Create supplemental.py

supplemental.py

@@ -0,0 +1,200 @@
+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)