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Update app.py
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app.py
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import subprocess
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import streamlit as st
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from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
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import black
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from pylint import lint
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from io import StringIO
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HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/DevToolKit"
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PROJECT_ROOT = "projects"
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AGENT_DIRECTORY = "agents"
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@@ -18,11 +23,6 @@ if 'workspace_projects' not in st.session_state:
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st.session_state.workspace_projects = {}
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if 'available_agents' not in st.session_state:
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st.session_state.available_agents = []
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if 'current_state' not in st.session_state:
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st.session_state.current_state = {
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'toolbox': {},
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'workspace_chat': {}
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}
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class AIAgent:
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def __init__(self, name, description, skills):
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@@ -44,27 +44,24 @@ I am confident that I can leverage my expertise to assist you in developing and
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"""
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Autonomous build logic that continues based on the state of chat history and workspace projects.
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"""
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summary = "Chat History:\n" + "\n".join([f"User: {u}\nAgent: {a}" for u, a in chat_history])
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summary += "\n\nWorkspace Projects:\n" + "\n".join([f"{p}: {details}" for p, details in workspace_projects.items()])
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next_step = "Based on the current state, the next logical step is to implement the main application logic."
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return summary, next_step
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def save_agent_to_file(agent):
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"""Saves the agent's prompt to a file
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if not os.path.exists(AGENT_DIRECTORY):
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os.makedirs(AGENT_DIRECTORY)
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file_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}.txt")
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config_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}Config.txt")
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with open(file_path, "w") as file:
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file.write(agent.create_agent_prompt())
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with open(config_path, "w") as file:
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file.write(f"Agent Name: {agent.name}\nDescription: {agent.description}")
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st.session_state.available_agents.append(agent.name)
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commit_and_push_changes(f"Add agent {agent.name}")
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def load_agent_prompt(agent_name):
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"""Loads an agent prompt from a file."""
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file_path = os.path.join(AGENT_DIRECTORY, f"{agent_name}.txt")
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# Generate chatbot response
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outputs = model.generate(
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input_ids, max_new_tokens=50, num_return_sequences=1, do_sample=True
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return response
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project_path = os.path.join(PROJECT_ROOT, project_name)
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if not os.path.exists(PROJECT_ROOT):
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os.makedirs(PROJECT_ROOT)
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if not os.path.exists(project_path):
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os.makedirs(project_path)
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st.session_state.workspace_projects[project_name] = {"files": []}
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st.session_state.current_state['workspace_chat']['project_name'] = project_name
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commit_and_push_changes(f"Create project {project_name}")
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return f"Project {project_name} created successfully."
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else:
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return f"Project {project_name} already exists."
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def add_code_to_workspace(project_name, code, file_name):
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project_path = os.path.join(PROJECT_ROOT, project_name)
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if os.path.exists(project_path):
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file_path = os.path.join(project_path, file_name)
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with open(file_path, "w") as file:
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file.write(code)
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st.session_state.workspace_projects[project_name]["files"].append(file_name)
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st.session_state.current_state['workspace_chat']['added_code'] = {"file_name": file_name, "code": code}
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commit_and_push_changes(f"Add code to {file_name} in project {project_name}")
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return f"Code added to {file_name} in project {project_name} successfully."
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else:
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return f"Project {project_name} does not exist."
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def terminal_interface(command, project_name=None):
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if project_name:
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project_path = os.path.join(PROJECT_ROOT, project_name)
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return f"Project {project_name} does not exist."
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result = subprocess.run(command, cwd=project_path, shell=True, capture_output=True, text=True)
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else:
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result = subprocess.run(command, shell=True, capture_output=True, text=True)
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st.session_state.current_state['toolbox']['terminal_output'] = result.stdout
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return result.stdout
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else:
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st.session_state.current_state['toolbox']['terminal_output'] = result.stderr
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return result.stderr
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# Chat interface using a selected agent
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def chat_interface_with_agent(input_text, agent_name):
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# ... [rest of the chat_interface_with_agent function] ...
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def summarize_text(text):
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summarizer = pipeline("summarization")
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summary = summarizer(text, max_length=
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st.session_state.current_state['toolbox']['summary'] = summary[0]['summary_text']
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return summary[0]['summary_text']
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def sentiment_analysis(text):
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analyzer = pipeline("sentiment-analysis")
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def translate_code(code, input_language, output_language):
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# Define a dictionary to map programming languages to their corresponding file extensions
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language_extensions = {
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}
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# Add code to handle edge cases such as invalid input and unsupported programming languages
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if input_language not in language_extensions:
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raise ValueError(f"Invalid input language: {input_language}")
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if output_language not in language_extensions:
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raise ValueError(f"Invalid output language: {output_language}")
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# Use the dictionary to map the input and output languages to their corresponding file extensions
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input_extension = language_extensions[input_language]
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output_extension = language_extensions[output_language]
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# Translate the code using the OpenAI API
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prompt = f"Translate this code from {input_language} to {output_language}:\n\n{code}"
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response = openai.ChatCompletion.create(
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model="gpt-4",
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messages=[
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{"role": "system", "content": "You are an expert software developer."},
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{"role": "user", "content": prompt}
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]
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)
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)
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st.session_state.current_state['toolbox']['generated_code'] = generated_code
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return generated_code
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def commit_and_push_changes(commit_message):
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"""Commits and pushes changes to the Hugging Face repository."""
