Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import pipeline
+import re
+import time
+
+# Initialize the model pipeline
+model_id = "openai/gpt-oss-120b"
+pipe = None
+
+def initialize_model():
+    global pipe
+    try:
+        pipe = pipeline(
+            "text-generation",
+            model=model_id,
+            torch_dtype="auto",
+            device_map="auto",
+        )
+        return "✅ Model loaded successfully!"
+    except Exception as e:
+        return f"❌ Error loading model: {str(e)}"
+
+def generate_code(prompt, task_type, language, max_tokens, temperature):
+    if pipe is None:
+        return "❌ Model not initialized. Please load the model first.", ""
+    
+    try:
+        # Customize prompt based on task type
+        if task_type == "Generate Code":
+            system_prompt = f"You are an expert {language} programmer. Generate clean, optimized, and well-commented code for the following request:"
+            full_prompt = f"{system_prompt}\n\n{prompt}\n\nCode:"
+        elif task_type == "Fix Bugs":
+            system_prompt = f"You are an expert {language} debugger. Analyze the following code and fix all bugs, then provide the corrected version:"
+            full_prompt = f"{system_prompt}\n\n{prompt}\n\nFixed Code:"
+        elif task_type == "Optimize Code":
+            system_prompt = f"You are an expert {language} optimizer. Analyze and optimize the following code for better performance and readability:"
+            full_prompt = f"{system_prompt}\n\n{prompt}\n\nOptimized Code:"
+        else:  # Explain Code
+            system_prompt = f"You are an expert {language} teacher. Explain the following code step by step:"
+            full_prompt = f"{system_prompt}\n\n{prompt}\n\nExplanation:"
+        
+        messages = [
+            {"role": "user", "content": full_prompt},
+        ]
+        
+        outputs = pipe(
+            messages,
+            max_new_tokens=int(max_tokens),
+            temperature=temperature,
+            do_sample=True,
+            pad_token_id=pipe.tokenizer.eos_token_id
+        )
+        
+        generated_text = outputs[0]["generated_text"][-1]["content"] if isinstance(outputs[0]["generated_text"], list) else outputs[0]["generated_text"]
+        
+        # Extract code if it's wrapped in code blocks
+        code_match = re.search(r'```(?:\w+\n)?(.*?)```', generated_text, re.DOTALL)
+        if code_match:
+            code_output = code_match.group(1).strip()
+        else:
+            code_output = generated_text.strip()
+        
+        # Generate explanation based on the output
+        explanation = f"Task completed successfully! Generated {len(code_output)} characters of {language} code."
+        if task_type == "Fix Bugs":
+            explanation = "Bugs have been identified and fixed. Please review the corrected code."
+        elif task_type == "Optimize Code":
+            explanation = "Code has been optimized for better performance and readability."
+        elif task_type == "Explain Code":
+            explanation = "Code explanation provided below."
+        
+        return code_output, explanation
+        
+    except Exception as e:
+        return f"❌ Error generating code: {str(e)}", "Please try again with different parameters."
+
+# Custom CSS for modern UI
+css = """
+.gradio-container {
+    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+}
+
+.header {
+    text-align: center;
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+    color: white;
+    padding: 2rem;
+    border-radius: 15px;
+    margin-bottom: 2rem;
+    box-shadow: 0 10px 30px rgba(0,0,0,0.2);
+}
+
+.header h1 {
+    font-size: 2.5rem;
+    font-weight: 700;
+    margin: 0;
+    text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
+}
+
+.header p {
+    font-size: 1.2rem;
+    margin: 0.5rem 0 0 0;
+    opacity: 0.9;
+}
+
+.custom-button {
+    background: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%);
+    border: none;
+    color: white;
+    font-weight: 600;
+    border-radius: 8px;
+    transition: all 0.3s ease;
+}
+
+.custom-button:hover {
+    transform: translateY(-2px);
+    box-shadow: 0 5px 15px rgba(79, 172, 254, 0.4);
+}
+
+.footer {
+    text-align: center;
+    margin-top: 3rem;
+    padding: 2rem;
+    background: linear-gradient(135deg, #2d3436 0%, #636e72 100%);
+    color: white;
+    border-radius: 15px;
+    box-shadow: 0 5px 15px rgba(0,0,0,0.1);
+}
+
+.footer h3 {
+    margin: 0 0 1rem 0;
+    font-size: 1.3rem;
+}
+
+.footer a {
+    color: #74b9ff;
+    text-decoration: none;
+    margin: 0 1rem;
+    font-weight: 500;
+    transition: color 0.3s ease;
+}
+
+.footer a:hover {
+    color: #0984e3;
+}
+
+.status-box {
+    padding: 1rem;
+    border-radius: 8px;
+    margin: 1rem 0;
+    font-weight: 500;
+}
+
+.code-output {
+    background: #1e1e1e;
+    border-radius: 8px;
+    border: 1px solid #333;
+}
+
+.explanation-output {
+    background: linear-gradient(135deg, #a8edea 0%, #fed6e3 100%);
+    border-radius: 8px;
+    padding: 1rem;
+}
+"""
+
