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Rename app_with_image.py to app.py
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"""
Python to C++ Code Optimizer - AI-Powered Code Conversion
Modern Gradio app with password protection for secure deployments
Supported Models:
- GPT-4o (OpenAI) - Premium, fastest, most accurate
- Claude-3.5-Sonnet (Anthropic) - Premium, excellent for code
⚠️ SECURITY WARNING:
This app executes arbitrary code. Only run code from trusted sources.
Malicious code can harm the system. Use at your own risk.
"""
import os
import io
import sys
import subprocess
import socket
import httpx
from openai import OpenAI
import anthropic
# Workaround for Python 3.13 where stdlib audioop is removed; instruct pydub to use pure-python fallback
os.environ.setdefault("PYDUB_SIMPLE_AUDIOOP", "1")
import gradio as gr
# Try to load from .env file if available
try:
from dotenv import load_dotenv
load_dotenv()
except ImportError:
pass
# PASSWORD PROTECTION
# Set this as a Hugging Face Secret: APP_PASSWORD
APP_PASSWORD = os.environ.get("APP_PASSWORD", "demo123") # Change default!
# Lazy initialization of AI clients with explicit HTTP client to avoid Gradio conflicts
def get_openai_client():
api_key = os.environ.get("OPENAI_API_KEY")
if not api_key:
raise ValueError("OPENAI_API_KEY not found. Please set it in your environment or .env file.")
# Create a clean HTTP client without proxies to avoid Gradio conflicts
http_client = httpx.Client(
timeout=60.0,
limits=httpx.Limits(max_keepalive_connections=5, max_connections=10)
)
return OpenAI(api_key=api_key, http_client=http_client)
def get_claude_client():
api_key = os.environ.get("ANTHROPIC_API_KEY")
if not api_key:
raise ValueError("ANTHROPIC_API_KEY not found. Please set it in your environment or .env file.")
# Create a clean HTTP client without proxies to avoid Gradio conflicts
http_client = httpx.Client(
timeout=60.0,
limits=httpx.Limits(max_keepalive_connections=5, max_connections=10)
)
return anthropic.Anthropic(api_key=api_key, http_client=http_client)
# Model configurations
OPENAI_MODEL = "gpt-4o"
CLAUDE_MODEL = "claude-3-5-sonnet-20240620"
# System and user prompts
system_message = (
"You are an assistant that reimplements Python code in high performance C++. "
"Respond only with C++ code; use comments sparingly and do not provide any explanation other than occasional comments. "
"The C++ response needs to produce an identical output in the fastest possible time."
)
def user_prompt_for(python):
user_prompt = (
"Rewrite this Python code in C++ with the fastest possible implementation that produces identical output in the least time. "
"Respond only with C++ code; do not explain your work other than a few comments. "
"Pay attention to number types to ensure no int overflows. Remember to #include all necessary C++ packages such as iomanip.\n\n"
)
user_prompt += python
return user_prompt
def messages_for(python):
return [
{"role": "system", "content": system_message},
{"role": "user", "content": user_prompt_for(python)}
]
def write_output(cpp):
"""Write C++ code to file for compilation"""
code = cpp.replace("```cpp","").replace("```","")
with open("optimized.cpp", "w") as f:
f.write(code)
def stream_gpt(python):
"""Stream GPT-4o response"""
try:
client = get_openai_client()
stream = client.chat.completions.create(
model=OPENAI_MODEL,
messages=messages_for(python),
stream=True
)
reply = ""
for chunk in stream:
fragment = chunk.choices[0].delta.content or ""
reply += fragment
yield reply.replace('```cpp\n','').replace('```','')
except ValueError as e:
yield f"❌ Error: {str(e)}"
except Exception as e:
yield f"❌ Error: {str(e)}"
def stream_claude(python):
"""Stream Claude response"""
try:
client = get_claude_client()
result = client.messages.stream(
model=CLAUDE_MODEL,
max_tokens=2000,
system=system_message,
messages=[{"role": "user", "content": user_prompt_for(python)}],
)
reply = ""
with result as stream:
for text in stream.text_stream:
reply += text
yield reply.replace('```cpp\n','').replace('```','')
except ValueError as e:
yield f"❌ Error: {str(e)}"
except Exception as e:
yield f"❌ Error: {str(e)}"
def optimize(python, model):
"""Convert Python to C++ using selected AI model"""
if model in ["GPT-4o", "GPT"]:
result = stream_gpt(python)
elif model in ["Claude-3.5-Sonnet", "Claude"]:
result = stream_claude(python)
else:
raise ValueError(f"Unknown model: {model}")
for stream_so_far in result:
yield stream_so_far
def execute_python(code):
