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#!/usr/bin/env python3
"""
Hugging Face Spaces frontend for UniversalAPIAgentTool
Connects to Modal Labs backend for API execution
"""
import gradio as gr
import requests
import json
import time
from typing import Dict, Any, Optional
# Modal backend URL (will be updated after deployment)
MODAL_BACKEND_URL = "https://jomasego--execute-api-call.modal.run"
def execute_api_call_via_modal(
base_url: str,
endpoint: str,
method: str = "GET",
headers: str = "",
params: str = "",
json_body: str = "",
timeout: int = 30
) -> tuple:
"""
Execute API call via Modal Labs backend
"""
try:
# Prepare request data for Modal backend
request_data = {
"base_url": base_url,
"endpoint": endpoint,
"method": method,
"headers": headers,
"params": params,
"json_body": json_body,
"timeout": timeout
}
# Call Modal backend
start_time = time.time()
response = requests.post(
MODAL_BACKEND_URL,
json=request_data,
timeout=60 # Give Modal more time
)
modal_execution_time = time.time() - start_time
if response.status_code == 200:
result = response.json()
# Format the response for display
status_code = result.get("status_code", 0)
response_headers = result.get("response_headers", {})
response_body = result.get("response_body", "")
execution_time = result.get("execution_time", 0.0)
error_message = result.get("error_message")
# Create formatted output
output_lines = [
f"π **API Call Executed via Modal Labs**",
f"β±οΈ **Execution Time**: {execution_time:.3f}s (Modal: {modal_execution_time:.3f}s)",
f"π **Status Code**: {status_code}",
""
]
if error_message:
output_lines.extend([
f"β **Error**: {error_message}",
""
])
if response_headers:
output_lines.extend([
"π **Response Headers**:",
"```json",
json.dumps(response_headers, indent=2),
"```",
""
])
if response_body:
output_lines.extend([
"π **Response Body**:",
"```json" if response_body.strip().startswith(('{', '[')) else "```",
response_body,
"```"
])
return "\n".join(output_lines), f"Status: {status_code}"
else:
return f"β **Modal Backend Error**\nStatus: {response.status_code}\nResponse: {response.text}", "Modal Error"
except requests.exceptions.Timeout:
return "β **Timeout Error**\nModal backend request timed out", "Timeout"
except requests.exceptions.ConnectionError:
return "β **Connection Error**\nCannot connect to Modal backend", "Connection Error"
except Exception as e:
return f"β **Error**\n{str(e)}", "Error"
def create_gradio_interface():
"""Create the Gradio interface"""
# Example configurations
examples = [
[
"https://api.coingecko.com",
"/api/v3/simple/price",
"GET",
"",
'{"ids": "bitcoin,ethereum", "vs_currencies": "usd"}',
"",
30
],
[
"https://api.github.com",
"/repos/microsoft/vscode",
"GET",
"",
"",
"",
30
],
[
"https://jsonplaceholder.typicode.com",
"/posts",
"POST",
'{"Content-Type": "application/json"}',
"",
'{"title": "Test Post", "body": "This is a test", "userId": 1}',
30
]
]
with gr.Blocks(
title="UniversalAPIAgentTool - HF Spaces + Modal Labs",
theme=gr.themes.Soft(),
css="""
.gradio-container {
max-width: 1200px !important;
}
.tab-nav {
background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
}
"""
) as interface:
gr.Markdown("""
# π UniversalAPIAgentTool
**Powered by Hugging Face Spaces + Modal Labs**
Universal MCP tool that enables AI agents to access any REST API. This frontend runs on HF Spaces while the backend executes on Modal Labs for optimal performance.
