2: Generating Analysis Strategy\n\n"
        yield "Creating specific code guidance based on file structure and your question...\n\n"
        
        code_guidance = generate_code_guidance_streaming(file_examination, analysis_query)
        
        yield f"✅ **Analysis Approach:** {code_guidance.analysis_approach}\n"
        yield f"✅ **Required Imports:** {', '.join(code_guidance.required_imports)}\n"
        yield f"✅ **Regex Patterns:** {len(code_guidance.specific_patterns)} patterns\n"
        
        # Show the actual patterns for transparency
        if code_guidance.specific_patterns:
            yield "🔍 **Pattern Details:**\n"
            for i, pattern in enumerate(code_guidance.specific_patterns[:3], 1):  # Show first 3
                yield f"   {i}. `{pattern}`\n"
            if len(code_guidance.specific_patterns) > 3:
                yield f"   ... and {len(code_guidance.specific_patterns) - 3} more patterns\n"
        
        yield f"✅ **Expected Outputs:** {', '.join(code_guidance.expected_outputs)}\n\n"
        
        # Step 3: Code Execution
        yield "## 🚀 Step 3: Executing Analysis\n\n"
        yield "Running guided code analysis with enhanced context...\n\n"
        
        # Stream the execution results
        execution_generator = execute_guided_analysis_streaming(file_path, file_examination, code_guidance, analysis_query)
        
        execution_results = []
        for chunk in execution_generator:
            yield chunk
            execution_results.append(chunk)
        
        # Final Summary
        yield "\n\n## ✅ Analysis Complete!\n\n"
        
        final_analysis = f"""### 📊 **Analysis Summary**

**File:** `{os.path.basename(file_path)}`  
**Type:** {file_examination.file_type} ({file_examination.data_format})  
**Approach:** {code_guidance.analysis_approach}  
**Complexity:** {file_examination.complexity_level}  

**Guided Features Used:**
- ✅ Structure-aware examination
- ✅ Question-specific code generation  
- ✅ {len(code_guidance.specific_patterns)} targeted patterns
- ✅ Enhanced error handling

---

{''.join(execution_results)}
"""
        
        yield final_analysis
        return final_analysis
        
    except Exception as e:
        error_msg = f"❌ **Error in guided analysis:** {str(e)}\n\n"
        yield error_msg
        return error_msg

def examine_file_structure_streaming(file_path: str) -> FileExamination:
    """Examine file structure with minimal processing for streaming."""
    try:
        if not os.path.exists(file_path):
            return FileExamination(
                file_type="error",
                structure_pattern="File not found",
                sample_lines=[],
                key_patterns=[],
                data_format="unknown",
                complexity_level="Simple"
            )
        
        # Read first 20 lines
        with open(file_path, 'r', encoding='utf-8') as f:
            sample_lines = []
            for i, line in enumerate(f):
                if i >= 20:
                    break
                sample_lines.append(line.rstrip('\n\r'))
        
        if not sample_lines:
            sample_lines = ["<empty file>"]
        
        # Quick analysis based on file extension and content
        file_ext = os.path.splitext(file_path)[1].lower()
        first_lines_text = '\n'.join(sample_lines[:5])
        
        # Simple file type detection
        if file_ext in ['.log', '.txt']:
            if 'ERROR' in first_lines_text or 'WARN' in first_lines_text:
                file_type = "application_log"
                structure_pattern = "Log entries with timestamps and severity levels"
                key_patterns = ["timestamp", "log_level", "error_codes"]
            else:
                file_type = "text_log"
                structure_pattern = "Plain text with line-based entries"
                key_patterns = ["timestamps", "text_patterns"]
        elif file_ext == '.csv':
            file_type = "csv_data"
            structure_pattern = "Comma-separated values with headers"
            key_patterns = ["column_headers", "data_rows"]
        elif file_ext == '.json':
            file_type = "json_data"
            structure_pattern = "Structured JSON data"
            key_patterns = ["json_objects", "nested_data"]
        else:
            file_type = "generic_file"
            structure_pattern = "Unknown structure"
            key_patterns = ["general_patterns"]
        
