The syntax error you're encountering, `SyntaxError: unterminated string literal`, means there's an issue with how a string is defined in your Python code. The Python interpreter detected a string that was started but never properly closed.

This is happening because the AI, in a previous response, included a comment block that contained a line like `Here are the queries I'll use:`. This line was not a valid Python comment or string and was incorrectly inserted at the top of the Python file, causing the syntax error.

To fix this, you need to remove the invalid text from the top of your `app.py` file and ensure the code starts with the standard Python shebang and docstring. The file should begin with the line `#!/usr/bin/env python3` and the code block provided in the previous, corrected response.

-----

### Corrected Code

Below is the complete, correct Python code. Simply copy and paste this entire block into your `app.py` file, replacing all its current contents. This will remove the syntax error and ensure the application runs as intended.

````python
#!/usr/bin/env python3
"""
Code Flow Analyzer with Gradio Interface - Hugging Face Spaces & Colab Compatible
A single-file application that uses LangChain agents with the Gemini model to analyze code structure
and generate Mermaid.js flowchart diagrams through a web interface.
"""

import ast
import re
import os
import traceback
import sys
from typing import Dict, Any, List, Tuple
import getpass

# Check if running in Colab
try:
    import google.colab
    IN_COLAB = True
    print("🟢 Running in Google Colab")
except ImportError:
    IN_COLAB = False
    print("🟡 Running locally or in Hugging Face Spaces")

# Install dependencies if in Colab
if IN_COLAB:
    print("📦 Installing dependencies...")
    os.system("pip install -q gradio langchain langgraph langchain-google-genai")
    print("✅ Dependencies installed")

import gradio as gr
from langchain.chat_models import init_chat_model
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.tools import tool
from langgraph.prebuilt import create_react_agent
from langgraph.checkpoint.memory import MemorySaver

# Sample code examples (unchanged)
SAMPLE_PYTHON = '''def main():
    user_input = get_user_input()
    if user_input:
        result = process_data(user_input)
        if result > 0:
            display_result(result)
        else:
            show_error()
    else:
        show_help()

def get_user_input():
    return input("Enter data: ")

def process_data(data):
    for i in range(len(data)):
        if data[i].isdigit():
            return int(data[i])
    return -1

def display_result(result):
    print(f"Result: {result}")

def show_error():
    print("Error processing data")

def show_help():
    print("Please provide valid input")'''

SAMPLE_JAVASCRIPT = '''function calculateTotal(items) {
    let total = 0;
    for (let item of items) {
        if (item.price > 0) {
            total += item.price;
        }
    }
    return total;
}

function processOrder(order) {
    if (validateOrder(order)) {
        const total = calculateTotal(order.items);
        if (total > 100) {
            applyDiscount(order);
        }
        return generateReceipt(order);
    } else {
        throw new Error("Invalid order");
    }
}

function validateOrder(order) {
    return order && order.items && order.items.length > 0;
}

function applyDiscount(order) {
    order.discount = 0.1; // 10% discount
}

function generateReceipt(order) {
    return {
        items: order.items,
        total: calculateTotal(order.items),
        timestamp: new Date()
    };
}'''

SAMPLE_JAVA = '''public class Calculator {
    public static void main(String[] args) {
        Calculator calc = new Calculator();
        int result = calc.performCalculation();
        calc.displayResult(result);
    }

    public int performCalculation() {
        int a = getUserInput();
        int b = getUserInput();

        if (a > b) {
            return multiply(a, b);
        } else {
            return add(a, b);
        }
    }

    private int add(int x, int y) {
        return x + y;
    }

    private int multiply(int x, int y) {
        return x * y;
    }

    private int getUserInput() {
        return 5; // Simplified for demo
    }

    private void displayResult(int result) {
        System.out.println("Result: " + result);
    }
}'''

# --- Gemini API Key Setup ---
def setup_api_key():
    """Setup API key for Colab, Hugging Face Spaces, and local environments"""
    api_key = os.getenv("GOOGLE_API_KEY")

    if not api_key:
        if IN_COLAB:
            print("🔑 Please enter your Google API key:")
            print("   Get a key from: https://aistudio.google.com/app/apikey")
            api_key = getpass.getpass("GOOGLE_API_KEY: ")
            if api_key:
                os.environ["GOOGLE_API_KEY"] = api_key
                print("✅ API key set successfully")
            else:
                print("⚠️ No API key provided - agent features will be disabled")
        else:
            print("⚠️ GOOGLE_API_KEY not found in environment variables")
            print("   Set it with: export GOOGLE_API_KEY='your-key-here'")
            print("   In Hugging Face Spaces, use the 'Secrets' tab to set the key.")