"""⚠️ WARNING: Executes arbitrary Python code"""
try:
output = io.StringIO()
sys.stdout = output
exec(code)
finally:
sys.stdout = sys.__stdout__
return output.getvalue()
def execute_cpp(code):
"""⚠️ WARNING: Compiles and executes arbitrary C++ code"""
write_output(code)
try:
compile_cmd = ["g++", "-O3", "-std=c++17", "-o", "optimized", "optimized.cpp"]
compile_result = subprocess.run(
compile_cmd,
check=True,
text=True,
capture_output=True,
timeout=30
)
run_cmd = ["./optimized"]
run_result = subprocess.run(
run_cmd,
check=True,
text=True,
capture_output=True,
timeout=30
)
return run_result.stdout
except subprocess.TimeoutExpired:
return "⚠️ Execution timed out (30 seconds limit)"
except subprocess.CalledProcessError as e:
return f"❌ An error occurred:\n{e.stderr}"
except Exception as e:
return f"❌ Unexpected error: {str(e)}"
# Example Python code
default_python = """import time
def calculate(iterations, param1, param2):
result = 1.0
for i in range(1, iterations+1):
j = i * param1 - param2
result -= (1/j)
j = i * param1 + param2
result += (1/j)
return result
start_time = time.time()
result = calculate(100_000_000, 4, 1) * 4
end_time = time.time()
print(f"Result: {result:.12f}")
print(f"Execution Time: {(end_time - start_time):.6f} seconds")
"""
# Modern CSS
modern_css = """
.gradio-container, body, html {
background: #f9fafb !important; /* light background to avoid dark-mode conflicts */
color: #111827 !important; /* ensure readable text */
}
.gradio-container {
max-width: 1400px !important;
margin: 0 auto !important;
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
}
.modern-header {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
padding: 24px;
border-radius: 16px;
margin-bottom: 24px;
text-align: center;
box-shadow: 0 8px 32px rgba(0, 0, 0, 0.1);
}
.modern-header h1 {
margin: 0;
font-size: 32px;
font-weight: 700;
letter-spacing: -0.5px;
}
.modern-header p {
margin: 12px 0 0 0;
opacity: 0.9;
font-size: 18px;
font-weight: 400;
}
.security-warning {
background: #fee2e2 !important;
border: 2px solid #dc2626 !important;
border-radius: 12px !important;
padding: 16px !important;
margin: 16px 0 !important;
}
.python-input {
background: #f8fafc !important;
border: 2px solid #e2e8f0 !important;
border-radius: 12px !important;
padding: 16px !important;
font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', monospace !important;
font-size: 14px !important;
color: #1e293b !important;
line-height: 1.5 !important;
}
.python-input:focus {
border-color: #3b82f6 !important;
box-shadow: 0 0 0 3px rgba(59, 130, 246, 0.1) !important;
}
.cpp-output {
background: #f1f5f9 !important;
border: 2px solid #cbd5e1 !important;
border-radius: 12px !important;
padding: 16px !important;
font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', monospace !important;
font-size: 14px !important;
color: #0f172a !important;
line-height: 1.5 !important;
}
.model-selector {
background: white !important;
border: 2px solid #e2e8f0 !important;
border-radius: 12px !important;
padding: 12px 16px !important;
font-size: 16px !important;
color: #374151 !important;
}
.model-selector:focus {
border-color: #3b82f6 !important;
box-shadow: 0 0 0 3px rgba(59, 130, 246, 0.1) !important;
}
.modern-button {
background: linear-gradient(135deg, #3b82f6 0%, #1d4ed8 100%) !important;
color: white !important;
border: none !important;
border-radius: 12px !important;
padding: 14px 28px !important;
font-weight: 600 !important;
font-size: 16px !important;
cursor: pointer !important;
transition: all 0.2s ease !important;
box-shadow: 0 4px 6px rgba(59, 130, 246, 0.2) !important;
}
.modern-button:hover {
transform: translateY(-2px) !important;
box-shadow: 0 8px 12px rgba(59, 130, 246, 0.3) !important;
}
.run-button {
background: linear-gradient(135deg, #10b981 0%, #059669 100%) !important;
color: white !important;
border: none !important;
border-radius: 10px !important;
padding: 12px 24px !important;
font-weight: 600 !important;
font-size: 14px !important;
cursor: pointer !important;
transition: all 0.2s ease !important;
box-shadow: 0 4px 6px rgba(16, 185, 129, 0.2) !important;
}
.run-button:hover {
transform: translateY(-1px) !important;
box-shadow: 0 6px 8px rgba(16, 185, 129, 0.3) !important;
}
.output-section {
background: #f8fafc;
border: 1px solid #e2e8f0;
border-radius: 12px;
padding: 16px;
margin: 12px 0;
font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', monospace;
font-size: 13px;
line-height: 1.4;
color: #374151;
min-height: 100px;
overflow-y: auto;
}
.python-output {
