from langchain_core.prompts import PromptTemplate from langchain_core.runnables import RunnableSequence from langchain_community.llms import HuggingFaceEndpoint from huggingface_hub.inference_api import InferenceApi as InferenceClient import streamlit as st from prompts import ( ACTION_PROMPT, ADD_PROMPT, COMPRESS_HISTORY_PROMPT, LOG_PROMPT, LOG_RESPONSE, MODIFY_PROMPT, PREFIX, READ_PROMPT, TASK_PROMPT, UNDERSTAND_TEST_RESULTS_PROMPT, WEB_DEV_SYSTEM_PROMPT, AI_SYSTEM_PROMPT, WEB_DEV, PYTHON_CODE_DEV, HUGGINGFACE_FILE_DEV ) from utils import ( parse_action, parse_file_content, read_python_module_structure, extract_imports, # Unused import, consider removing or using get_file, # Unused import, consider removing or using ) # --- Constants --- AGENT_TYPES = [ "Task Executor", "Information Retriever", "Decision Maker", "Data Analyzer", ] TOOL_TYPES = [ "Web Scraper", "Database Connector", "API Caller", "File Handler", "Text Processor", ] VERBOSE = False MAX_HISTORY = 100 MODEL = "mistralai/Mixtral-8x7B-Instruct-v0.1" # Consider using a smaller model # --- Initialize Hugging Face client --- client = InferenceClient(MODEL) # --- Classes --- class Agent: def __init__(self, name: str, agent_type: str, complexity: int): self.name = name self.type = agent_type self.complexity = complexity self.tools: List[Tool] = [] def add_tool(self, tool: "Tool"): self.tools.append(tool) def __str__(self): return f"{self.name} ({self.type}) - Complexity: {self.complexity}" class Tool: def __init__(self, name: str, tool_type: str): self.name = name self.type = tool_type def __str__(self): return f"{self.name} ({self.type})" class Pypelyne: def __init__(self): self.agents: List[Agent] = [] self.tools: List[Tool] = [] self.history: str = "" self.task: str = "" self.purpose: str = "" self.directory: str = "" def add_agent(self, agent: Agent): self.agents.append(agent) def add_tool(self, tool: Tool): self.tools.append(tool) def generate_chat_app(self) -> str: time.sleep(2) # Simulate processing time return f"Chat app generated with {len(self.agents)} agents and {len(self.tools)} tools." def run_gpt( self, prompt_template: str, stop_tokens: List[str], max_tokens: int, **prompt_kwargs ) -> str: content = ( PREFIX.format( module_summary=read_python_module_structure(self.directory)[0], purpose=self.purpose, ) + prompt_template.format(**prompt_kwargs) ) if VERBOSE: print(LOG_PROMPT.format(content)) try: stream = client.text_generation( prompt=content, max_new_tokens=max_tokens, stop_sequences=stop_tokens if stop_tokens else None, do_sample=True, temperature=0.7, ) resp = "".join(token for token in stream) except Exception as e: print(f"Error in run_gpt: {e}") resp = f"Error: {e}" if VERBOSE: print(LOG_RESPONSE.format(resp)) return resp def compress_history(self): resp = self.run_gpt( COMPRESS_HISTORY_PROMPT, stop_tokens=["observation:", "task:", "action:", "thought:"], max_tokens=512, task=self.task, history=self.history, ) self.history = f"observation: {resp}\n" def run_action(self, action_name: str, action_input: str) -> str: if action_name == "COMPLETE": return "Task completed." if len(self.history.split("\n")) > MAX_HISTORY: if VERBOSE: print("COMPRESSING HISTORY") self.compress_history() action_funcs = { "MAIN": self.call_main, "UPDATE-TASK": self.call_set_task, "MODIFY-FILE": self.call_modify, "READ-FILE": self.call_read, "ADD-FILE": self.call_add, "TEST": self.call_test, } if action_name not in action_funcs: return f"Unknown action: {action_name}" print(f"RUN: {action_name} {action_input}") return action_funcs[action_name](action_input) def call_main(self, action_input: str) -> str: resp = self.run_gpt( ACTION_PROMPT, stop_tokens=["observation:", "task:"], max_tokens=256, task=self.task, history=self.history, ) lines = resp.strip().strip("\n").split("\n") for line in lines: if line == "": continue if line.startswith("thought: "): self.history += f"{line}\n" elif line.startswith("action: "): action_name, action_input = parse_action(line) self.history += f"{line}\n" return self.run_action(action_name, action_input) return "No valid action found." def call_set_task(self, action_input: str) -> str: self.task = ( self.run_gpt( TASK_PROMPT, stop_tokens=[], max_tokens=64, task=self.task, history=self.history, ) .strip("\n") .strip() ) self.history += f"observation: task has been updated to: {self.task}\n" return f"Task updated: {self.task}" def call_modify(self, action_input: str) -> str: if not os.path.exists(action_input): self.history += "observation: file does not exist\n" return "File does not exist." content = read_python_module_structure(self.directory)[1] f_content = ( content[action_input] if content[action_input] else "< document is empty >" ) resp = self.run_gpt( MODIFY_PROMPT, stop_tokens=["action:", "thought:", "observation:"], max_tokens=2048, task=self.task, history=self.history, file_path=action_input, file_contents=f_content, ) new_contents, description = parse_file_content(resp) if new_contents is None: self.history += "observation: failed to modify file\n" return "Failed to modify file." with open(action_input, "w") as f: f.write(new_contents) self.history += f"observation: file successfully modified\n" self.history += f"observation: {description}\n" return f"File modified: {action_input}" def call_read(self, action_input: str) -> str: if not