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Create app.py
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app.py
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@@ -0,0 +1,783 @@
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1 |
+
#!/usr/bin/env python3
|
2 |
+
"""
|
3 |
+
Code Flow Analyzer with Gradio Interface - Colab Compatible
|
4 |
+
A single-file application that uses LangChain agents to analyze code structure
|
5 |
+
and generate Mermaid.js flowchart diagrams through a web interface.
|
6 |
+
|
7 |
+
For Google Colab:
|
8 |
+
1. Run this cell to install dependencies and start the app
|
9 |
+
2. Enter your GROQ_API_KEY when prompted
|
10 |
+
3. The Gradio interface will appear inline
|
11 |
+
|
12 |
+
For local usage:
|
13 |
+
1. Set your API key: export GROQ_API_KEY="your-key-here"
|
14 |
+
2. Run: python code_flow_analyzer.py
|
15 |
+
3. Open the provided URL in your browser
|
16 |
+
"""
|
17 |
+
|
18 |
+
import ast
|
19 |
+
import re
|
20 |
+
import os
|
21 |
+
import traceback
|
22 |
+
import sys
|
23 |
+
from typing import Dict, Any, List, Tuple
|
24 |
+
import getpass
|
25 |
+
|
26 |
+
# Check if running in Colab
|
27 |
+
try:
|
28 |
+
import google.colab
|
29 |
+
IN_COLAB = True
|
30 |
+
print("π’ Running in Google Colab")
|
31 |
+
except ImportError:
|
32 |
+
IN_COLAB = False
|
33 |
+
print("π‘ Running locally")
|
34 |
+
|
35 |
+
# Install dependencies if in Colab
|
36 |
+
if IN_COLAB:
|
37 |
+
print("π¦ Installing dependencies...")
|
38 |
+
os.system("pip install -q gradio langchain langgraph langchain-groq")
|
39 |
+
print("β
Dependencies installed")
|
40 |
+
|
41 |
+
import gradio as gr
|
42 |
+
from langchain.chat_models import init_chat_model
|
43 |
+
from langchain.tools import tool
|
44 |
+
from langgraph.prebuilt import create_react_agent
|
45 |
+
from langgraph.checkpoint.memory import MemorySaver
|
46 |
+
|
47 |
+
# Sample code examples
|
48 |
+
SAMPLE_PYTHON = '''def main():
|
49 |
+
user_input = get_user_input()
|
50 |
+
if user_input:
|
51 |
+
result = process_data(user_input)
|
52 |
+
if result > 0:
|
53 |
+
display_result(result)
|
54 |
+
else:
|
55 |
+
show_error()
|
56 |
+
else:
|
57 |
+
show_help()
|
58 |
+
|
59 |
+
def get_user_input():
|
60 |
+
return input("Enter data: ")
|
61 |
+
|
62 |
+
def process_data(data):
|
63 |
+
for i in range(len(data)):
|
64 |
+
if data[i].isdigit():
|
65 |
+
return int(data[i])
|
66 |
+
return -1
|
67 |
+
|
68 |
+
def display_result(result):
|
69 |
+
print(f"Result: {result}")
|
70 |
+
|
71 |
+
def show_error():
|
72 |
+
print("Error processing data")
|
73 |
+
|
74 |
+
def show_help():
|
75 |
+
print("Please provide valid input")'''
|
76 |
+
|
77 |
+
SAMPLE_JAVASCRIPT = '''function calculateTotal(items) {
|
78 |
+
let total = 0;
|
79 |
+
for (let item of items) {
|
80 |
+
if (item.price > 0) {
|
81 |
+
total += item.price;
|
82 |
+
}
|
83 |
+
}
|
84 |
+
return total;
|
85 |
+
}
|
86 |
+
|
87 |
+
function processOrder(order) {
|
88 |
+
if (validateOrder(order)) {
|
89 |
+
const total = calculateTotal(order.items);
|
90 |
+
if (total > 100) {
|
91 |
+
applyDiscount(order);
|
92 |
+
}
|
93 |
+
return generateReceipt(order);
|
94 |
+
} else {
|
95 |
+
throw new Error("Invalid order");
|
96 |
+
}
|
97 |
+
}
|
98 |
+
|
99 |
+