        return FileExamination(
            file_type=file_type,
            structure_pattern=structure_pattern,
            sample_lines=sample_lines,
            key_patterns=key_patterns,
            data_format="structured" if file_ext in ['.csv', '.json'] else "unstructured",
            complexity_level="Medium"
        )
        
    except Exception as e:
        return FileExamination(
            file_type="error",
            structure_pattern=f"Error reading file: {str(e)}",
            sample_lines=[],
            key_patterns=[],
            data_format="unknown",
            complexity_level="Simple"
        )

def generate_code_guidance_streaming(file_examination: FileExamination, analysis_query: str = None) -> CodeGuidance:
    """Generate code guidance with quick processing for streaming."""
    
    if not file_examination or file_examination.file_type == "error":
        return CodeGuidance(
            analysis_approach="Basic file analysis with error handling",
            required_imports=["re", "os"],
            code_structure="1. Check file exists\n2. Basic error handling\n3. Simple output",
            specific_patterns=[],
            expected_outputs=["Error message"],
            error_handling="Try-catch with informative errors"
        )
    
    # Quick guidance based on file type
    if "log" in file_examination.file_type:
        approach = "Log file analysis with pattern matching"
        imports = ["re", "datetime", "collections"]
        patterns = [r'\d{4}-\d{2}-\d{2}', r'ERROR|WARN|INFO', r'\d+\.\d+\.\d+\.\d+']
        outputs = ["Error counts", "Timeline analysis", "IP addresses"]
    elif "csv" in file_examination.file_type:
        approach = "CSV data analysis with statistical insights"
        imports = ["pandas", "numpy", "re"]
        patterns = [r'^\w+,', r'\d+', r'\w+@\w+']
        outputs = ["Data summary", "Column analysis", "Statistics"]
    elif "json" in file_examination.file_type:
        approach = "JSON structure analysis and data extraction"
        imports = ["json", "re", "collections"]
        patterns = [r'"[\w]+":', r'\{.*\}', r'\[.*\]']
        outputs = ["Structure overview", "Key extraction", "Value analysis"]
    else:
        approach = "General text analysis with pattern detection"
        imports = ["re", "collections", "os"]
        patterns = [r'\w+', r'\d+', r'[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}']
        outputs = ["Pattern summary", "Content analysis", "Statistics"]
    
    return CodeGuidance(
        analysis_approach=approach,
        required_imports=imports,
        code_structure=f"1. Load and validate file\n2. Apply {len(patterns)} specific patterns\n3. Generate insights\n4. Format results",
        specific_patterns=patterns,
        expected_outputs=outputs,
        error_handling="Comprehensive error handling with informative messages"
    )

def execute_guided_analysis_streaming(file_path: str, file_examination: FileExamination, 
                                    code_guidance: CodeGuidance, analysis_query: str = None) -> Generator[str, None, None]:
    """Execute the analysis with streaming progress updates."""
    
    try:
        yield "### 🔄 **Initializing Analysis Environment**\n\n"
        
        # Create analysis agent
        try:
            model = init_chat_model("gpt-4.1-2025-04-14", model_provider="openai")
            agent = create_analysis_agent(file_path, model)
            yield "✅ Analysis agent initialized successfully\n\n"
        except Exception as e:
            yield f"❌ Failed to initialize agent: {str(e)}\n\n"
            return
        
        yield "### 📝 **Generating Analysis Code**\n\n"
        
        # Create analysis prompt
        user_analysis = analyze_user_question(analysis_query or "Comprehensive analysis")
        
        analysis_prompt = f"""
Analyze the uploaded file based on this guidance:

**File Information:**
- Type: {file_examination.file_type}
- Structure: {file_examination.structure_pattern}  
- Format: {file_examination.data_format}
- Complexity: {file_examination.complexity_level}

**User Question Analysis:**
- Intent: {user_analysis['intent']}
- Focus Areas: {user_analysis['focus_areas']}
- Analysis Type: {user_analysis['analysis_type']}

**Generated Guidance:**
- Approach: {code_guidance.analysis_approach}
- Required Imports: {code_guidance.required_imports}
- Patterns to Use: {code_guidance.specific_patterns}
- Expected Outputs: {code_guidance.expected_outputs}