    else:
        print("✅ Google API key found")

    return api_key or os.getenv("GOOGLE_API_KEY")

# Setup API key
api_key = setup_api_key()

# Initialize LangChain components
model = None
memory = None
agent_executor = None

if api_key:
    try:
        # Use a stable, updated model name to avoid the 404 error
        model = ChatGoogleGenerativeAI(model="gemini-2.0-flash-001", temperature=0)
        print("✅ Gemini model initialized successfully: gemini-2.0-flash-001")
        memory = MemorySaver()
    except Exception as e:
        print(f"❌ Could not initialize Gemini model: {e}")
        print("   Please check your API key and internet connection.")
        model = None
        memory = None

# --- Tool Definitions (unchanged) ---
@tool
def analyze_code_structure(source_code: str) -> Dict[str, Any]:
    """
    Analyzes source code structure to identify functions, control flow, and dependencies.
    Returns structured data about the code that can be used to generate flow diagrams.
    """
    try:
        # Try to parse as Python first
        try:
            tree = ast.parse(source_code)
            return _analyze_python_ast(tree)
        except SyntaxError:
            # If Python parsing fails, do basic text analysis
            return _analyze_code_text(source_code)

    except Exception as e:
        return {"error": f"Analysis error: {str(e)}"}

def _analyze_python_ast(tree) -> Dict[str, Any]:
    """Analyze Python AST"""
    analysis = {
        "functions": [],
        "classes": [],
        "control_flows": [],
        "imports": [],
        "call_graph": {}
    }

    class CodeAnalyzer(ast.NodeVisitor):
        def __init__(self):
            self.current_function = None

        def visit_FunctionDef(self, node):
            func_info = {
                "name": node.name,
                "line": node.lineno,
                "args": [arg.arg for arg in node.args.args],
                "calls": [],
                "conditions": [],
                "loops": []
            }

            self.current_function = node.name
            analysis["call_graph"][node.name] = []

            # Analyze function body
            for child in ast.walk(node):
                if isinstance(child, ast.Call):
                    if hasattr(child.func, 'id'):
                        func_info["calls"].append(child.func.id)
                        analysis["call_graph"][node.name].append(child.func.id)
                    elif hasattr(child.func, 'attr'):
                        func_info["calls"].append(child.func.attr)
                elif isinstance(child, ast.If):
                    func_info["conditions"].append(f"if condition at line {child.lineno}")
                elif isinstance(child, (ast.For, ast.While)):
                    loop_type = "for" if isinstance(child, ast.For) else "while"
                    func_info["loops"].append(f"{loop_type} loop at line {child.lineno}")

            analysis["functions"].append(func_info)
            self.generic_visit(node)

        def visit_ClassDef(self, node):
            class_info = {
                "name": node.name,
                "line": node.lineno,
                "methods": []
            }

            for item in node.body:
                if isinstance(item, ast.FunctionDef):
                    class_info["methods"].append(item.name)

            analysis["classes"].append(class_info)
            self.generic_visit(node)

        def visit_Import(self, node):
            for alias in node.names:
                analysis["imports"].append(alias.name)
            self.generic_visit(node)

        def visit_ImportFrom(self, node):
            module = node.module or ""
            for alias in node.names:
                analysis["imports"].append(f"{module}.{alias.name}")
            self.generic_visit(node)

    analyzer = CodeAnalyzer()
    analyzer.visit(tree)
    return analysis

def _analyze_code_text(source_code: str) -> Dict[str, Any]:
    """Basic text-based code analysis for non-Python code"""
    lines = source_code.split('\n')
    analysis = {
        "functions": [],
        "classes": [],
        "control_flows": [],
        "imports": [],
        "call_graph": {}
    }

    for i, line in enumerate(lines, 1):
        line = line.strip()

        # JavaScript function detection
        js_func_match = re.match(r'function\s+(\w+)\s*\(', line)