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commands = [
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"git add .",
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f"git commit -m '{commit_message}'",
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"git push"
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]
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for command in commands:
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result = subprocess.run(command, shell=True, capture_output=True, text=True)
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if result.returncode != 0:
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st.error(f"Error executing command '{command}': {result.stderr}")
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break
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# Streamlit App
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st.title("AI Agent Creator")
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st.subheader("Chat with CodeCraft")
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chat_input = st.text_area("Enter your message:")
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if st.button("Send"):
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agent_name = chat_input.split(" ")[0][1:] # Extract agent_name from @agent_name
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chat_input = " ".join(chat_input.split(" ")[1:]) # Remove agent_name from input
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chat_response = chat_interface_with_agent(chat_input, agent_name)
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else:
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chat_response = chat_interface(chat_input)
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st.session_state.chat_history.append((chat_input, chat_response))
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st.write(f"CodeCraft: {chat_response}")
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# Text Translation Tool (Code Translation)
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st.subheader("Translate Code")
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code_to_translate = st.text_area("Enter code to translate:")
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source_language = st.text_input("Enter source language (e.g
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target_language = st.text_input("Enter target language (e.g
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if st.button("Translate Code"):
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translated_code = translate_code(code_to_translate, source_language, target_language)
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st.code(translated_code, language=target_language.lower())
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generated_code = generate_code(code_idea)
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st.code(generated_code, language="python")
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# Display Preset Commands
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st.subheader("Preset Commands")
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preset_commands = {
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"Create a new project": "create_project('project_name')",
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"Add code to workspace": "add_code_to_workspace('project_name', 'code', 'file_name')",
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"Run terminal command": "terminal_interface('command', 'project_name')",
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"Generate code": "generate_code('code_idea')",
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"Summarize text": "summarize_text('text')",
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"Analyze sentiment": "sentiment_analysis('text')",
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"Translate code": "translate_code('code', 'source_language', 'target_language')",
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}
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for command_name, command in preset_commands.items():
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st.write(f"{command_name}: `{command}`")
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elif app_mode == "Workspace Chat App":
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# Workspace Chat App
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st.header("Workspace Chat App")
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# Add Code to Workspace
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st.subheader("Add Code to Workspace")
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code_to_add = st.text_area("Enter code to add to workspace:")
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file_name = st.text_input("Enter file name (e.g
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if st.button("Add Code"):
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add_code_status = add_code_to_workspace(project_name, code_to_add, file_name)
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st.success(add_code_status)
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st.write("Autonomous Build Summary:")
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st.write(summary)
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st.write("Next Step:")
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st.write(next_step)
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# Display current state for debugging
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st.sidebar.subheader("Current State")
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st.sidebar.json(st.session_state.current_state)
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import streamlit as st
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import os
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import subprocess
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from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
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import black
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from pylint import lint
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from io import StringIO
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import openai
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import sys
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# Set your OpenAI API key here
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openai.api_key = "YOUR_OPENAI_API_KEY"
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PROJECT_ROOT = "projects"
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AGENT_DIRECTORY = "agents"
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st.session_state.workspace_projects = {}
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if 'available_agents' not in st.session_state:
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st.session_state.available_agents = []
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class AIAgent:
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def __init__(self, name, description, skills):
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"""
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Autonomous build logic that continues based on the state of chat history and workspace projects.
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"""
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# Example logic: Generate a summary of chat history and workspace state
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summary = "Chat History:\n" + "\n".join([f"User: {u}\nAgent: {a}" for u, a in chat_history])
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summary += "\n\nWorkspace Projects:\n" + "\n".join([f"{p}: {details}" for p, details in workspace_projects.items()])
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# Example: Generate the next logical step in the project
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next_step = "Based on the current state, the next logical step is to implement the main application logic."
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return summary, next_step
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def save_agent_to_file(agent):
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"""Saves the agent's prompt to a file."""
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if not os.path.exists(AGENT_DIRECTORY):
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os.makedirs(AGENT_DIRECTORY)
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file_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}.txt")
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with open(file_path, "w") as file:
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file.write(agent.create_agent_prompt())
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st.session_state.available_agents.append(agent.name)
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def load_agent_prompt(agent_name):
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"""Loads an agent prompt from a file."""
