Update app.py

app.py

@@ -1,248 +1,317 @@
-import os
import subprocess
import streamlit as st
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import black
from pylint import lint
from io import StringIO
-HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/DevToolKit"
PROJECT_ROOT = "projects"
AGENT_DIRECTORY = "agents"
-Global state to manage communication between Tool Box and Workspace Chat App
-if 'chat_history' not in st.session_state:
st.session_state.chat_history = []
if 'terminal_history' not in st.session_state:
st.session_state.terminal_history = []
if 'workspace_projects' not in st.session_state:
st.session_state.workspace_projects = {}
if 'available_agents' not in st.session_state:
st.session_state.available_agents = []
if 'current_state' not in st.session_state:
st.session_state.current_state = {
'toolbox': {},
'workspace_chat': {}
}
-class AIAgent:
def init(self, name, description, skills):
self.name = name
self.description = description
self.skills = skills
-
-
-def create_agent_prompt(self):
-    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, workspace_projects):
-    """
-    Autonomous build logic that continues based on the state of chat history and workspace projects.
-    """
-    summary = "Chat History:\n" + "\n".join([f"User: {u}\nAgent: {a}" for u, a in chat_history])
-    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 save_agent_to_file(agent):
"""Saves the agent's prompt to a file locally and then commits to the Hugging Face repository."""
if not os.path.exists(AGENT_DIRECTORY):
os.makedirs(AGENT_DIRECTORY)
file_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}.txt")
config_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}Config.txt")
with open(file_path, "w") as file:
file.write(agent.create_agent_prompt())
with open(config_path, "w") as file:
file.write(f"Agent Name: {agent.name}\nDescription: {agent.description}")
st.session_state.available_agents.append(agent.name)
-
-commit_and_push_changes(f"Add agent {agent.name}")
-def load_agent_prompt(agent_name):
"""Loads an agent prompt from a file."""
file_path = os.path.join(AGENT_DIRECTORY, f"{agent_name}.txt")
if os.path.exists(file_path):
with open(file_path, "r") as file:
agent_prompt = file.read()
return agent_prompt
else:
return None
-def create_agent_from_text(name, text):
skills = text.split('\n')
agent = AIAgent(name, "AI agent created from text input.", skills)
save_agent_to_file(agent)
return agent.create_agent_prompt()
-Chat interface using a selected agent
-def chat_interface_with_agent(input_text, agent_name):
agent_prompt = load_agent_prompt(agent_name)
if agent_prompt is None:
return f"Agent {agent_name} not found."
-
-# Load the GPT-2 model which is compatible with AutoModelForCausalLM
-model_name = "gpt2"
-try:
-    model = AutoModelForCausalLM.from_pretrained(model_name)
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
-except EnvironmentError as e:
-    return f"Error loading model: {e}"
-
-# Combine the agent prompt with user input
-combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:"
-
-# Truncate input text to avoid exceeding the model's maximum length
-max_input_length = 900
-input_ids = tokenizer.encode(combined_input, return_tensors="pt")
-if input_ids.shape[1] > max_input_length:
-    input_ids = input_ids[:, :max_input_length]
-
-# Generate chatbot response
-outputs = model.generate(
-    input_ids, max_new_tokens=50, num_return_sequences=1, do_sample=True, pad_token_id=tokenizer.eos_token_id # Set pad_token_id to eos_token_id
-)
-response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-return response
-def workspace_interface(project_name):
project_path = os.path.join(PROJECT_ROOT, project_name)
if not os.path.exists(PROJECT_ROOT):
os.makedirs(PROJECT_ROOT)
if not os.path.exists(project_path):
os.makedirs(project_path)
st.session_state.workspace_projects[project_name] = {"files": []}
st.session_state.current_state['workspace_chat']['project_name'] = project_name
commit_and_push_changes(f"Create project {project_name}")
return f"Project {project_name} created successfully."
else:
return f"Project {project_name} already exists."