function validateOrder(order) {
|
100 |
+
return order && order.items && order.items.length > 0;
|
101 |
+
}
|
102 |
+
|
103 |
+
function applyDiscount(order) {
|
104 |
+
order.discount = 0.1; // 10% discount
|
105 |
+
}
|
106 |
+
|
107 |
+
function generateReceipt(order) {
|
108 |
+
return {
|
109 |
+
items: order.items,
|
110 |
+
total: calculateTotal(order.items),
|
111 |
+
timestamp: new Date()
|
112 |
+
};
|
113 |
+
}'''
|
114 |
+
|
115 |
+
SAMPLE_JAVA = '''public class Calculator {
|
116 |
+
public static void main(String[] args) {
|
117 |
+
Calculator calc = new Calculator();
|
118 |
+
int result = calc.performCalculation();
|
119 |
+
calc.displayResult(result);
|
120 |
+
}
|
121 |
+
|
122 |
+
public int performCalculation() {
|
123 |
+
int a = getUserInput();
|
124 |
+
int b = getUserInput();
|
125 |
+
|
126 |
+
if (a > b) {
|
127 |
+
return multiply(a, b);
|
128 |
+
} else {
|
129 |
+
return add(a, b);
|
130 |
+
}
|
131 |
+
}
|
132 |
+
|
133 |
+
private int add(int x, int y) {
|
134 |
+
return x + y;
|
135 |
+
}
|
136 |
+
|
137 |
+
private int multiply(int x, int y) {
|
138 |
+
return x * y;
|
139 |
+
}
|
140 |
+
|
141 |
+
private int getUserInput() {
|
142 |
+
return 5; // Simplified for demo
|
143 |
+
}
|
144 |
+
|
145 |
+
private void displayResult(int result) {
|
146 |
+
System.out.println("Result: " + result);
|
147 |
+
}
|
148 |
+
}'''
|
149 |
+
|
150 |
+
# Handle API key setup
|
151 |
+
def setup_api_key():
|
152 |
+
"""Setup API key for both Colab and local environments"""
|
153 |
+
api_key = os.getenv("GROQ_API_KEY")
|
154 |
+
|
155 |
+
if not api_key:
|
156 |
+
if IN_COLAB:
|
157 |
+
print("π Please enter your Groq API key:")
|
158 |
+
print(" Get a free key from: https://console.groq.com/")
|
159 |
+
api_key = getpass.getpass("GROQ_API_KEY: ")
|
160 |
+
if api_key:
|
161 |
+
os.environ["GROQ_API_KEY"] = api_key
|
162 |
+
print("β
API key set successfully")
|
163 |
+
else:
|
164 |
+
print("β οΈ No API key provided - agent features will be disabled")
|
165 |
+
else:
|
166 |
+
print("β οΈ GROQ_API_KEY not found in environment variables")
|
167 |
+
print(" Get a free API key from: https://console.groq.com/")
|
168 |
+
print(" Set it with: export GROQ_API_KEY='your-key-here'")
|
169 |
+
else:
|
170 |
+
print("β
Groq API key found")
|
171 |
+
|
172 |
+
return api_key or os.getenv("GROQ_API_KEY")
|
173 |
+
|
174 |
+
# Setup API key
|
175 |
+
api_key = setup_api_key()
|
176 |
+
|
177 |
+
# Initialize LangChain components
|
178 |
+
model = None
|
179 |
+
memory = None
|
180 |
+
agent_executor = None
|
181 |
+
|
182 |
+
if api_key:
|
183 |
+
try:
|
184 |
+
model = init_chat_model("groq:qwen2.5-32b-instruct")
|
185 |
+
memory = MemorySaver()
|
186 |
+
print("β
Groq model initialized successfully")
|
187 |
+
except Exception as e:
|
188 |
+
print(f"β Could not initialize LangChain model: {e}")
|
189 |
+
print(" Please check your API key and internet connection")
|
190 |
+
model = None
|
191 |
+
memory = None
|
192 |
+
|
193 |
+
@tool
|
194 |
+
def analyze_code_structure(source_code: str) -> Dict[str, Any]:
|
195 |
+
"""
|
196 |
+
Analyzes source code structure to identify functions, control flow, and dependencies.
|
197 |
+
Returns structured data about the code that can be used to generate flow diagrams.