background: #fef3c7 !important;
border: 2px solid #f59e0b !important;
color: #92400e !important;
}
.cpp-output-result {
background: #dbeafe !important;
border: 2px solid #3b82f6 !important;
color: #1e40af !important;
}
.performance-card {
background: linear-gradient(135deg, #f0f9ff 0%, #e0f2fe 100%);
border: 1px solid #0ea5e9;
border-radius: 12px;
padding: 20px;
margin: 16px 0;
text-align: center;
}
.performance-card h3 {
margin: 0 0 12px 0;
color: #0c4a6e;
font-size: 18px;
font-weight: 600;
}
.performance-metric {
display: inline-block;
background: white;
border-radius: 8px;
padding: 8px 16px;
margin: 4px;
font-weight: 600;
color: #0c4a6e;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
/* Login styling */
body, .gradio-container {
background: url('assets/3656064.jpg') center/cover no-repeat fixed !important;
}
.login-wrapper {
display: flex;
align-items: center;
justify-content: center;
min-height: 100vh;
padding: 24px;
background: linear-gradient(rgba(0, 0, 0, 0.4), rgba(0, 0, 0, 0.4)), url('assets/3656064.jpg') center/cover no-repeat fixed;
}
.login-card {
max-width: 400px;
width: 100%;
background: rgba(255, 255, 255, 0.95);
backdrop-filter: blur(10px);
border: 1px solid rgba(255, 255, 255, 0.3);
border-radius: 20px;
padding: 48px 40px;
box-shadow: 0 20px 60px rgba(0, 0, 0, 0.3);
}
.login-icon {
font-size: 56px;
text-align: center;
margin-bottom: 16px;
filter: drop-shadow(0 2px 4px rgba(0,0,0,0.1));
}
.login-title {
text-align: center;
margin: 0 0 8px 0;
font-size: 28px;
font-weight: 700;
color: #1f2937;
letter-spacing: -0.02em;
}
.login-subtitle {
text-align: center;
margin: 0 0 32px 0;
font-size: 15px;
color: #6b7280;
font-weight: 400;
}
.login-input label {
color: #374151 !important;
font-weight: 600 !important;
font-size: 14px !important;
}
.login-input input {
background: white !important;
border: 2px solid #e5e7eb !important;
border-radius: 10px !important;
padding: 12px 16px !important;
font-size: 16px !important;
color: #111827 !important;
}
.login-input input:focus {
border-color: #667eea !important;
background: white !important;
outline: none !important;
box-shadow: 0 0 0 4px rgba(102, 126, 234, 0.1) !important;
}
.login-error {
color: #dc2626;
background: #fee2e2;
border: 1px solid #fca5a5;
padding: 12px;
border-radius: 10px;
text-align: center;
font-weight: 500;
}
.freepik-credit {
margin-top: 24px;
text-align: center;
font-size: 12px;
color: #9ca3af;
}
.freepik-credit a {
color: #667eea;
text-decoration: none;
font-weight: 500;
}
.freepik-credit a:hover {
text-decoration: underline;
}
"""
# Create the interface with password protection
def create_interface():
with gr.Blocks(css=modern_css, title="Python to C++ Code Optimizer", theme=gr.themes.Soft()) as app:
authorized = gr.State(False)
# Background image is handled via CSS: assets/3656064.jpg
# For Hugging Face Space: use /file=assets/3656064.jpg in CSS
# For local: use assets/3656064.jpg in CSS
def check_password(pw):
ok = pw == APP_PASSWORD
return (
gr.update(visible=not ok), # login hidden when ok
gr.update(visible=ok), # main shown when ok
gr.update(value="" if ok else "Invalid password", visible=not ok)
)
# Login gate
with gr.Group(visible=True) as login_group:
gr.HTML("""
<div class="login-wrapper">
<div class="login-card">
<div class="login-icon">πŸ”</div>
<div class="login-title">Private Access</div>
<div class="login-subtitle">Enter password to continue</div>
""")
pw = gr.Textbox(
label="Password",
type="password",
placeholder="Enter password",
elem_classes=["login-input"],
container=True
)
login_btn = gr.Button("Continue", elem_classes=["modern-button"], size="lg")
login_error = gr.Markdown(visible=False, elem_classes=["login-error"])
gr.HTML("""
<div class="freepik-credit">Background image by <a href="https://www.freepik.com" target="_blank">Freepik</a></div>
</div>
</div>
""")
# Main UI wrapped for toggling visibility
with gr.Group(visible=False) as main_group:
main_group.elem_id = "main_group"
# Header Section
gr.HTML("""
<div class="modern-header">
<h1>πŸš€ Python to C++ Code Optimizer</h1>
<p>AI-powered code conversion with real-time execution and performance analysis</p>
</div>
""")
# Security Warning
gr.HTML("""
<div class="security-warning">
<h3 style="color: #dc2626; margin: 0 0 8px 0;">⚠️ Security Warning</h3>
<p style="margin: 0; color: #991b1b; font-weight: 500;">
This interface executes arbitrary code. <strong>Only run code from trusted sources.</strong><br>
Malicious code can harm your system. Use at your own risk.