-def add_code_to_workspace(project_name, code, file_name):
project_path = os.path.join(PROJECT_ROOT, project_name)
if os.path.exists(project_path):
file_path = os.path.join(project_path, file_name)
with open(file_path, "w") as file:
file.write(code)
st.session_state.workspace_projects[project_name]["files"].append(file_name)
st.session_state.current_state['workspace_chat']['added_code'] = {"file_name": file_name, "code": code}
commit_and_push_changes(f"Add code to {file_name} in project {project_name}")
return f"Code added to {file_name} in project {project_name} successfully."
else:
return f"Project {project_name} does not exist."
-def terminal_interface(command, project_name=None):
if project_name:
project_path = os.path.join(PROJECT_ROOT, project_name)
if not os.path.exists(project_path):
return f"Project {project_name} does not exist."
result = subprocess.run(command, cwd=project_path, shell=True, capture_output=True, text=True)
else:
result = subprocess.run(command, shell=True, capture_output=True, text=True)
if result.returncode == 0:
st.session_state.current_state['toolbox']['terminal_output'] = result.stdout
return result.stdout
else:
st.session_state.current_state['toolbox']['terminal_output'] = result.stderr
return result.stderr
-def code_editor_interface(code):
try:
formatted_code = black.format_str(code, mode=black.FileMode())
except black.NothingChanged:
formatted_code = code
result = StringIO()
sys.stdout = result
sys.stderr = result
(pylint_stdout, pylint_stderr) = lint.py_run(code, return_std=True)
sys.stdout = sys.stdout
sys.stderr = sys.stderr
lint_message = pylint_stdout.getvalue() + pylint_stderr.getvalue()
st.session_state.current_state['toolbox']['formatted_code'] = formatted_code
st.session_state.current_state['toolbox']['lint_message'] = lint_message
return formatted_code, lint_message
-def summarize_text(text):
summarizer = pipeline("summarization")
summary = summarizer(text, max_length=50, min_length=25, do_sample=False)
st.session_state.current_state['toolbox']['summary'] = summary[0]['summary_text']
return summary[0]['summary_text']
-def sentiment_analysis(text):
analyzer = pipeline("sentiment-analysis")
sentiment = analyzer(text)
st.session_state.current_state['toolbox']['sentiment'] = sentiment[0]
return sentiment[0]
-def translate_code(code, input_language, output_language):
# Define a dictionary to map programming languages to their corresponding file extensions
language_extensions = {
# ignore the specific languages right now, and continue to EOF
}
-
-# Add code to handle edge cases such as invalid input and unsupported programming languages
-if input_language not in language_extensions:
-    raise ValueError(f"Invalid input language: {input_language}")
-if output_language not in language_extensions:
-    raise ValueError(f"Invalid output language: {output_language}")
-
-# Use the dictionary to map the input and output languages to their corresponding file extensions
-input_extension = language_extensions[input_language]
-output_extension = language_extensions[output_language]
-
-# Translate the code using the OpenAI API
-prompt = f"Translate this code from {input_language} to {output_language}:\n\n{code}"
-response = openai.ChatCompletion.create(
-    model="gpt-4",
-    messages=[
-        {"role": "system", "content": "You are an expert software developer."},
-        {"role": "user", "content": prompt}
-    ]
-)
-translated_code = response.choices[0].message['content'].strip()
-
-# Return the translated code
-translated_code = response.choices[0].message['content'].strip()
-st.session_state.current_state['toolbox']['translated_code'] = translated_code
-return translated_code
-def generate_code(code_idea):
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are an expert software developer."},
{"role": "user", "content": f"Generate a Python code snippet for the following idea:\n\n{code_idea}"}
]
)
generated_code = response.choices[0].message['content'].strip()
st.session_state.current_state['toolbox']['generated_code'] = generated_code
return generated_code
-def commit_and_push_changes(commit_message):
"""Commits and pushes changes to the Hugging Face repository."""