|
198 |
+
"""
|
199 |
+
try:
|
200 |
+
# Try to parse as Python first
|
201 |
+
try:
|
202 |
+
tree = ast.parse(source_code)
|
203 |
+
return _analyze_python_ast(tree)
|
204 |
+
except SyntaxError:
|
205 |
+
# If Python parsing fails, do basic text analysis
|
206 |
+
return _analyze_code_text(source_code)
|
207 |
+
|
208 |
+
except Exception as e:
|
209 |
+
return {"error": f"Analysis error: {str(e)}"}
|
210 |
+
|
211 |
+
def _analyze_python_ast(tree) -> Dict[str, Any]:
|
212 |
+
"""Analyze Python AST"""
|
213 |
+
analysis = {
|
214 |
+
"functions": [],
|
215 |
+
"classes": [],
|
216 |
+
"control_flows": [],
|
217 |
+
"imports": [],
|
218 |
+
"call_graph": {}
|
219 |
+
}
|
220 |
+
|
221 |
+
class CodeAnalyzer(ast.NodeVisitor):
|
222 |
+
def __init__(self):
|
223 |
+
self.current_function = None
|
224 |
+
|
225 |
+
def visit_FunctionDef(self, node):
|
226 |
+
func_info = {
|
227 |
+
"name": node.name,
|
228 |
+
"line": node.lineno,
|
229 |
+
"args": [arg.arg for arg in node.args.args],
|
230 |
+
"calls": [],
|
231 |
+
"conditions": [],
|
232 |
+
"loops": []
|
233 |
+
}
|
234 |
+
|
235 |
+
self.current_function = node.name
|
236 |
+
analysis["call_graph"][node.name] = []
|
237 |
+
|
238 |
+
# Analyze function body
|
239 |
+
for child in ast.walk(node):
|
240 |
+
if isinstance(child, ast.Call):
|
241 |
+
if hasattr(child.func, 'id'):
|
242 |
+
func_info["calls"].append(child.func.id)
|
243 |
+
analysis["call_graph"][node.name].append(child.func.id)
|
244 |
+
elif hasattr(child.func, 'attr'):
|
245 |
+
func_info["calls"].append(child.func.attr)
|
246 |
+
elif isinstance(child, ast.If):
|
247 |
+
func_info["conditions"].append(f"if condition at line {child.lineno}")
|
248 |
+
elif isinstance(child, (ast.For, ast.While)):
|
249 |
+
loop_type = "for" if isinstance(child, ast.For) else "while"
|
250 |
+
func_info["loops"].append(f"{loop_type} loop at line {child.lineno}")
|
251 |
+
|
252 |
+
analysis["functions"].append(func_info)
|
253 |
+
self.generic_visit(node)
|
254 |
+
|
255 |
+
def visit_ClassDef(self, node):
|
256 |
+
class_info = {
|
257 |
+
"name": node.name,
|
258 |
+
"line": node.lineno,
|
259 |
+
"methods": []
|
260 |
+
}
|
261 |
+
|
262 |
+
for item in node.body:
|
263 |
+
if isinstance(item, ast.FunctionDef):
|
264 |
+
class_info["methods"].append(item.name)
|
265 |
+
|
266 |
+
analysis["classes"].append(class_info)
|
267 |
+
self.generic_visit(node)
|
268 |
+
|
269 |
+
def visit_Import(self, node):
|
270 |
+
for alias in node.names:
|
271 |
+
analysis["imports"].append(alias.name)
|
272 |
+
self.generic_visit(node)
|
273 |
+
|
274 |
+
def visit_ImportFrom(self, node):
|
275 |
+
module = node.module or ""
|
276 |
+
for alias in node.names:
|
277 |
+
analysis["imports"].append(f"{module}.{alias.name}")
|
278 |
+
self.generic_visit(node)
|
279 |
+
|
280 |
+
analyzer = CodeAnalyzer()
|
281 |
+
analyzer.visit(tree)
|
282 |
+
return analysis
|
283 |
+
|
284 |
+
def _analyze_code_text(source_code: str) -> Dict[str, Any]:
|
285 |
+
"""Basic text-based code analysis for non-Python code"""
|
286 |
+
lines = source_code.split('\n')
|
287 |
+
analysis = {
|
288 |
+
"functions": [],
|
289 |
+
"classes": [],
|
290 |
+
"control_flows": [],
|
291 |
+
"imports": [],
|
292 |
+
"call_graph": {}
|
293 |
+
}
|
294 |
+
|
295 |
+
for i, line in enumerate(lines, 1):
|
296 |
+
line = line.strip()
|
297 |
+
|
298 |
+
# JavaScript function detection
|
299 |
+
js_func_match = re.match(r'function\s+(\w+)\s*\(', line)
|
300 |
+
if js_func_match:
|
301 |
+
func_name = js_func_match.group(1)
|
302 |
+
analysis["functions"].append({
|
303 |
+
"name": func_name,
|
304 |
+
"line": i,
|
305 |
+
"args": [],
|
306 |
+
"calls": [],
|
307 |
+
"conditions": [],
|
308 |
+
"loops": []
|
309 |
+
})
|
310 |
+
analysis["call_graph"][func_name] = []
|
311 |
+
|
312 |
+
# Java/C++ method detection
|
313 |
+
java_method_match = re.match(r'(?:public|private|protected)?\s*(?:static)?\s*\w+\s+(\w+)\s*\(', line)
|
314 |
+
if java_method_match and not js_func_match:
|
315 |
+
func_name = java_method_match.group(1)
|
316 |
+
if func_name not in ['class', 'if', 'for', 'while']: # Avoid keywords
|
317 |
+
analysis["functions"].append({
|
318 |
+
"name": func_name,
|
319 |
+
"line": i,
|
320 |
+
"args": [],
|
321 |
+
"calls": [],
|
322 |
+
"conditions": [],
|
323 |
+
"loops": []
|
324 |
+
})
|
325 |
+
analysis["call_graph"][func_name] = []
|
326 |
+
|
327 |
+
# Control structures
|
328 |
+
if re.match(r'\s*(if|else|elif|switch)\s*[\(\{]', line):
|
329 |
+
analysis["control_flows"].append(f"condition at line {i}")
|
330 |
+
|
331 |
+
if re.match(r'\s*(for|while|do)\s*[\(\{]', line):
|
332 |
+
analysis["control_flows"].append(f"loop at line {i}")
|
333 |
+
|
334 |
+
return analysis
|
335 |
+
|
336 |
+
@tool
|
337 |
+
def generate_mermaid_diagram(analysis_data: Dict[str, Any]) -> str:
|
338 |
+
"""
|
339 |
+
Generates a Mermaid.js flowchart diagram from code analysis data.