os.path.exists(action_input): self.history += "observation: file does not exist\n" return "File does not exist." content = read_python_module_structure(self.directory)[1] f_content = ( content[action_input] if content[action_input] else "< document is empty >" ) resp = self.run_gpt( READ_PROMPT, stop_tokens=[], max_tokens=256, task=self.task, history=self.history, file_path=action_input, file_contents=f_content, ).strip("\n") self.history += f"observation: {resp}\n" return f"File read: {action_input}" def call_add(self, action_input: str) -> str: d = os.path.dirname(action_input) if not d.startswith(self.directory): self.history += ( f"observation: files must be under directory {self.directory}\n" ) return f"Invalid directory: {d}" elif not action_input.endswith(".py"): self.history += "observation: can only write .py files\n" return "Only .py files are allowed." else: if d and not os.path.exists(d): os.makedirs(d) if not os.path.exists(action_input): resp = self.run_gpt( ADD_PROMPT, stop_tokens=["action:", "thought:", "observation:"], max_tokens=2048, task=self.task, history=self.history, file_path=action_input, ) new_contents, description = parse_file_content(resp) if new_contents is None: self.history += "observation: failed to write file\n" return "Failed to write file." with open(action_input, "w") as f: f.write(new_contents) self.history += "observation: file successfully written\n" self.history += f"observation: {description}\n" return f"File added: {action_input}" else: self.history += "observation: file already exists\n" return "File already exists." def call_test(self, action_input: str) -> str: result = subprocess.run( ["python", "-m", "pytest", "--collect-only", self.directory], capture_output=True, text=True, ) if result.returncode != 0: self.history += f"observation: there are no tests! Test should be written in a test folder under {self.directory}\n" return "No tests found." result = subprocess.run( ["python", "-m", "pytest", self.directory], capture_output=True, text=True, ) if result.returncode == 0: self.history += "observation: tests pass\n" return "All tests passed." resp = self.run_gpt( UNDERSTAND_TEST_RESULTS_PROMPT, stop_tokens=[], max_tokens=256, task=self.task, history=self.history, stdout=result.stdout[:5000], stderr=result.stderr[:5000], ) self.history += f"observation: tests failed: {resp}\n" return f"Tests failed: {resp}" # --- Global Pypelyne Instance --- pypelyne = Pypelyne() # --- Helper Functions --- def create_agent(name: str, agent_type: str, complexity: int) -> Agent: agent = Agent(name, agent_type, complexity) pypelyne.add_agent(agent) return agent def create_tool(name: str, tool_type: str) -> Tool: tool = Tool(name, tool_type) pypelyne.add_tool(tool) return tool # --- Streamlit App Code --- def main(): st.title("🧠 Pypelyne: Your AI-Powered Coding Assistant") # --- Sidebar --- st.sidebar.title("⚙️ Settings") if "directory" not in st.session_state: st.session_state.directory = "." pypelyne.directory = st.sidebar.text_input( "Project Directory:", value=st.session_state.directory, help="Path to your coding project", ) st.session_state.directory = pypelyne.directory # Update session state if "purpose" not in st.session_state: st.session_state.purpose = "" pypelyne.purpose = st.sidebar.text_area( "Project Purpose:", value=st.session_state.purpose, help="Describe the purpose of your coding project.", ) st.session_state.purpose = pypelyne.purpose # Update session state # --- Agent and Tool Management --- st.sidebar.header("🤖 Agents") if "agents" not in st.session_state: st.session_state.agents = [] show_agent_creation = st.sidebar.expander( "Create New Agent", expanded=False ) with show_agent_creation: agent_name = st.text_input("Agent Name:") agent_type = st.selectbox("Agent Type:", AGENT_TYPES) agent_complexity = st.slider("Complexity (1-5):", 1, 5, 3) if st.button("Add Agent"): create_agent(agent_name, agent_type, agent_complexity) st.session_state.agents = pypelyne.agents # Update session state st.sidebar.header("🛠️ Tools") if "tools" not in st.session_state: st.session_state.tools = [] show_tool_creation = st.sidebar.expander("Create New Tool", expanded=False) with show_tool_creation: tool_name = st.text_input("Tool Name:") tool_type = st.selectbox("Tool Type:", TOOL_TYPES) if st.button("Add Tool"): create_tool(tool_name, tool_type) st.session_state.tools = pypelyne.tools # Update session state # --- Display Agents and Tools --- st.sidebar.subheader("Active Agents:") for agent in st.session_state.agents: st.sidebar.write(f"- {agent}") st.sidebar.subheader("Available Tools:") for tool in st.session_state.tools: st.sidebar.write(f"- {tool}") # --- Main Content Area --- st.header("💻 Code Interaction") if "task" not in st.session_state: st.session_state.task = "" task_input = st.text_area( "🎯 Task:", value=st.session_state.task, help="Describe the coding task you want to perform.", ) if task_input: pypelyne.task = task_input st.session_state.task = pypelyne.task # Update session state user_input = st.text_input( "💬 Your Input:", help="Provide instructions or ask questions." ) if st.button("Execute"): if user_input: with st.spinner("Pypelyne is working..."): response = pypelyne.run_action("MAIN", user_input) st.write("Pypelyne Says: ", response) # --- Run the Streamlit app --- if __name__ == "__main__": main()