</p>
</div>
""")
# Main Content Area
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### πŸ“ Python Code Input")
python_input = gr.Textbox(
label="Python Code:",
value=default_python,
lines=15,
elem_classes=["python-input"],
placeholder="Enter your Python code here..."
)
with gr.Row():
model_selector = gr.Dropdown(
["GPT-4o", "Claude-3.5-Sonnet"],
label="Select AI Model",
value="GPT-4o",
elem_classes=["model-selector"]
)
convert_button = gr.Button("✨ Convert to C++", elem_classes=["modern-button"])
with gr.Column(scale=1):
gr.Markdown("### ⚑ Optimized C++ Code")
cpp_output = gr.Textbox(
label="Generated C++ Code:",
lines=15,
elem_classes=["cpp-output"],
interactive=False
)
# Execution Section
gr.Markdown("---")
gr.Markdown("## πŸƒ Code Execution & Performance Comparison")
with gr.Row():
with gr.Column():
gr.Markdown("### 🐍 Python Output")
run_python_button = gr.Button("▢️ Run Python", elem_classes=["run-button"])
python_output = gr.Textbox(
label="Python Execution Output:",
lines=5,
elem_classes=["python-output"],
interactive=False
)
with gr.Column():
gr.Markdown("### ⚑ C++ Output")
run_cpp_button = gr.Button("▢️ Run C++", elem_classes=["run-button"])
cpp_execution_output = gr.Textbox(
label="C++ Execution Output:",
lines=5,
elem_classes=["cpp-output-result"],
interactive=False
)
# Event handlers
convert_button.click(
fn=optimize,
inputs=[python_input, model_selector],
outputs=cpp_output
)
run_python_button.click(
fn=execute_python,
inputs=python_input,
outputs=python_output
)
run_cpp_button.click(
fn=execute_cpp,
inputs=cpp_output,
outputs=cpp_execution_output
)
# Bind login after main_group is defined
login_btn.click(
fn=check_password,
inputs=[pw],
outputs=[login_group, main_group, login_error]
)
return app
# Launch the app
if __name__ == "__main__":
print("\n" + "="*50)
print(f"===== Application Startup at {__import__('datetime').datetime.now().strftime('%Y-%m-%d %H:%M:%S')} =====")
print("="*50)
# Create the app
app = create_interface()
# Check if running on Hugging Face Spaces
is_huggingface = os.getenv("SPACE_ID") is not None
if is_huggingface:
# Hugging Face Spaces configuration
print("πŸš€ Launching Python to C++ Code Optimizer on Hugging Face Spaces")
print("πŸ” Password protection enabled")
app.launch(
show_error=True
)
else:
# Local development configuration
def get_available_port(start_port=7860):
"""Find an available port starting from start_port"""
port = start_port
while port < start_port + 100:
try:
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.bind(('', port))
return port
except OSError:
port += 1
return start_port
port = get_available_port()
print(f"πŸš€ Launching Python to C++ Code Optimizer on port: {port}")
print(f"πŸ” Password protection enabled. Password: {APP_PASSWORD}")
app.launch(
server_name="127.0.0.1",
server_port=port,
show_error=True
)