|
340 |
+
Creates a visual representation of the code flow including function calls and control structures.
|
341 |
+
"""
|
342 |
+
if "error" in analysis_data:
|
343 |
+
return f"flowchart TD\n Error[β {analysis_data['error']}]"
|
344 |
+
|
345 |
+
functions = analysis_data.get("functions", [])
|
346 |
+
call_graph = analysis_data.get("call_graph", {})
|
347 |
+
|
348 |
+
if not functions:
|
349 |
+
return """flowchart TD
|
350 |
+
Start([π Program Start]) --> NoFunc[No Functions Found]
|
351 |
+
NoFunc --> End([π Program End])
|
352 |
+
|
353 |
+
classDef startEnd fill:#e1f5fe,stroke:#01579b,stroke-width:2px
|
354 |
+
classDef warning fill:#fff3e0,stroke:#e65100,stroke-width:2px
|
355 |
+
|
356 |
+
class Start,End startEnd
|
357 |
+
class NoFunc warning"""
|
358 |
+
|
359 |
+
mermaid_lines = ["flowchart TD"]
|
360 |
+
mermaid_lines.append(" Start([π Program Start]) --> Main")
|
361 |
+
|
362 |
+
# Create nodes for each function
|
363 |
+
func_nodes = []
|
364 |
+
for i, func in enumerate(functions):
|
365 |
+
func_name = func["name"]
|
366 |
+
safe_name = re.sub(r'[^a-zA-Z0-9_]', '_', func_name)
|
367 |
+
node_id = f"F{i}_{safe_name}"
|
368 |
+
func_nodes.append(node_id)
|
369 |
+
|
370 |
+
# Function node with emoji
|
371 |
+
mermaid_lines.append(f" {node_id}[βοΈ {func_name}()]")
|
372 |
+
|
373 |
+
# Add control structures within function
|
374 |
+
conditions = func.get("conditions", [])
|
375 |
+
loops = func.get("loops", [])
|
376 |
+
|
377 |
+
if conditions:
|
378 |
+
for j, condition in enumerate(conditions[:2]): # Limit to 2 conditions per function
|
379 |
+
cond_id = f"{node_id}_C{j}"
|
380 |
+
mermaid_lines.append(f" {node_id} --> {cond_id}{{π€ Decision}}")
|
381 |
+
mermaid_lines.append(f" {cond_id} -->|Yes| {node_id}_Y{j}[β
True Path]")
|
382 |
+
mermaid_lines.append(f" {cond_id} -->|No| {node_id}_N{j}[β False Path]")
|
383 |
+
|
384 |
+
if loops:
|
385 |
+
for j, loop in enumerate(loops[:1]): # Limit to 1 loop per function
|
386 |
+
loop_id = f"{node_id}_L{j}"
|
387 |
+
loop_type = "π Loop" if "for" in loop else "β° While Loop"
|
388 |
+
mermaid_lines.append(f" {node_id} --> {loop_id}[{loop_type}]")
|
389 |
+
mermaid_lines.append(f" {loop_id} --> {loop_id}") # Self-loop
|
390 |
+
|
391 |
+
# Connect main flow
|
392 |
+
if func_nodes:
|
393 |
+
mermaid_lines.append(f" Main --> {func_nodes[0]}")
|
394 |
+
|
395 |
+
# Connect functions in sequence (simplified)
|
396 |
+
for i in range(len(func_nodes) - 1):
|
397 |
+
mermaid_lines.append(f" {func_nodes[i]} --> {func_nodes[i + 1]}")
|
398 |
+
|
399 |
+
# Connect to end
|
400 |
+
mermaid_lines.append(f" {func_nodes[-1]} --> End([π Program End])")
|
401 |
+
|
402 |
+
# Add function call relationships (simplified to avoid clutter)
|
403 |
+
call_count = 0
|
404 |
+
for caller, callees in call_graph.items():
|
405 |
+
if call_count >= 3: # Limit number of call relationships
|
406 |
+
break
|
407 |
+
caller_node = None
|
408 |
+
for node in func_nodes:
|
409 |
+
if caller.lower() in node.lower():
|
410 |
+
caller_node = node
|
411 |
+
break
|
412 |
+
|
413 |
+
if caller_node:
|
414 |
+
for callee in callees[:2]: # Limit callees per function
|
415 |
+
callee_node = None
|
416 |
+
for node in func_nodes:
|
417 |
+
if callee.lower() in node.lower():
|
418 |
+
callee_node = node
|
419 |
+
break
|
420 |
+
if callee_node and callee_node != caller_node:
|
421 |
+
mermaid_lines.append(f" {caller_node} -.->|calls| {callee_node}")
|
422 |
+
call_count += 1
|
423 |
+
|
424 |
+
# Add styling
|
425 |
+
mermaid_lines.extend([
|
426 |
+
"",
|
427 |
+
" classDef startEnd fill:#e1f5fe,stroke:#01579b,stroke-width:3px,color:#000",
|
428 |
+
" classDef process fill:#f3e5f5,stroke:#4a148c,stroke-width:2px,color:#000",
|
429 |
+
" classDef decision fill:#fff3e0,stroke:#e65100,stroke-width:2px,color:#000",
|
430 |
+
" classDef success fill:#e8f5e8,stroke:#2e7d32,stroke-width:2px,color:#000",
|
431 |
+
" classDef error fill:#ffebee,stroke:#c62828,stroke-width:2px,color:#000",
|
432 |
+
"",
|
433 |
+
" class Start,End startEnd",
|
434 |
+
f" class {','.join(func_nodes)} process" if func_nodes else ""
|
435 |
+
])
|
436 |
+
|
437 |
+
return "\n".join(mermaid_lines)
|
438 |
+
|
439 |
+
@tool
|
440 |
+
def calculate_complexity_score(analysis_data: Dict[str, Any]) -> int:
|
441 |
+
"""
|
442 |
+
Calculates a complexity score for the code based on various metrics.
|
443 |
+
Higher scores indicate more complex code structure.
|
444 |
+
"""
|
445 |
+
if "error" in analysis_data:
|
446 |
+
return 0
|
447 |
+
|
448 |
+
score = 0
|
449 |
+
functions = analysis_data.get("functions", [])
|
450 |
+
|
451 |
+
# Base score for number of functions
|
452 |
+
score += len(functions) * 3
|
453 |
+
|
454 |
+
# Add score for control structures
|
455 |
+
for func in functions:
|
456 |
+
score += len(func.get("conditions", [])) * 4 # Conditions add complexity
|
457 |
+
score += len(func.get("loops", [])) * 3 # Loops add complexity
|
458 |
+
score += len(func.get("calls", [])) * 1 # Function calls add some complexity
|
459 |
+
score += len(func.get("args", [])) * 1 # Parameters add complexity
|
460 |
+
|
461 |
+
# Add score for classes
|
462 |
+
score += len(analysis_data.get("classes", [])) * 5
|
463 |
+
|
464 |
+
return min(score, 100) # Cap at 100
|
465 |
+
|
466 |
+
# Create the agent if model is available
|
467 |
+
if model and memory:
|
468 |
+
tools = [analyze_code_structure, generate_mermaid_diagram, calculate_complexity_score]
|
469 |
+
agent_executor = create_react_agent(model, tools, checkpointer=memory)
|
470 |
+
print("β
LangChain agent created successfully")
|
471 |
+
else:
|
472 |
+
agent_executor = None
|
473 |
+
print("β LangChain agent not available")
|
474 |
+
|
475 |
+
def analyze_code_with_agent(source_code: str, language: str = "auto") -> Tuple[str, str, List[str], int, str]:
|
476 |
+
"""
|
477 |
+
Main function that uses the LangChain agent to analyze code and generate diagrams.
