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import os
import json
import logging
from typing import List, Dict, Tuple, Optional, Any
from dataclasses import dataclass
from abc import ABC, abstractmethod
from huggingface_hub import HfApi, InferenceApi
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
@dataclass
class ProjectConfig:
name: str
description: str
technologies: List[str]
structure: Dict[str, List[str]]
class WebDevelopmentTool(ABC):
def __init__(self, name: str, description: str):
self.name = name
self.description = description
@abstractmethod
def generate_code(self, *args, **kwargs):
pass
class HTMLGenerator(WebDevelopmentTool):
def __init__(self):
super().__init__("HTML Generator", "Generates HTML code for web pages")
def generate_code(self, structure: Dict[str, Any]) -> str:
html = "<html><body>"
for tag, content in structure.items():
html += f"<{tag}>{content}</{tag}>"
html += "</body></html>"
return html
class CSSGenerator(WebDevelopmentTool):
def __init__(self):
super().__init__("CSS Generator", "Generates CSS code for styling web pages")
def generate_code(self, styles: Dict[str, Dict[str, str]]) -> str:
css = ""
for selector, properties in styles.items():
css += f"{selector} {{\n"
for prop, value in properties.items():
css += f" {prop}: {value};\n"
css += "}\n"
return css
class JavaScriptGenerator(WebDevelopmentTool):
def __init__(self):
super().__init__("JavaScript Generator", "Generates JavaScript code for web functionality")
def generate_code(self, functions: List[Dict[str, Any]]) -> str:
js = ""
for func in functions:
js += f"function {func['name']}({', '.join(func['params'])}) {{\n"
js += f" {func['body']}\n"
js += "}\n\n"
return js
class EnhancedAIAgent:
def __init__(self, name: str, description: str, skills: List[str], model_name: str):
self.name = name
self.description = description
self.skills = skills
self.model_name = model_name
self.html_gen_tool = HTMLGenerator()
self.css_gen_tool = CSSGenerator()
self.js_gen_tool = JavaScriptGenerator()
self.hf_api = HfApi()
self.inference_api = InferenceApi(repo_id=model_name, token=os.environ.get("HF_API_TOKEN"))
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.text_generation = pipeline("text-generation", model=self.model, tokenizer=self.tokenizer)
self.logger = logging.getLogger(__name__)
def generate_agent_response(self, prompt: str) -> str:
try:
response = self.inference_api(prompt)
return response[0]['generated_text']
except Exception as e:
self.logger.error(f"Error generating response: {str(e)}")
return f"Error: Unable to generate response. {str(e)}"
def create_project_structure(self, project_config: ProjectConfig) -> Dict[str, str]:
project_files = {}
for directory, files in project_config.structure.items():
for file in files:
file_path = os.path.join(directory, file)
if file.endswith('.html'):
content = self.html_gen_tool.generate_code({"body": f"<h1>{project_config.name}</h1>"})
elif file.endswith('.css'):
content = self.css_gen_tool.generate_code({"body": {"font-family": "Arial, sans-serif"}})
elif file.endswith('.js'):
content = self.js_gen_tool.generate_code([{"name": "init", "params": [], "body": "console.log('Initialized');"}])
else:
content = f"// TODO: Implement {file}"
project_files[file_path] = content
return project_files
def generate_project_config(self, project_description: str) -> ProjectConfig:
prompt = f"""
Based on the following project description, generate a ProjectConfig object:
Description: {project_description}
The ProjectConfig should include:
- name: A short, descriptive name for the project
- description: A brief summary of the project
- technologies: A list of technologies to be used (e.g., ["HTML", "CSS", "JavaScript", "React"])
- structure: A dictionary representing the file structure, where keys are directories and values are lists of files
Respond with a JSON object representing the ProjectConfig.
"""
response = self.generate_agent_response(prompt)
config_dict = json.loads(response)
return ProjectConfig(**config_dict)
def implement_feature(self, feature_description: str, existing_code: Optional[str] = None) -> str:
prompt = f"""
Feature to implement: {feature_description}
Existing code:
```
{existing_code if existing_code else 'No existing code provided.'}
```
Please implement the described feature, modifying the existing code if provided.
Respond with only the code, no explanations.
"""
return self.generate_agent_response(prompt)
def review_code(self, code: str) -> str:
prompt = f"""
Please review the following code and provide feedback:
```
{code}
```
Consider the following aspects in your review:
1. Code quality and readability
2. Potential bugs or errors
3. Adherence to best practices
4. Suggestions for improvement
Provide your feedback in a structured format.
"""
return self.generate_agent_response(prompt)
def optimize_code(self, code: str, optimization_goal: str) -> str:
prompt = f"""
Please optimize the following code with the goal of improving {optimization_goal}:
```
{code}
```
Provide only the optimized code in your response, no explanations.
"""
return self.generate_agent_response(prompt)
def generate_documentation(self, code: str) -> str:
prompt = f"""
Please generate comprehensive documentation for the following code:
```
{code}
```
Include the following in your documentation:
1. Overview of the code's purpose
2. Description of functions/classes and their parameters
3. Usage examples
4. Any important notes or considerations
Provide the documentation in Markdown format.
"""
return self.generate_agent_response(prompt)
def suggest_tests(self, code: str) -> str:
prompt = f"""
Please suggest unit tests for the following code:
```
{code}
```
For each function or class, provide:
1. Test case description
2. Input values
3. Expected output or behavior
Provide the suggestions in a structured format.
"""
return self.generate_agent_response(prompt)
def explain_code(self, code: str) -> str:
prompt = f"""
Please provide a detailed explanation of the following code:
```
{code}
```
Include in your explanation:
1. Overall purpose of the code
2. Breakdown of each significant part
3. How different components interact
4. Any notable algorithms or design patterns used
Explain in a way that would be understandable to a junior developer.
"""
return self.generate_agent_response(prompt)
def suggest_refactoring(self, code: str) -> str:
prompt = f"""
Please suggest refactoring improvements for the following code:
```
{code}
```
Consider the following in your suggestions:
1. Improving code readability
2. Enhancing maintainability
3. Applying design patterns where appropriate
4. Optimizing performance (if applicable)
Provide specific suggestions and explain the benefits of each.
"""
return self.generate_agent_response(prompt) |