+# Create the Gradio interface
+with gr.Blocks(css=css, title="AI Code Generator & Bug Fixer", theme=gr.themes.Soft()) as demo:
+    
+    # Header
+    gr.HTML("""
+    <div class="header">
+        <h1>🚀 AI Code Generator & Bug Fixer</h1>
+        <p>Powered by Advanced AI • Generate, Fix, Optimize & Explain Code</p>
+    </div>
+    """)
+    
+    # Model initialization section
+    with gr.Row():
+        with gr.Column(scale=3):
+            model_status = gr.Textbox(
+                label="🤖 Model Status", 
+                value="Click 'Initialize Model' to load the AI model",
+                interactive=False
+            )
+        with gr.Column(scale=1):
+            init_btn = gr.Button(
+                "Initialize Model", 
+                variant="primary",
+                elem_classes=["custom-button"]
+            )
+    
+    gr.Markdown("---")
+    
+    # Main interface
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### ⚙️ Configuration")
+            
+            task_type = gr.Dropdown(
+                choices=["Generate Code", "Fix Bugs", "Optimize Code", "Explain Code"],
+                value="Generate Code",
+                label="🎯 Task Type"
+            )
+            
+            language = gr.Dropdown(
+                choices=["Python", "JavaScript", "Java", "C++", "C#", "Go", "Rust", "TypeScript", "PHP", "Ruby"],
+                value="Python",
+                label="💻 Programming Language"
+            )
+            
+            max_tokens = gr.Slider(
+                minimum=50,
+                maximum=1000,
+                value=256,
+                step=50,
+                label="📏 Max Tokens"
+            )
+            
+            temperature = gr.Slider(
+                minimum=0.1,
+                maximum=1.0,
+                value=0.7,
+                step=0.1,
+                label="🌡️ Temperature (Creativity)"
+            )
+            
+            generate_btn = gr.Button(
+                "🚀 Generate/Fix Code",
+                variant="primary",
+                size="lg",
+                elem_classes=["custom-button"]
+            )
+            
+        with gr.Column(scale=2):
+            gr.Markdown("### 📝 Input")
+            
+            prompt = gr.Textbox(
+                label="Your Code Request or Buggy Code",
+                placeholder="Example: Create a function to sort a list of dictionaries by a specific key...",
+                lines=8
+            )
+            
+            gr.Markdown("### 💡 Examples")
+            examples = gr.Examples(
+                examples=[
+                    ["Generate Code", "Python", "Create a REST API with FastAPI for user management with CRUD operations"],
+                    ["Fix Bugs", "JavaScript", "function calculateSum(arr) {\n  let sum = 0;\n  for (let i = 0; i <= arr.length; i++) {\n    sum += arr[i];\n  }\n  return sum;\n}"],
+                    ["Optimize Code", "Python", "def fibonacci(n):\n  if n <= 1:\n    return n\n  return fibonacci(n-1) + fibonacci(n-2)"],
+                    ["Explain Code", "Python", "class decorator(func):\n  def wrapper(*args, **kwargs):\n    print('Before')\n    result = func(*args, **kwargs)\n    print('After')\n    return result\n  return wrapper"]
+                ],
+                inputs=[task_type, language, prompt]
+            )
+    
+    gr.Markdown("---")
+    
+    # Output section
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### 📤 Generated Code")
+            code_output = gr.Code(
+                label="Result",
+                language="python",
+                elem_classes=["code-output"]
+            )
+            
+            gr.Markdown("### 💬 AI Explanation")
+            explanation_output = gr.Textbox(
+                label="Analysis & Explanation",
+                lines=3,
+                elem_classes=["explanation-output"]
+            )
+    
+    # Footer
+    gr.HTML("""
+    <div class="footer">
+        <h3>🛠️ Built by Hariom Kumar Pandit</h3>
+        <p>
+            <a href="https://github.com/hari7261" target="_blank">🐙 GitHub: hari7261</a>
+            <a href="https://huggingface.co/hari7261" target="_blank">🤗 HuggingFace: hari7261</a>
+        </p>
+        <p style="margin-top: 1rem; font-size: 0.9rem; opacity: 0.8;">
+            Empowering developers with AI-assisted coding • Made with ❤️
+        </p>
+    </div>
+    """)
+    
+    # Event handlers
+    init_btn.click(
+        fn=initialize_model,
+        outputs=model_status
+    )
+    
+    generate_btn.click(
+        fn=generate_code,
+        inputs=[prompt, task_type, language, max_tokens, temperature],
+        outputs=[code_output, explanation_output]
+    )
+    
+    # Update code language based on selection
+    def update_code_language(lang):
+        return gr.Code(language=lang.lower())
+    
+    language.change(
+        fn=update_code_language,
+        inputs=language,
+        outputs=code_output
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True,
+        debug=True
+    )