        if js_func_match:
            func_name = js_func_match.group(1)
            analysis["functions"].append({
                "name": func_name,
                "line": i,
                "args": [],
                "calls": [],
                "conditions": [],
                "loops": []
            })
            analysis["call_graph"][func_name] = []

        # Java/C++ method detection
        java_method_match = re.match(r'(?:public|private|protected)?\s*(?:static)?\s*\w+\s+(\w+)\s*\(', line)
        if java_method_match and not js_func_match:
            func_name = java_method_match.group(1)
            if func_name not in ['class', 'if', 'for', 'while']:  # Avoid keywords
                analysis["functions"].append({
                    "name": func_name,
                    "line": i,
                    "args": [],
                    "calls": [],
                    "conditions": [],
                    "loops": []
                })
                analysis["call_graph"][func_name] = []

        # Control structures
        if re.match(r'\s*(if|else|elif|switch)\s*[\(\{]', line):
            analysis["control_flows"].append(f"condition at line {i}")

        if re.match(r'\s*(for|while|do)\s*[\(\{]', line):
            analysis["control_flows"].append(f"loop at line {i}")

    return analysis

@tool
def generate_mermaid_diagram(analysis_data: Dict[str, Any]) -> str:
    """
    Generates a Mermaid.js flowchart diagram from code analysis data.
    Creates a visual representation of the code flow including function calls and control structures.
    """
    if "error" in analysis_data:
        return f"flowchart TD\n    Error[❌ {analysis_data['error']}]"

    functions = analysis_data.get("functions", [])
    call_graph = analysis_data.get("call_graph", {})

    if not functions:
        return """flowchart TD
    Start([🚀 Program Start]) --> NoFunc[No Functions Found]
    NoFunc --> End([🏁 Program End])

    classDef startEnd fill:#e1f5fe,stroke:#01579b,stroke-width:2px
    classDef warning fill:#fff3e0,stroke:#e65100,stroke-width:2px

    class Start,End startEnd
    class NoFunc warning"""

    mermaid_lines = ["flowchart TD"]
    mermaid_lines.append("    Start([🚀 Program Start]) --> Main")

    # Create nodes for each function
    func_nodes = []
    for i, func in enumerate(functions):
        func_name = func["name"]
        safe_name = re.sub(r'[^a-zA-Z0-9_]', '_', func_name)
        node_id = f"F{i}_{safe_name}"
        func_nodes.append(node_id)

        # Function node with emoji
        mermaid_lines.append(f"    {node_id}[⚙️ {func_name}()]")

        # Add control structures within function
        conditions = func.get("conditions", [])
        loops = func.get("loops", [])

        if conditions:
            for j, condition in enumerate(conditions[:2]):  # Limit to 2 conditions per function
                cond_id = f"{node_id}_C{j}"
                mermaid_lines.append(f"    {node_id} --> {cond_id}{{🤔 Decision}}")
                mermaid_lines.append(f"    {cond_id} -->|Yes| {node_id}_Y{j}[✅ True Path]")
                mermaid_lines.append(f"    {cond_id} -->|No| {node_id}_N{j}[❌ False Path]")

        if loops:
            for j, loop in enumerate(loops[:1]):  # Limit to 1 loop per function
                loop_id = f"{node_id}_L{j}"
                loop_type = "🔄 Loop" if "for" in loop else "⏰ While Loop"
                mermaid_lines.append(f"    {node_id} --> {loop_id}[{loop_type}]")
                mermaid_lines.append(f"    {loop_id} --> {loop_id}")  # Self-loop

    # Connect main flow
    if func_nodes:
        mermaid_lines.append(f"    Main --> {func_nodes[0]}")

        # Connect functions in sequence (simplified)
        for i in range(len(func_nodes) - 1):
            mermaid_lines.append(f"    {func_nodes[i]} --> {func_nodes[i + 1]}")

        # Connect to end
        mermaid_lines.append(f"    {func_nodes[-1]} --> End([🏁 Program End])")

    # Add function call relationships (simplified to avoid clutter)
    call_count = 0
    for caller, callees in call_graph.items():
        if call_count >= 3:  # Limit number of call relationships
            break
        caller_node = None
        for node in func_nodes:
            if caller.lower() in node.lower():
                caller_node = node
                break