|
478 |
+
Returns: (mermaid_diagram, analysis_summary, functions_found, complexity_score, error_message)
|
479 |
+
"""
|
480 |
+
if not source_code.strip():
|
481 |
+
return "", "No code provided", [], 0, "Please enter some source code to analyze"
|
482 |
+
|
483 |
+
if not agent_executor:
|
484 |
+
return "", "Agent not available", [], 0, "β LangChain agent not initialized. Please check your GROQ_API_KEY"
|
485 |
+
|
486 |
+
try:
|
487 |
+
# Detect language if auto
|
488 |
+
if language == "auto":
|
489 |
+
if "def " in source_code or "import " in source_code:
|
490 |
+
language = "Python"
|
491 |
+
elif "function " in source_code or "const " in source_code or "let " in source_code:
|
492 |
+
language = "JavaScript"
|
493 |
+
elif ("public " in source_code and "class " in source_code) or "System.out" in source_code:
|
494 |
+
language = "Java"
|
495 |
+
elif "#include" in source_code or "std::" in source_code:
|
496 |
+
language = "C++"
|
497 |
+
else:
|
498 |
+
language = "Unknown"
|
499 |
+
|
500 |
+
config = {"configurable": {"thread_id": f"session_{hash(source_code) % 10000}"}}
|
501 |
+
|
502 |
+
prompt = f"""
|
503 |
+
You are a code analysis expert. Analyze the following {language} source code and create a visual flow diagram.
|
504 |
+
|
505 |
+
Code to analyze:
|
506 |
+
```{language.lower()}
|
507 |
+
{source_code}
|
508 |
+
```
|
509 |
+
|
510 |
+
Please follow these steps exactly:
|
511 |
+
1. First use the analyze_code_structure tool to understand the code structure
|
512 |
+
2. Then use the generate_mermaid_diagram tool to create a flowchart
|
513 |
+
3. Use the calculate_complexity_score tool to assess code complexity
|
514 |
+
4. Provide a brief summary of what the code does and its structure
|
515 |
+
|
516 |
+
Important guidelines:
|
517 |
+
- Focus on the main flow between functions and important control structures
|
518 |
+
- Keep the diagram clean and readable
|
519 |
+
- Identify all functions and their relationships
|
520 |
+
- Note any loops, conditionals, and function calls
|
521 |
+
- Provide insights about code organization and complexity
|
522 |
+
|
523 |
+
Please be thorough in your analysis and make sure to use all three tools in order.
|
524 |
+
"""
|
525 |
+
|
526 |
+
# Execute the agent
|
527 |
+
result = agent_executor.invoke(
|
528 |
+
{"messages": [{"role": "user", "content": prompt}]},
|
529 |
+
config
|
530 |
+
)
|
531 |
+
|
532 |
+
if result and "messages" in result:
|
533 |
+
response = result["messages"][-1].content
|
534 |
+
|
535 |
+
# Extract Mermaid diagram
|
536 |
+
mermaid_match = re.search(r'```mermaid\n(.*?)\n```', response, re.DOTALL)
|
537 |
+
if not mermaid_match:
|
538 |
+
mermaid_match = re.search(r'flowchart TD.*?(?=\n\n|\Z)', response, re.DOTALL)
|
539 |
+
|
540 |
+
mermaid_diagram = mermaid_match.group(1) if mermaid_match else mermaid_match.group(0) if mermaid_match else ""
|
541 |
+
|
542 |
+
# If no mermaid found in response, try to generate directly
|
543 |
+
if not mermaid_diagram:
|
544 |
+
print("β οΈ No Mermaid diagram found in response, generating fallback...")
|
545 |
+
# Fallback: analyze directly
|
546 |
+
analysis_result = analyze_code_structure.invoke({"source_code": source_code})
|
547 |
+
mermaid_diagram = generate_mermaid_diagram.invoke({"analysis_data": analysis_result})
|
548 |
+
|
549 |
+
# Extract complexity score
|
550 |
+
complexity_match = re.search(r'complexity.*?(\d+)', response, re.IGNORECASE)
|
551 |
+
complexity_score = int(complexity_match.group(1)) if complexity_match else 1
|
552 |
+
|
553 |
+
# Extract functions
|
554 |
+
functions_found = []
|
555 |
+
func_matches = re.findall(r'Functions found:.*?([^\n]+)', response, re.IGNORECASE)
|
556 |
+
if func_matches:
|
557 |
+
functions_found = [f.strip() for f in func_matches[0].split(',')]
|
558 |
+
else:
|
559 |
+
# Fallback: extract from analysis
|
560 |
+
analysis_result = analyze_code_structure.invoke({"source_code": source_code})
|
561 |
+
functions_found = [f["name"] for f in analysis_result.get("functions", [])]
|
562 |
+
|
563 |
+
# Clean up the response for summary
|
564 |
+
summary = re.sub(r'```mermaid.*?```', '', response, flags=re.DOTALL)
|
565 |
+
summary = re.sub(r'flowchart TD.*?(?=\n\n|\Z)', '', summary, flags=re.DOTALL)
|
566 |
+
summary = summary.strip()
