triAGI-Coder

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acecalisto3 commited on Aug 31, 2024

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e96849b

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1 Parent(s): c0311b6

Update supplemental.py

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supplemental.py +200 -168

supplemental.py CHANGED Viewed

@@ -1,200 +1,232 @@
-from huggingface_hub import HfApi
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from typing import List, Dict
-class Agent:
-    def __init__(self, name, role, tools, knowledge_base=None):
-        self.name = name
-        self.role = role
-        self.tools = tools
-        self.knowledge_base = knowledge_base
-        self.memory = []
-    def act(self, prompt, context):
-        self.memory.append((prompt, context))
-        action = self.choose_action(prompt, context)
-        return action
-    def choose_action(self, prompt, context):
-        # Placeholder for action selection logic
-        return {"tool": "Code Generation", "arguments": {"language": "python", "code": "print('Hello, World!')"}}
-    def observe(self, observation):
-        pass
-    def learn(self, data):
-        pass
-    def __str__(self):
-        return f"""Agent: {self.name} (Role: {self.role})"""
-class Tool:
-    def __init__(self, name, description):
         self.name = name
         self.description = description
-    def run(self, arguments):
-        return {"output": "Tool Output"}
-class CodeGenerationTool(Tool):
     def __init__(self):
-        super().__init__("Code Generation", "Generates code snippets in various languages.")
-    def run(self, arguments):
-        language = arguments.get("language", "python")
-        code = arguments.get("code", "print('Hello, World!')")
-        return {"output": f"""```{language}\n{code}\n```"""}
-class DataRetrievalTool(Tool):
     def __init__(self):
-        super().__init__("Data Retrieval", "Accesses data from APIs, databases, or files.")
-    def run(self, arguments):
-        source = arguments.get("source", "https://example.com/data")
-        return {"output": f"""Data from {source}"""}
-class TextGenerationTool(Tool):
-    def __init__(self):
-        super().__init__("Text Generation", "Generates human-like text based on a given prompt.")
-        self.tokenizer = AutoTokenizer.from_pretrained("bigcode/starcoder")
-        self.model = AutoModelForCausalLM.from_pretrained("bigcode/starcoder")
-    def run(self, arguments):
-        prompt = arguments.get("prompt", "Write a short story about a cat.")
-        inputs = self.tokenizer(prompt, return_tensors="pt")
-        outputs = self.model.generate(**inputs, max_length=50)
-        return {"output": self.tokenizer.decode(outputs[0], skip_special_tokens=True)}
-class CodeExecutionTool(Tool):
-    def __init__(self):
-        super().__init__("Code Execution", "Runs code snippets in various languages.")
-    def run(self, arguments):
-        code = arguments.get("code", "print('Hello, World!')")
-        return {"output": f"""Code executed: {code}"""}
-class CodeDebuggingTool(Tool):
-    def __init__(self):
-        super().__init__("Code Debugging", "Identifies and resolves errors in code snippets.")
-    def run(self, arguments):
-        code = arguments.get("code", "print('Hello, World!')")
-        return {"output": f"""Code debugged: {code}"""}
-class CodeSummarizationTool(Tool):
-    def __init__(self):
-        super().__init__("Code Summarization", "Provides a concise overview of the functionality of a code snippet.")
-    def run(self, arguments):
-        code = arguments.get("code", "print('Hello, World!')")
-        return {"output": f"""Code summary: {code}"""}
-class CodeTranslationTool(Tool):
-    def __init__(self):
-        super().__init__("Code Translation", "Translates code snippets between different programming languages.")
-    def run(self, arguments):
-        code = arguments.get("code", "print('Hello, World!')")
-        return {"output": f"""Translated code: {code}"""}
-class CodeOptimizationTool(Tool):
-    def __init__(self):
-        super().__init__("Code Optimization", "Optimizes code for performance and efficiency.")