        if caller_node:
            for callee in callees[:2]:  # Limit callees per function
                callee_node = None
                for node in func_nodes:
                    if callee.lower() in node.lower():
                        callee_node = node
                        break
                if callee_node and callee_node != caller_node:
                    mermaid_lines.append(f"    {caller_node} -.->|calls| {callee_node}")
                    call_count += 1

    # Add styling
    mermaid_lines.extend([
        "",
        "    classDef startEnd fill:#e1f5fe,stroke:#01579b,stroke-width:3px,color:#000",
        "    classDef process fill:#f3e5f5,stroke:#4a148c,stroke-width:2px,color:#000",
        "    classDef decision fill:#fff3e0,stroke:#e65100,stroke-width:2px,color:#000",
        "    classDef success fill:#e8f5e8,stroke:#2e7d32,stroke-width:2px,color:#000",
        "    classDef error fill:#ffebee,stroke:#c62828,stroke-width:2px,color:#000",
        "",
        "    class Start,End startEnd",
        f"    class {','.join(func_nodes)} process" if func_nodes else ""
    ])

    return "\n".join(mermaid_lines)

@tool
def calculate_complexity_score(analysis_data: Dict[str, Any]) -> int:
    """
    Calculates a complexity score for the code based on various metrics.
    Higher scores indicate more complex code structure.
    """
    if "error" in analysis_data:
        return 0

    score = 0
    functions = analysis_data.get("functions", [])

    # Base score for number of functions
    score += len(functions) * 3

    # Add score for control structures
    for func in functions:
        score += len(func.get("conditions", [])) * 4  # Conditions add complexity
        score += len(func.get("loops", [])) * 3       # Loops add complexity
        score += len(func.get("calls", [])) * 1       # Function calls add some complexity
        score += len(func.get("args", [])) * 1        # Parameters add complexity

    # Add score for classes
    score += len(analysis_data.get("classes", [])) * 5

    return min(score, 100)  # Cap at 100

# Create the agent if model is available
if model and memory:
    tools = [analyze_code_structure, generate_mermaid_diagram, calculate_complexity_score]
    agent_executor = create_react_agent(model, tools, checkpointer=memory)
    print("✅ LangChain agent created successfully")
else:
    agent_executor = None
    print("❌ LangChain agent not available")

def analyze_code_with_agent(source_code: str, language: str = "auto") -> Tuple[str, str, List[str], int, str]:
    """
    Main function that uses the LangChain agent to analyze code and generate diagrams.
    Returns: (mermaid_diagram, analysis_summary, functions_found, complexity_score, error_message)
    """
    if not source_code.strip():
        return "", "No code provided", [], 0, "Please enter some source code to analyze"

    if not agent_executor:
        return "", "Agent not available", [], 0, "❌ LangChain agent not initialized. Please check your GOOGLE_API_KEY"

    try:
        # Detect language if auto
        if language == "auto":
            if "def " in source_code or "import " in source_code:
                language = "Python"
            elif "function " in source_code or "const " in source_code or "let " in source_code:
                language = "JavaScript"
            elif ("public " in source_code and "class " in source_code) or "System.out" in source_code:
                language = "Java"
            elif "#include" in source_code or "std::" in source_code:
                language = "C++"
            else:
                language = "Unknown"

        config = {
            "configurable": {"thread_id": f"session_{hash(source_code) % 10000}"},
            "recursion_limit": 100
        }

        # Refined prompt for better tool use
        prompt = f"""
        You are a code analysis expert. Analyze the following {language} source code.
        Your task is to:
        1. Use the 'analyze_code_structure' tool with the full source code provided below.
        2. Use the 'generate_mermaid_diagram' tool with the output of the first tool.
        3. Use the 'calculate_complexity_score' tool with the output of the first tool.
        4. Provide a brief, human-readable summary of the analysis, including the generated Mermaid diagram, complexity score, and a list of functions found.
