import threading import time import gradio as gr import logging import json import re import torch import tempfile import subprocess import ast import os import dataclasses from pathlib import Path from typing import Dict, List, Tuple, Optional, Any, Union from dataclasses import dataclass, field from enum import Enum from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline from sentence_transformers import SentenceTransformer import faiss import numpy as np from PIL import Image from templates import TemplateManager, Template # Import TemplateManager and Template # Configure logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', handlers=[ logging.StreamHandler(), logging.FileHandler('gradio_builder.log') ] ) logger = logging.getLogger(__name__) # Constants DEFAULT_PORT = 7860 MODEL_CACHE_DIR = Path("model_cache") TEMPLATE_DIR = Path("templates") TEMP_DIR = Path("temp") DATABASE_PATH = Path("code_database.json") # Path for our simple database # Ensure directories exist for directory in [MODEL_CACHE_DIR, TEMPLATE_DIR, TEMP_DIR]: directory.mkdir(exist_ok=True, parents=True) class RAGSystem: def __init__(self, model_name: str = "gpt2", device: str = "cuda" if torch.cuda.is_available() else "cpu", embedding_model="all-mpnet-base-v2"): try: self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.model = AutoModelForCausalLM.from_pretrained(model_name).to(device) self.device = device self.pipe = pipeline("text-generation", model=self.model, tokenizer=self.tokenizer, device=self.device) self.embedding_model = SentenceTransformer(embedding_model) self.load_database() logger.info("RAG system initialized successfully.") except Exception as e: logger.error(f"Error loading language model or embedding model: {e}. Falling back to placeholder generation.") self.pipe = None self.embedding_model = None self.code_embeddings = None def load_database(self): """Loads or creates the code database""" if DATABASE_PATH.exists(): try: with open(DATABASE_PATH, 'r', encoding='utf-8') as f: self.database = json.load(f) self.code_embeddings = np.array(self.database['embeddings']) logger.info("Loaded code database from file.") except (json.JSONDecodeError, KeyError) as e: logger.error(f"Error loading code database: {e}. Creating new database.") self.database = {'codes': [], 'embeddings': []} self.code_embeddings = np.array([]) else: logger.info("Code database does not exist. Creating new database.") self.database = {'codes': [], 'embeddings': []} self.code_embeddings = np.array([]) if self.embedding_model and len(self.database['codes']) != len(self.database['embeddings']): logger.warning("Mismatch between number of codes and embeddings, rebuilding embeddings.") self.rebuild_embeddings() elif self.embedding_model is None: logger.warning("Embeddings are not supported in this context.") # Index the embeddings for efficient searching if len(self.code_embeddings) > 0 and self.embedding_model: self.index = faiss.IndexFlatL2(self.code_embeddings.shape[1]) # L2 distance self.index.add(self.code_embeddings) def add_to_database(self, code: str): """Adds a code snippet to the database""" try: embedding = self.embedding_model.encode(code) self.database['codes'].append(code) self.database['embeddings'].append(embedding.tolist()) self.code_embeddings = np.vstack((self.code_embeddings, embedding)) self.index.add(np.array([embedding])) # update FAISS index self.save_database() logger.info(f"Added code snippet to database. Total size: {len(self.database['codes'])}.") except Exception as e: logger.error(f"Error adding to database: {e}") def save_database(self): """Saves the database to a file""" try: with open(DATABASE_PATH, 'w', encoding='utf-8') as f: json.dump(self.database, f, indent=2) logger.info(f"Saved database to {DATABASE_PATH}.") except Exception as e: logger.error(f"Error saving database: {e}") def rebuild_embeddings(self): """Rebuilds embeddings from the codes""" try: embeddings = self.embedding_model.encode(self.database['codes']) self.code_embeddings = embeddings self.database['embeddings'] = embeddings.tolist() self.index = faiss.IndexFlatL2(embeddings.shape[1]) # L2 distance self.index.add(embeddings) self.save_database() logger.info("Rebuilt and saved embeddings to the database.") except Exception as e: logger.error(f"Error rebuilding embeddings: {e}") def retrieve_similar_code(self, description: str, top_k: int = 3) -> List[str]: """Retrieves similar code snippets from the database""" if self.embedding_model is None: logger.warning("Embedding model is not available. Cannot retrieve similar code.") return [] try: embedding = self.embedding_model.encode(description) D, I = self.index.search(np.array([embedding]), top_k) logger.info(f"Retrieved {top_k} similar code snippets for description: {description}.") return [self.database['codes'][i] for i in I[0]] except Exception as e: logger.error(f"Error retrieving similar code: {e}") return [] def generate_code(self, description: str, template_code: str) -> str: retrieved_codes = self.retrieve_similar_code(description) prompt = f"Description: {description}\nRetrieved Code Snippets:\n{''.join([f'```python\n{code}\n```\n' for code in retrieved_codes])}\nTemplate:\n```python\n{template_code}\n```\nGenerated