|
567 |
+
|
568 |
+
return mermaid_diagram, summary, functions_found, complexity_score, ""
|
569 |
+
|
570 |
+
except Exception as e:
|
571 |
+
error_msg = f"β Analysis failed: {str(e)}"
|
572 |
+
print(f"Error details: {traceback.format_exc()}") # For debugging
|
573 |
+
return "", "", [], 0, error_msg
|
574 |
+
|
575 |
+
def create_gradio_interface():
|
576 |
+
"""Create and configure the Gradio interface"""
|
577 |
+
|
578 |
+
def analyze_code_gradio(code, language):
|
579 |
+
"""Wrapper function for Gradio interface"""
|
580 |
+
if not code.strip():
|
581 |
+
return (
|
582 |
+
"Please enter some code to analyze",
|
583 |
+
"",
|
584 |
+
"No analysis performed",
|
585 |
+
"Functions: 0 | Complexity: 0/100",
|
586 |
+
""
|
587 |
+
)
|
588 |
+
|
589 |
+
mermaid, summary, functions, complexity, error = analyze_code_with_agent(code, language)
|
590 |
+
|
591 |
+
if error:
|
592 |
+
return (
|
593 |
+
error,
|
594 |
+
"",
|
595 |
+
"Analysis failed",
|
596 |
+
"Functions: 0 | Complexity: 0/100",
|
597 |
+
""
|
598 |
+
)
|
599 |
+
|
600 |
+
# Format the outputs
|
601 |
+
mermaid_display = f"```mermaid\n{mermaid}\n```" if mermaid else "No diagram generated"
|
602 |
+
functions_display = f"**Functions Found:** {', '.join(functions)}" if functions else "No functions detected"
|
603 |
+
stats_display = f"Functions: {len(functions)} | Complexity: {complexity}/100"
|
604 |
+
|
605 |
+
return (
|
606 |
+
"β
Analysis completed successfully!",
|
607 |
+
mermaid_display,
|
608 |
+
summary,
|
609 |
+
stats_display,
|
610 |
+
functions_display
|
611 |
+
)
|
612 |
+
|
613 |
+
# Define the interface
|
614 |
+
with gr.Blocks(
|
615 |
+
title="π Code Flow Analyzer",
|
616 |
+
theme=gr.themes.Soft(),
|
617 |
+
css="""
|
618 |
+
.gradio-container {
|
619 |
+
max-width: 1200px !important;
|
620 |
+
}
|
621 |
+
.code-input {
|
622 |
+
font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', monospace !important;
|
623 |
+
}
|
624 |
+
"""
|
625 |
+
) as interface:
|
626 |
+
|
627 |
+
gr.Markdown("""
|
628 |
+
# π Code Flow Analyzer
|
629 |
+
|
630 |
+
**LangChain Agent + Mermaid.js** β’ Visualize Your Code Flow
|
631 |
+
|
632 |
+
This tool uses AI agents to analyze your source code and generate visual flowchart diagrams.
|
633 |
+
""")
|
634 |
+
|
635 |
+
# API Status
|
636 |
+
api_status = "π’ Groq LangChain Agent Ready (Qwen 2.5-32B)" if agent_executor else "π΄ Agent Not Available (Check GROQ_API_KEY)"
|
637 |
+
gr.Markdown(f"**Status:** {api_status}")
|
638 |
+
|
639 |
+
with gr.Row():
|
640 |
+
with gr.Column(scale=1):
|
641 |
+
gr.Markdown("### π Source Code Input")
|
642 |
+
|
643 |
+
language_dropdown = gr.Dropdown(
|
644 |
+
choices=["auto", "Python", "JavaScript", "Java", "C++", "Other"],
|
645 |
+
value="auto",
|
646 |
+
label="Programming Language",
|
647 |
+
info="Auto-detection usually works well"
|
648 |
+
)
|
649 |
+
|
650 |
+
code_input = gr.TextArea(
|
651 |
+
placeholder="Paste your source code here...",
|
652 |
+
lines=15,
|
653 |
+
label="Source Code",
|
654 |
+
elem_classes=["code-input"]
|
655 |
+
)
|
656 |
+
|
657 |
+
with gr.Row():
|
658 |
+
gr.Examples(
|
659 |
+
examples=[
|
660 |
+
[SAMPLE_PYTHON, "Python"],
|
661 |
+
[SAMPLE_JAVASCRIPT, "JavaScript"],
|
662 |
+
[SAMPLE_JAVA, "Java"]
|
663 |
+
],
|
664 |
+
inputs=[code_input, language_dropdown],
|
665 |
+
label="Quick Examples"
|
666 |
+
)
|
667 |
+
|
668 |
+
analyze_btn = gr.Button(
|
669 |
+
"π Analyze Code Flow",
|
670 |
+
variant="primary",
|
671 |
+
size="lg"
|
672 |
+
)
|
673 |
+
|
674 |
+
with gr.Column(scale=1):
|
675 |
+
gr.Markdown("### π Analysis Results")
|
676 |
+
|
677 |
+
status_output = gr.Textbox(
|
678 |
+
label="Status",
|
679 |
+
interactive=False
|
680 |
+
)
|
681 |
+
|
682 |
+
stats_output = gr.Textbox(
|
683 |
+
label="Statistics",
|
684 |
+
interactive=False
|
685 |
+
)
|
686 |
+
|
687 |
+
functions_output = gr.Markdown(
|
688 |
+
label="Functions Found"
|
689 |
+
)
|
690 |
+
|
691 |
+
with gr.Row():
|
692 |
+
with gr.Column():
|
693 |
+
gr.Markdown("### π¨ Generated Mermaid Diagram")
|
694 |
+
mermaid_output = gr.Textbox(
|
695 |
+
label="Mermaid Code",
|
696 |
+
lines=15,
|
697 |
+
max_lines=20,
|
698 |
+
interactive=True,
|
699 |
+
show_copy_button=True
|
700 |
+
)
|
701 |
+
|
702 |
+
gr.Markdown("""