""")
with gr.Tab("π§ API Executor"):
gr.Markdown("### Execute HTTP requests to any REST API")
with gr.Row():
with gr.Column(scale=2):
base_url = gr.Textbox(
label="Base URL",
placeholder="https://api.example.com",
value="https://api.coingecko.com"
)
endpoint = gr.Textbox(
label="Endpoint",
placeholder="/api/v1/resource",
value="/api/v3/simple/price"
)
with gr.Column(scale=1):
method = gr.Dropdown(
choices=["GET", "POST", "PUT", "DELETE", "PATCH"],
value="GET",
label="HTTP Method"
)
timeout = gr.Slider(
minimum=5,
maximum=120,
value=30,
step=5,
label="Timeout (seconds)"
)
with gr.Row():
headers = gr.Textbox(
label="Headers (JSON)",
placeholder='{"Authorization": "Bearer token", "Content-Type": "application/json"}',
lines=2
)
params = gr.Textbox(
label="Query Parameters (JSON)",
placeholder='{"key": "value", "limit": 10}',
lines=2,
value='{"ids": "bitcoin,ethereum", "vs_currencies": "usd"}'
)
json_body = gr.Textbox(
label="JSON Body (for POST/PUT)",
placeholder='{"name": "value", "data": [1, 2, 3]}',
lines=3
)
with gr.Row():
execute_btn = gr.Button("π Execute API Call", variant="primary", size="lg")
clear_btn = gr.Button("ποΈ Clear", variant="secondary")
with gr.Row():
with gr.Column(scale=3):
output = gr.Markdown(label="Response")
with gr.Column(scale=1):
status = gr.Textbox(label="Status", interactive=False)
# Examples
gr.Markdown("### π Quick Examples")
gr.Examples(
examples=examples,
inputs=[base_url, endpoint, method, headers, params, json_body, timeout],
label="Try these examples"
)
with gr.Tab("π Documentation"):
gr.Markdown("""
## π― How to Use
1. **Base URL**: The root URL of the API (e.g., `https://api.github.com`)
2. **Endpoint**: The specific path (e.g., `/repos/owner/repo`)
3. **Method**: HTTP method (GET, POST, PUT, DELETE, PATCH)
4. **Headers**: Authentication and content type headers as JSON
5. **Parameters**: URL query parameters as JSON
6. **JSON Body**: Request payload for POST/PUT requests
## π Authentication Examples
### API Key in Headers
```json
{"X-API-Key": "your-api-key-here"}
```
### Bearer Token
```json
{"Authorization": "Bearer your-token-here"}
```
### Basic Auth (base64 encoded)
```json
{"Authorization": "Basic dXNlcjpwYXNz"}
```
## π Example APIs to Try
### πͺ Cryptocurrency Prices (CoinGecko)
- **URL**: `https://api.coingecko.com`
- **Endpoint**: `/api/v3/simple/price`
- **Params**: `{"ids": "bitcoin", "vs_currencies": "usd"}`
### π GitHub Repository Info
- **URL**: `https://api.github.com`
- **Endpoint**: `/repos/microsoft/vscode`
- **Method**: GET
### π Country Information
- **URL**: `https://restcountries.com`
- **Endpoint**: `/v3.1/name/germany`
- **Method**: GET
### π Test POST Requests
- **URL**: `https://jsonplaceholder.typicode.com`
- **Endpoint**: `/posts`
- **Method**: POST
- **Headers**: `{"Content-Type": "application/json"}`
- **Body**: `{"title": "Test", "body": "Content", "userId": 1}`
## π€ For AI Agents (MCP)
This tool can be used by AI agents via MCP with this function call:
```json
{
"tool_name": "UniversalAPIAgentTool",
"function_name": "execute_api_call",
"parameters": {
"base_url": "https://api.example.com",
"endpoint": "/v1/resource",
"method": "GET",
"headers": {"Authorization": "Bearer token"},
"params": {"query": "value"},
"json_body": {"data": "value"}
}
}
```
## ποΈ Architecture
- **Frontend**: Hugging Face Spaces (Gradio)
- **Backend**: Modal Labs (Python + FastAPI)
- **Benefits**: Scalable, fast, and reliable API execution
## π Hackathon Project
Built for the **Agents & MCP Hackathon** to demonstrate how MCP can expand AI agent capabilities through universal API access.
""")
with gr.Tab("π§ Backend Status"):
gr.Markdown(f"""
## π₯οΈ Modal Labs Backend
**Backend URL**: `{MODAL_BACKEND_URL}`
The backend is hosted on Modal Labs for optimal performance and scalability.
""")
def check_backend_health():
try:
health_url = MODAL_BACKEND_URL.replace("execute-api-call", "health-check")
response = requests.get(health_url, timeout=10)
if response.status_code == 200:
data = response.json()
return f"β
**Backend Status**: Healthy\n**Service**: {data.get('service', 'Unknown')}\n**Version**: {data.get('version', 'Unknown')}"
else:
return f"β οΈ **Backend Status**: Unhealthy (Status: {response.status_code})"
except Exception as e:
return f"β **Backend Status**: Error - {str(e)}"
health_output = gr.Markdown()
health_btn = gr.Button("π Check Backend Health")
health_btn.click(fn=check_backend_health, outputs=health_output)
# Event handlers
execute_btn.click(
fn=execute_api_call_via_modal,
inputs=[base_url, endpoint, method, headers, params, json_body, timeout],
outputs=[output, status]
)
def clear_inputs():
return "", "", "GET", "", "", "", 30
clear_btn.click(
fn=clear_inputs,
outputs=[base_url, endpoint, method, headers, params, json_body, timeout]
)
return interface
if __name__ == "__main__":
# Create and launch the interface
interface = create_gradio_interface()
interface.launch(
server_name="0.0.0.0",
server_port=7860,
share=False,
show_error=True
)
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