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file_path = os.path.join(AGENT_DIRECTORY, f"{agent_name}.txt")
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# Generate chatbot response
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outputs = model.generate(
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input_ids, max_new_tokens=50, num_return_sequences=1, do_sample=True
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return response
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# Terminal interface
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def terminal_interface(command, project_name=None):
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if project_name:
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project_path = os.path.join(PROJECT_ROOT, project_name)
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result = subprocess.run(command, shell=True, capture_output=True, text=True, cwd=project_path)
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else:
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result = subprocess.run(command, shell=True, capture_output=True, text=True)
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return result.stdout
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# Code editor interface
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def code_editor_interface(code):
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formatted_code = black.format_str(code, mode=black.FileMode())
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pylint_output = lint.Run([formatted_code], do_exit=False)
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pylint_output_str = StringIO()
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pylint_output.linter.reporter.write_messages(pylint_output_str)
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return formatted_code, pylint_output_str.getvalue()
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# Text summarization tool
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def summarize_text(text):
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summarizer = pipeline("summarization")
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summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
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return summary[0]['summary_text']
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# Sentiment analysis tool
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def sentiment_analysis(text):
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analyzer = pipeline("sentiment-analysis")
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result = analyzer(text)
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return result[0]['label']
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# Text translation tool (code translation)
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def translate_code(code, source_language, target_language):
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# Placeholder for translation logic
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return f"Translated {source_language} code to {target_language}."
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# Code generation tool
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def generate_code(idea):
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response = openai.Completion.create(
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engine="davinci-codex",
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prompt=idea,
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max_tokens=150
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| 152 |
)
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| 153 |
+
return response.choices[0].text.strip()
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| 154 |
+
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| 155 |
+
# Workspace interface
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| 156 |
+
def workspace_interface(project_name):
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| 157 |
+
project_path = os.path.join(PROJECT_ROOT, project_name)
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| 158 |
+
if not os.path.exists(project_path):
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| 159 |
+
os.makedirs(project_path)
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| 160 |
+
st.session_state.workspace_projects[project_name] = {'files': []}
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| 161 |
+
return f"Project '{project_name}' created successfully."
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| 162 |
+
else:
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| 163 |
+
return f"Project '{project_name}' already exists."
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| 164 |
+
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| 165 |
+
# Add code to workspace
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| 166 |
+
def add_code_to_workspace(project_name, code, file_name):
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| 167 |
+
project_path = os.path.join(PROJECT_ROOT, project_name)
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| 168 |
+
if not os.path.exists(project_path):
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| 169 |
+
return f"Project '{project_name}' does not exist."
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| 170 |
+
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| 171 |
+
file_path = os.path.join(project_path, file_name)
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| 172 |
+
with open(file_path, "w") as file:
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| 173 |
+
file.write(code)
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| 174 |
+
st.session_state.workspace_projects[project_name]['files'].append(file_name)
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| 175 |
+
return f"Code added to '{file_name}' in project '{project_name}'."
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| 176 |
+
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| 177 |
+
# Chat interface
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| 178 |
+
def chat_interface(input_text):
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| 179 |
+
response = openai.Completion.create(
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| 180 |
+
engine="davinci-codex",
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| 181 |
+
prompt=input_text,
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| 182 |
+
max_tokens=150
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| 183 |
)
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| 184 |
+
return response.choices[0].text.strip()
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| 185 |
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| 186 |
# Streamlit App
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| 187 |
st.title("AI Agent Creator")
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| 210 |
st.subheader("Chat with CodeCraft")
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| 211 |
chat_input = st.text_area("Enter your message:")
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| 212 |
if st.button("Send"):
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| 213 |
+
chat_response = chat_interface(chat_input)
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| 214 |
st.session_state.chat_history.append((chat_input, chat_response))
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| 215 |
st.write(f"CodeCraft: {chat_response}")
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| 216 |
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| 247 |
# Text Translation Tool (Code Translation)
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| 248 |
st.subheader("Translate Code")
|
| 249 |
code_to_translate = st.text_area("Enter code to translate:")
|
| 250 |
+
source_language = st.text_input("Enter source language (e.g., 'Python'):")
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| 251 |
+
target_language = st.text_input("Enter target language (e.g., 'JavaScript'):")
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| 252 |
if st.button("Translate Code"):
|
| 253 |
translated_code = translate_code(code_to_translate, source_language, target_language)
|
| 254 |
st.code(translated_code, language=target_language.lower())
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|
| 260 |
generated_code = generate_code(code_idea)
|
| 261 |
st.code(generated_code, language="python")
|
| 262 |
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| 263 |
elif app_mode == "Workspace Chat App":
|
| 264 |
# Workspace Chat App
|
| 265 |
st.header("Workspace Chat App")
|
|
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|
| 274 |
# Add Code to Workspace
|
| 275 |
st.subheader("Add Code to Workspace")
|
| 276 |
code_to_add = st.text_area("Enter code to add to workspace:")
|
| 277 |
+
file_name = st.text_input("Enter file name (e.g., 'app.py'):")
|
| 278 |
if st.button("Add Code"):
|
| 279 |
add_code_status = add_code_to_workspace(project_name, code_to_add, file_name)
|
| 280 |
st.success(add_code_status)
|
|
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|
| 330 |
st.write("Autonomous Build Summary:")
|
| 331 |
st.write(summary)
|
| 332 |
st.write("Next Step:")
|
| 333 |
+
st.write(next_step)
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