|
703 |
+
**π‘ How to visualize:**
|
704 |
+
1. Copy the Mermaid code above
|
705 |
+
2. Visit [mermaid.live](https://mermaid.live)
|
706 |
+
3. Paste and see your code flow diagram!
|
707 |
+
|
708 |
+
**π± For Colab users:**
|
709 |
+
- The Mermaid code above shows your program's flow structure
|
710 |
+
- Copy it to mermaid.live for a beautiful visual diagram
|
711 |
+
- Try the examples above to see different code patterns
|
712 |
+
""")
|
713 |
+
|
714 |
+
with gr.Row():
|
715 |
+
with gr.Column():
|
716 |
+
gr.Markdown("### π Analysis Summary")
|
717 |
+
summary_output = gr.Textbox(
|
718 |
+
label="AI Agent Analysis",
|
719 |
+
lines=8,
|
720 |
+
interactive=False
|
721 |
+
)
|
722 |
+
|
723 |
+
# Connect the analyze button
|
724 |
+
analyze_btn.click(
|
725 |
+
fn=analyze_code_gradio,
|
726 |
+
inputs=[code_input, language_dropdown],
|
727 |
+
outputs=[status_output, mermaid_output, summary_output, stats_output, functions_output]
|
728 |
+
)
|
729 |
+
|
730 |
+
# Footer
|
731 |
+
environment_info = "Google Colab" if IN_COLAB else "Local Environment"
|
732 |
+
gr.Markdown(f"""
|
733 |
+
---
|
734 |
+
**π οΈ Running in:** {environment_info}
|
735 |
+
|
736 |
+
**π¦ Dependencies:** gradio, langchain, langgraph, langchain-groq
|
737 |
+
|
738 |
+
**π§ Powered by:** LangChain Agents, Groq API (Qwen 2.5-32B), Mermaid.js, Gradio
|
739 |
+
|
740 |
+
**π Get Groq API Key:** [console.groq.com](https://console.groq.com/) (Free tier available!)
|
741 |
+
""")
|
742 |
+
|
743 |
+
return interface
|
744 |
+
|
745 |
+
def main():
|
746 |
+
"""Main function to run the application"""
|
747 |
+
print("π Code Flow Analyzer with Gradio")
|
748 |
+
print("=" * 50)
|
749 |
+
print(f"π Environment: {'Google Colab' if IN_COLAB else 'Local'}")
|
750 |
+
|
751 |
+
if agent_executor:
|
752 |
+
print("β
LangChain agent ready")
|
753 |
+
else:
|
754 |
+
print("β LangChain agent not available")
|
755 |
+
if IN_COLAB:
|
756 |
+
print(" π‘ Restart this cell and enter your GROQ_API_KEY when prompted")
|
757 |
+
|
758 |
+
print("\nπ Starting Gradio interface...")
|
759 |
+
|
760 |
+
# Create and launch the interface
|
761 |
+
interface = create_gradio_interface()
|
762 |
+
|
763 |
+
# Launch configuration for Colab vs local
|
764 |
+
if IN_COLAB:
|
765 |
+
# For Colab, use public sharing and specific settings
|
766 |
+
interface.launch(
|
767 |
+
share=True, # Enable public sharing for Colab
|
768 |
+
debug=False,
|
769 |
+
height=600,
|
770 |
+
show_error=True
|
771 |
+
)
|
772 |
+
else:
|
773 |
+
# For local usage
|
774 |
+
interface.launch(
|
775 |
+
server_name="0.0.0.0",
|
776 |
+
server_port=7860,
|
777 |
+
share=False, # Disable sharing for local use
|
778 |
+
debug=False
|
779 |
+
)
|
780 |
+
|
781 |
+
# Auto-run if in Colab or when script is executed directly
|
782 |
+
if __name__ == "__main__" or IN_COLAB:
|
783 |
+
main()
|