        5. Present the final result in a clear, easy-to-read format.
        
        Source Code to Analyze:
        ```{language.lower()}
        {source_code}
        ```
        """

        result = agent_executor.invoke(
            {"messages": [{"role": "user", "content": prompt}]},
            config
        )

        if result and "messages" in result:
            response_content = result["messages"][-1].content
            
            # Extract Mermaid diagram
            mermaid_match = re.search(r'```mermaid\n(.*?)\n```', response_content, re.DOTALL)
            mermaid_diagram = mermaid_match.group(1) if mermaid_match else ""

            # Extract complexity score
            complexity_match = re.search(r'complexity.*?(\d+)', response_content, re.IGNORECASE)
            complexity_score = int(complexity_match.group(1)) if complexity_match else 0

            # Extract functions
            functions_found = []
            func_matches = re.findall(r'Functions found:.*?([^\n]+)', response_content, re.IGNORECASE)
            if func_matches:
                functions_found = [f.strip() for f in func_matches[0].split(',')]
            else:
                # Fallback: extract from analysis
                analysis_result = analyze_code_structure.invoke({"source_code": source_code})
                functions_found = [f["name"] for f in analysis_result.get("functions", [])]

            # Clean up the response for summary
            summary = re.sub(r'```mermaid.*?```', '', response_content, flags=re.DOTALL)
            summary = re.sub(r'flowchart TD.*?(?=\n\n|\Z)', '', summary, flags=re.DOTALL)
            summary = summary.strip()

            if not mermaid_diagram and not summary:
                # Last resort fallback if agent fails entirely
                analysis_result = analyze_code_structure.invoke({"source_code": source_code})
                mermaid_diagram = generate_mermaid_diagram.invoke({"analysis_data": analysis_result})
                complexity_score = calculate_complexity_score.invoke({"analysis_data": analysis_result})
                functions_found = [f["name"] for f in analysis_result.get("functions", [])]
                summary = "Agent failed to provide a detailed summary, but a fallback analysis was successful."

            return mermaid_diagram, summary, functions_found, complexity_score, ""

    except Exception as e:
        error_msg = f"❌ Analysis failed: {str(e)}"
        print(f"Error details: {traceback.format_exc()}")
        return "", "", [], 0, error_msg

# --- Gradio Interface Setup (UPDATED) ---
def create_gradio_interface():
    """Create and configure the Gradio interface"""

    def analyze_code_gradio(code, language):
        """Wrapper function for Gradio interface"""
        if not code.strip():
            return (
                "Please enter some code to analyze",
                "",
                "",
                "No analysis performed",
                "Functions: 0 | Complexity: 0/100",
                ""
            )

        mermaid, summary, functions, complexity, error = analyze_code_with_agent(code, language)

        if error:
            return (
                error,
                "",
                "",
                "Analysis failed",
                "Functions: 0 | Complexity: 0/100",
                ""
            )

        # Format the outputs
        mermaid_code_display = f"```mermaid\n{mermaid}\n```" if mermaid else "No diagram generated"
        functions_display = f"**Functions Found:** {', '.join(functions)}" if functions else "No functions detected"
        stats_display = f"Functions: {len(functions)} | Complexity: {complexity}/100"

        return (
            "✅ Analysis completed successfully!",
            mermaid, # Raw Mermaid code for the gr.Mermaid component
            mermaid_code_display, # Markdown-formatted code for the textbox
            summary,
            stats_display,
            functions_display
        )

    # Define the interface
    with gr.Blocks(
        title="🔄 Code Flow Analyzer",
        theme=gr.themes.Soft(),
        css="""
        .gradio-container {
            max-width: 1200px !important;
        }
        .code-input {
            font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', monospace !important;
        }
        """
    ) as interface:

        gr.Markdown("""
        # 🔄 Code Flow Analyzer

        **LangChain Agent + Mermaid.js** • Visualize Your Code Flow

        This tool uses AI agents to analyze your source code and generate visual flowchart diagrams.
