Integrate API call and enhance Gradio UI for code analysis

.gitignore

@@ -0,0 +1 @@
+.idea

app.py

@@ -1,7 +1,109 @@
 import gradio as gr
+import requests
+import os
 
-def greet(name):
-    return "Hello " + name + "!!"
+# API configuration
+API_BASE_URL = os.getenv("API_BASE_URL", "http://localhost:8000")
+API_KEY = os.getenv("API_KEY", "")
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+def analyze_code(code: str, language: str, model: str) -> str:
+    """Send code to the vulnerability analysis API and return results."""
+    if not code.strip():
+        return "Please enter some code to analyze."
+
+    api_url = f"{API_BASE_URL}/analyze"
+
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {API_KEY}"
+    }
+
+    payload = {
+        "code": code,
+        "model": model,
+        "language": language
+    }
+
+    try:
+        response = requests.post(api_url, json=payload, headers=headers, timeout=60)
+
+        if response.status_code == 403:
+            error_detail = response.json().get("detail", "Token limit exceeded.")
+            return f"Error: {error_detail}"
+
+        if response.status_code != 200:
+            error_detail = response.json().get("detail", f"API error: {response.status_code}")
+            return f"Error: {error_detail}"
+
+        result = response.json()
+
+        # Format the result
+        status = result.get("result", {}).get("status", "unknown")
+        cwe_type = result.get("result", {}).get("cweType", "N/A")
+        model_used = result.get("result", {}).get("model", model)
+        explanation = result.get("result", {}).get("response", "")
+
+        if status == "yes":
+            output = f"⚠️ **Vulnerability Detected**\n\n"
+            output += f"**CWE Type:** {cwe_type}\n"
+            output += f"**Model:** {model_used}\n\n"
+            output += f"**Analysis:**\n{explanation}"
+        else:
+            output = f"✅ **No Vulnerability Detected**\n\n"
+            output += f"**Model:** {model_used}\n\n"
+            output += f"**Analysis:**\n{explanation}"
+
+        return output
+
+    except requests.exceptions.Timeout:
+        return "Error: Request timed out. Please try again."
+    except requests.exceptions.ConnectionError:
+        return f"Error: Could not connect to API at {API_BASE_URL}"
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Language options
+LANGUAGES = [
+    "python", "javascript", "typescript", "java", "c", "cpp",
+    "csharp", "go", "rust", "php", "ruby", "swift", "kotlin"
+]
+
+# Model options
+MODELS = ["virtueguard-code", "claude-4-sonnet", "gpt-4.1"]
+
+# Create Gradio interface
+with gr.Blocks(title="Code Vulnerability Scanner") as demo:
+    gr.Markdown("# 🔍 Code Vulnerability Scanner")
+    gr.Markdown("Analyze your code for potential security vulnerabilities using LLM-based detection.")
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            code_input = gr.Code(
+                label="Code to Analyze",
+                language="python",
+                lines=15,
+                placeholder="Paste your code here..."
+            )
+        with gr.Column(scale=1):
+            language_dropdown = gr.Dropdown(
+                choices=LANGUAGES,
+                value="python",
+                label="Programming Language"
+            )
+            model_dropdown = gr.Dropdown(
+                choices=MODELS,
+                value="virtueguard-code",
+                label="Model"
+            )
+            analyze_btn = gr.Button("🔍 Analyze Code", variant="primary")
+
+    result_output = gr.Markdown(label="Analysis Result")
+
+    analyze_btn.click(
+        fn=analyze_code,
+        inputs=[code_input, language_dropdown, model_dropdown],
+        outputs=result_output
+    )
+
+if __name__ == "__main__":
+    demo.launch()