commands = [
"git add .",
f"git commit -m '{commit_message}'",
"git push"
]
for command in commands:
result = subprocess.run(command, shell=True, capture_output=True, text=True)
if result.returncode != 0:
st.error(f"Error executing command '{command}': {result.stderr}")
break
-Streamlit App
-st.title("AI Agent Creator")
-Sidebar navigation
-st.sidebar.title("Navigation")
app_mode = st.sidebar.selectbox("Choose the app mode", ["AI Agent Creator", "Tool Box", "Workspace Chat App"])
-if app_mode == "AI Agent Creator":
# AI Agent Creator
st.header("Create an AI Agent from Text")
-
-st.subheader("From Text")
-agent_name = st.text_input("Enter agent name:")
-text_input = st.text_area("Enter skills (one per line):")
-if st.button("Create Agent"):
-    agent_prompt = create_agent_from_text(agent_name, text_input)
-    st.success(f"Agent '{agent_name}' created and saved successfully.")
-    st.session_state.available_agents.append(agent_name)
-elif app_mode == "Tool Box":
# Tool Box
st.header("AI-Powered Tools")
-
-# Chat Interface
-st.subheader("Chat with CodeCraft")
-chat_input = st.text_area("Enter your message:")
-if st.button("Send"):
-    if chat_input.startswith("@"):
-        agent_name = chat_input.split(" ")[0][1:]  # Extract agent_name from @agent_name
-        chat_input = " ".join(chat_input.split(" ")[1:])  # Remove agent_name from input
-        chat_response = chat_interface_with_agent(chat_input, agent_name)
+import os
import subprocess
import streamlit as st
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import black
from pylint import lint
from io import StringIO
+import = "https://huggingface.co/spaces/acecalisto3/DevToolKit"
PROJECT_ROOT = "projects"
AGENT_DIRECTORY = "agents"os
+import subprocess
+from transformers import pipeline, st.session_state:
st.session_state.chat_history = []
if 'terminal_history' not in st.session_state:
st.session_state.terminal_history = []
if 'workspace_projects' not in st.session_state:
st.session_state.workspace_projects = {}
if 'available_agents' not in st.session_state:
st.session_state.available_agents = []
if 'current_state' not in st.session_state:
st.session_state.current_state = {
'toolbox': {}AutoModelForCausalLM,
'workspace_chat': {}
}AutoTokenizer
+import AIAgent:
def init(self, name, description, skills):
self.name = name
self.description = description
self.skills = skillsblack
+from pylint import lint
+from io import StringIO
+# import openai  # We're not using OpenAI anymore
+import sys
+
+# Set your Hugging Face API key is {self.name}. I possess a comprehensive understanding of the following areas:
{skills_str}here
+HF_API_KEY = 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.
"YOUR_HUGGING_FACE_API_KEY""
return agent_prompt
+
+PROJECT_ROOT = "projects"
+AGENT_DIRECTORY = "agents"
+
+# Global state to manage communication between Tool Box and Workspace Chat App
+if 'chat_history' not in st.session_state:
+    st.session_state.chat_history = []
+if 'terminal_history' not in st.session_state:
+    st.session_state.terminal_history = []
+if 'workspace_projects' not in st.session_state:
+    st.session_state.workspace_projects = {}
+if save_agent_to_file(agent):
"""Saves the agent'available_agents' prompt to a file locally and then commits to the Hugging Face repository."""
if not os.path.exists(AGENT_DIRECTORY):
os.makedirs(AGENT_DIRECTORY)
file_pathin = os.path.join(AGENT_DIRECTORY, f"{agent.name}.txt")
config_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}Config.txt")
with open(file_path, "w") as file:
file.write(agent.create_agent_prompt())
with open(config_path, "w") as file:
file.write(f"Agent Name: {agent.name}\nDescription: {agent.description}")
st.session_state:
+    st.session_state.available_agents = []
+
+class AIAgent:
"""Loads an agent prompt from a file."""