        """)

        # API Status
        model_info = ""
        if agent_executor and model:
            model_info = " (Gemini LLM)"
        api_status = f"🟢 Gemini LangChain Agent Ready{model_info}" if agent_executor else "🔴 Agent Not Available (Check GOOGLE_API_KEY)"
        gr.Markdown(f"**Status:** {api_status}")

        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown("### 📝 Source Code Input")

                language_dropdown = gr.Dropdown(
                    choices=["auto", "Python", "JavaScript", "Java", "C++", "Other"],
                    value="auto",
                    label="Programming Language",
                    info="Auto-detection usually works well"
                )

                code_input = gr.TextArea(
                    placeholder="Paste your source code here...",
                    lines=15,
                    label="Source Code",
                    elem_classes=["code-input"]
                )

                with gr.Row():
                    gr.Examples(
                        examples=[
                            [SAMPLE_PYTHON, "Python"],
                            [SAMPLE_JAVASCRIPT, "JavaScript"],
                            [SAMPLE_JAVA, "Java"]
                        ],
                        inputs=[code_input, language_dropdown],
                        label="Quick Examples"
                    )

                analyze_btn = gr.Button(
                    "🚀 Analyze Code Flow",
                    variant="primary",
                    size="lg"
                )

            with gr.Column(scale=1):
                gr.Markdown("### 📊 Analysis Results")

                status_output = gr.Textbox(
                    label="Status",
                    interactive=False
                )

                stats_output = gr.Textbox(
                    label="Statistics",
                    interactive=False
                )

                functions_output = gr.Markdown(
                    label="Functions Found"
                )

        with gr.Row():
            with gr.Column():
                gr.Markdown("### 🎨 Generated Mermaid Diagram")
                # Updated component to render the diagram visually
                mermaid_output = gr.Mermaid(
                    label="Flowchart Diagram"
                )

                # Keep the code in a collapsible block for users who want to copy it
                with gr.Accordion("Mermaid Code (for copying)", open=False):
                    mermaid_code_output = gr.Textbox(
                        label="Mermaid Code",
                        lines=10,
                        interactive=True,
                        show_copy_button=True
                    )
        
        with gr.Row():
            with gr.Column():
                gr.Markdown("### 📋 Analysis Summary")
                summary_output = gr.Textbox(
                    label="AI Agent Analysis",
                    lines=8,
                    interactive=False
                )

        # Connect the analyze button
        # Updated outputs to include the new mermaid_code_output component
        analyze_btn.click(
            fn=analyze_code_gradio,
            inputs=[code_input, language_dropdown],
            outputs=[status_output, mermaid_output, mermaid_code_output, summary_output, stats_output, functions_output]
        )

        # Footer
        environment_info = "Google Colab" if IN_COLAB else "Hugging Face Spaces or Local Environment"
        gr.Markdown(f"""
        ---
        **🛠️ Running in:** {environment_info}

        **📦 Dependencies:** gradio, langchain, langgraph, langchain-google-genai

        **🔧 Powered by:** LangChain Agents, Google Gemini, Mermaid.js, Gradio

        **🆓 Get Google API Key:** [aistudio.google.com/app/apikey](https://aistudio.google.com/app/apikey)
        """)

    return interface

def main():
    """Main function to run the application"""
    print("🔄 Code Flow Analyzer with Gradio")
    print("=" * 50)
    print(f"🌐 Environment: {'Google Colab' if IN_COLAB else 'Hugging Face Spaces or Local'}")

    if agent_executor:
        print("✅ LangChain agent ready")
    else:
        print("❌ LangChain agent not available")
        if IN_COLAB:
            print("   💡 Restart this cell and enter your GOOGLE_API_KEY when prompted")
        else:
            print("   💡 Please set your GOOGLE_API_KEY as an environment variable or secret")

    print("\n🚀 Starting Gradio interface...")

    # Create and launch the interface
    interface = create_gradio_interface()

    # Launch configuration for Colab vs local/Spaces
    interface.launch(
        share=True if IN_COLAB else False,
        debug=False,
        height=600,
        show_error=True
    )

# Auto-run if in Colab or when script is executed directly
if __name__ == "__main__" or IN_COLAB:
    main()
````