Code:\n```python\n" if self.pipe: try: generated_text = self.pipe(prompt, max_length=500, num_return_sequences=1)[0]['generated_text'] generated_code = generated_text.split("Generated Code:")[1].strip().split('```')[0] logger.info("Code generated successfully.") return generated_code except Exception as e: logger.error(f"Error generating code with language model: {e}. Returning template code.") return template_code else: logger.warning("Text generation pipeline is not available. Returning placeholder code.") return f"# Placeholder code generation. Description: {description}\n{template_code}" def generate_interface(self, screenshot: Optional[Image.Image], description: str) -> str: retrieved_codes = self.retrieve_similar_code(description) prompt = f"Create a Gradio interface based on this description: {description}\nRetrieved Code Snippets:\n{''.join([f'```python\n{code}\n```\n' for code in retrieved_codes])}" if screenshot: prompt += "\nThe interface should resemble the provided screenshot." prompt += "\n```python\n" if self.pipe: try: generated_text = self.pipe(prompt, max_length=500, num_return_sequences=1)[0]['generated_text'] generated_code = generated_text.split("```")[1].strip() logger.info("Interface code generated successfully.") return generated_code except Exception as e: logger.error(f"Error generating interface with language model: {e}. Returning placeholder.") return "import gradio as gr\n\ndemo = gr.Interface(fn=lambda x:x, inputs='text', outputs='text')\ndemo.launch()" else: logger.warning("Text generation pipeline is not available. Returning placeholder interface code.") return "import gradio as gr\n\ndemo = gr.Interface(fn=lambda x:x, inputs='text', outputs='text')\ndemo.launch()" class PreviewManager: def __init__(self): self.preview_code = "" def update_preview(self, code: str): """Update the preview with the generated code.""" self.preview_code = code logger.info("Preview updated with new code.") class GradioInterface: def __init__(self): self.template_manager = TemplateManager(TEMPLATE_DIR) self.template_manager.load_templates() self.current_code = "" self.rag_system = RAGSystem() self.preview_manager = PreviewManager() def _extract_components(self, code: str) -> List[str]: """Extract components from the code.""" components = [] function_matches = re.findall(r'def (\w+)', code components.extend(function_matches) class_matches = re.findall(r'class (\w+)', code) components.extend(class_matches) logger.info(f"Extracted components: {components}") return components def _get_template_choices(self) -> List[str]: """Get available template choices.""" choices = list(self.template_manager.templates.keys()) logger.info(f"Available template choices: {choices}") return choices def launch(self, **kwargs): with gr.Blocks() as interface: gr.Markdown("## Code Generation Interface") description_input = gr.Textbox(label="Description", placeholder="Enter a description for the code you want to generate.") code_output = gr.Textbox(label="Generated Code", interactive=False) generate_button = gr.Button("Generate Code") template_choice = gr.Dropdown(label="Select Template", choices=self._get_template_choices(), value=None) save_button = gr.Button("Save as Template") generate_button.click( fn=self.generate_code, inputs=[description_input, template_choice], outputs=code_output ) save_button.click( fn=self.save_template, inputs=[code_output, template_choice, description_input], outputs=code_output ) gr.Markdown("### Preview") preview_output = gr.Textbox(label="Preview", interactive=False) self.preview_manager.update_preview(code_output) generate_button.click( fn=lambda code: self.preview_manager.update_preview(code), inputs=code_output, outputs=preview_output ) logger.info("Launching Gradio interface.") interface.launch(**kwargs) def generate_code(self, description: str, template_choice: Optional[str]) -> str: """Generate code based on the description and selected template.""" template_code = self.template_manager.get_template(template_choice) if template_choice else "" # Get template code if selected logger.info(f"Generating code for description: {description} with template: {template_choice}") return self.rag_system.generate_code(description, template_code) def save_template(self, code: str, name: str, description: str) -> str: """Save the generated code as a template.""" try: components = self._extract_components(code) template = Template(code=code, description=description, components=components) if self.template_manager.save_template(name, template): self.rag_system.add_to_database(code) # Add code to the database logger.info(f"Template '{name}' saved successfully.") return f"✅ Template '{name}' saved successfully." else: logger.error("Failed to save template.") return "❌ Failed to save template." except Exception as e: logger.error(f"Error saving template: {e}") return f"❌ Error saving template: {str(e)}" def main(): logger.info("=== Application Startup ===") try: # Initialize and launch interface interface = GradioInterface() interface.launch( server_port=DEFAULT_PORT, share=False, debug=True ) except Exception as e: logger.error(f"Application error: {e}") raise finally: logger.info("=== Application Shutdown ===") if __name__ == "__main__": main()