-    def run(self, arguments):
-        code = arguments.get("code", "print('Hello, World!')")
-        return {"output": f"""Optimized code: {code}"""}
-class CodeDocumentationTool(Tool):
-    def __init__(self):
-        super().__init__("Code Documentation", "Generates documentation for code snippets.")
-    def run(self, arguments):
-        code = arguments.get("code", "print('Hello, World!')")
-        return {"output": f"""Code documentation: {code}"""}
-class ImageGenerationTool(Tool):
-    def __init__(self):
-        super().__init__("Image Generation", "Generates images based on text descriptions.")
-    def run(self, arguments):
-        description = arguments.get("description", "A cat sitting on a couch")
-        return {"output": f"""Generated image based on: {description}"""}
-class ImageEditingTool(Tool):
-    def __init__(self):
-        super().__init__("Image Editing", "Modifying existing images.")
-    def run(self, arguments):
-        image_path = arguments.get("image_path", "path/to/image.jpg")
-        return {"output": f"""Image edited: {image_path}"""}
-class ImageAnalysisTool(Tool):
-    def __init__(self):
-        super().__init__("Image Analysis", "Extracting information from images, such as objects, scenes, and emotions.")
-    def run(self, arguments):
-        image_path = arguments.get("image_path", "path/to/image.jpg")
-        return {"output": f"""Image analysis results: {image_path}"""}
-class EnhancedAIAgent(Agent):
-    def __init__(self, name: str, description: str, skills: List[str], llm: str):
-        super().__init__(name, description, skills)
-        self.text_gen_tool = TextGenerationTool()
-        self._hf_api = HfApi()  # Initialize HfApi here
-    def generate_agent_response(self, prompt: str) -> str:
-        return self.text_gen_tool.run({"prompt": prompt})
-    def create_agent_prompt(self) -> str:
-        skills_str = '\n'.join([f"* {skill}" for skill in self.skills])
-        agent_prompt = f"""
-As an elite expert developer, my name is {self.name}. I possess a comprehensive understanding of the following areas:
-{skills_str}
-I am confident that I can leverage my expertise to assist you in developing and deploying cutting-edge web applications. Please feel free to ask any questions or present any challenges you may encounter.
 """
-        return agent_prompt
-    def autonomous_build(self, chat_history: List[tuple[str, str]], workspace_projects: Dict[str, Dict],
-                        project_name: str, selected_model: str, hf_token: str) -> tuple[str, str]:
-                summary += "\n\nWorkspace Projects:\n" + "\n".join([f"{p}: {details}" for p, details in workspace_projects.items()])
-        next_step = "Based on the current state, the next logical step is to implement the main application logic."
-        return summary, next_step
-    def deploy_built_space_to_hf(self, project_name: str) -> str:
-        # Assuming you have a function that generates the space content
-        space_content = generate_space_content(project_name)
-        repository = self._hf_api.create_repo(
-            repo_id=project_name,
-            private=True,
-            token=hf_token,
-            exist_ok=True,
-            space_sdk="streamlit"
-        )
-        self._hf_api.upload_file(
-            path_or_fileobj=space_content,
-            path_in_repo="app.py",
-            repo_id=project_name,
-            repo_type="space",
-            token=hf_token
-        )
-        return repository.name
-    def has_valid_hf_token(self) -> bool:
-        return self._hf_api.whoami(token=hf_token) is not None
-def generate_space_content(project_name: str) -> str:
-    # Logic to generate the Streamlit app content based on project_name
-    return f"""
-import streamlit as st
-st.title('{project_name}')
-st.write('Hello, world!')
-"""
-# Example usage within your Streamlit app
-if __name__ == "__main__":
-    llm = "bigcode/starcoder"
-    agent = EnhancedAIAgent(name="AI Assistant", description="Expert in code generation", skills=["Python", "Streamlit"], llm=llm)
-    response = agent.generate_agent_response("How can I help you today?")
-    print(response)

+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)