**User's Specific Question:** {analysis_query or 'Provide comprehensive analysis'}

Please write Python code that follows this guidance and analyzes the file. The file is available at the virtual path '/uploaded_file.log'.
"""
        
        yield "✅ Analysis prompt prepared\n\n"
        yield "### ⚡ **Running AI Analysis**\n\n"
        
        # Execute analysis - let's go back to a simpler approach that works
        try:
            async def run_simple_analysis():
                # Try the original invoke method first to make sure it works
                result = await agent.ainvoke({"messages": [{"role": "user", "content": analysis_prompt}]})
                return result
            
            yield "🤖 AI model is analyzing your file...\n\n"
            result = asyncio.run(run_simple_analysis())
            
            # Debug: Show what we got
            yield "### 🔍 **AI Analysis Debug Info**\n\n"
            yield f"**Result Type:** {type(result)}\n"
            yield f"**Result Keys:** {list(result.keys()) if isinstance(result, dict) else 'Not a dict'}\n\n"
            
            # Display the thought process and generated code
            yield "### 🔍 **AI Thought Process & Generated Code**\n\n"
            
            yield f"💭 **AI Analysis Strategy:**\n"
            yield f"- File Type: {file_examination.file_type}\n"
            yield f"- Approach: {code_guidance.analysis_approach}\n"
            yield f"- Patterns: {len(code_guidance.specific_patterns)} regex patterns\n"
            yield f"- Question Focus: {user_analysis['analysis_type']}\n\n"
            
            # Extract content from result and find code blocks in ALL messages
            all_content = ""
            code_blocks = []
            final_result = ""
            
            if result and isinstance(result, dict) and "messages" in result:
                messages = result["messages"]
                yield f"**Messages Count:** {len(messages)}\n\n"
                
                import re
                
                # Check each message for code blocks and content
                for i, msg in enumerate(messages):
                    if hasattr(msg, 'content'):
                        msg_content = msg.content
                        msg_type = type(msg).__name__
                        
                        yield f"**Message {i+1} Type:** {msg_type}\n"
                        yield f"**Message {i+1} Content Preview:** {msg_content[:100]}...\n\n"
                        
                        # Look for code blocks in this specific message
                        if msg_type == "AIMessage":  # Code is usually in AI messages
                            # Try multiple patterns
                            python_blocks = re.findall(r'```python\n(.*?)\n```', msg_content, re.DOTALL)
                            generic_blocks = re.findall(r'```\n(.*?)\n```', msg_content, re.DOTALL)
                            
                            if python_blocks:
                                code_blocks.extend(python_blocks)
                                yield f"   🔧 **Found {len(python_blocks)} Python code blocks in this message!**\n\n"
                            elif generic_blocks:
                                code_blocks.extend(generic_blocks)
                                yield f"   🔧 **Found {len(generic_blocks)} generic code blocks in this message!**\n\n"
                        
                        all_content += msg_content + "\n"
                
                # Get final result from last AI message
                for msg in reversed(messages):
                    if hasattr(msg, 'content') and type(msg).__name__ == "AIMessage":
                        final_result = msg.content
                        break
            else:
                yield "❌ Unexpected result format\n\n"
                all_content = str(result)
                final_result = str(result)
            
            if code_blocks:
                yield "🔧 **Generated Python Code:**\n\n"
                for i, code_block in enumerate(code_blocks, 1):
                    clean_code = code_block.strip()
                    yield f"**Code Block {i}:**\n"
                    yield f"```python\n{clean_code}\n```\n\n"
                yield "⚡ **Code Execution Results:**\n\n"
            else:
                yield "🔍 **No code blocks found in any message**\n\n"
                yield f"**All Content Preview:**\n```\n{all_content[:500]}...\n```\n\n"
            
            # Show final results
            yield "### 📊 **Analysis Results**\n\n"
            yield final_result
            yield "\n\n"
                
        except Exception as e:
            yield f"❌ Error during analysis execution: {str(e)}\n\n"
            
    except Exception as e:
        yield f"❌ Error in analysis setup: {str(e)}\n\n"