file_path = os.path.join(AGENT_DIRECTORY, f"{agent_name}.txt")
if os.path.exists(file_path):
with open(file_path, "r") as file:
agent_prompt = file.read()
return agent_prompt
else:
return None
+    def __init__(self, text):
skills = text.split('\n')
agent = AIAgent(name, description, skills)
save_agent_to_file(agent)
return agent.create_agent_prompt():
+        self.name = name
+        self.description chat_interface_with_agent(input_text, agent_name):
agent_prompt = load_agent_prompt(agent_name)
if agent_prompt is None:
return f"Agent {agent_name} not found."description
+        self.skills = skills
+
+    def create_agent_prompt(self):
+        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, workspace_projects):
+        """
+        Autonomous build logic that continues based on the state of chat history and workspace projects.
+        """
+        # Example logic: Generate a summary of chat history and workspace state
+        summary = "Chat History:\n" + "\n".join([f"User: {u}\nAgent: {a}" for u, a in chat_history])
+        summary += "\n\nWorkspace Projects:\n" + "\n".join([f"{p}: {details}" for p, details in workspace_projects.items()])
+
+        # Example: Generate the next logical step in the project
+        next_step = "Based on the current state, the next logical step is to implement the main application logic."
+
+        return summary, next_step
+
+def save_agent_to_file(agent):
project_path = os.path.join(PROJECT_ROOT, project_name)
if not os.path.exists(PROJECT_ROOT):
os.makedirs(PROJECT_ROOT)
if not os.path.exists(project_path):
os.makedirs(project_path)
st.session_state.workspace_projects[project_name] = {"files": []}
st.session_state.current_state['workspace_chat']['project_name'] = project_name
commit_and_push_changes(f"Create project {project_name}")
return f"Project {project_name} created successfully."
else:
return f"Project {project_name} already exists."
+def add_code_to_workspace(project_name, code, file_name):
project_path = os.path.join(PROJECT_ROOT, project_name)
if os.path.exists(project_path):
file_path = os.path.join(project_path, file_name)
with open(file_path,     """Saves the file:
file.write(code)
st.session_state.workspace_projects[project_name]["files"].append(file_name)
st.session_state.current_state[agent's prompt {"file_name": file_name, "code": code}
commit_and_push_changes(f"Add code to a in project {project_name}")
return f"Code added to {file_name} in project {project_name} successfullyfile."
else:
return f""
+def terminal_interface(command, project_name=None):
    if project_name:
project_path = os.path.join(PROJECT_ROOT, project_name)
if not os.path.exists(AGENT_DIRECTORY):
return f"Project {project_name} does not exist."
result = subprocess.run(command, cwd=project_path, shell=True, capture_output=True, text=True)
else:
result = subprocess.run(command, shell=True, capture_output=True, text=True)
if result.returncode == 0:
st.session_state.current_state['toolbox']['terminal_output'] = result.stdout
return result.stdout
else:
st.session_state.current_state['toolbox']['terminal_output'] = result.stderr
return result.stderr
+def code_editor_interface(code):
try:
formatted_code = black        os.makedirs(AGENT_DIRECTORY))
except black.NothingChanged:
formatted_code = code
result = StringIO()
sys.stdout = result
sys.stderr = result
(pylint_stdout, pylint_stderr) = lint.py_run(code, return_std=True)
sys.stdout = sys.stdout
sys.stderr = sys.stderr
lint_message = pylint_stdout.getvalue() + pylint_stderr.getvalue()
st.session_state.current_state['toolbox']['formatted_code'] = formatted_code
st.session_state.current_state['toolbox']['lint_message'] = lint_message
return formatted_code, lint_message
+    file_path summarize_text(text):
summarizer = os.path.join("summarization")
summary = summarizer(textAGENT_DIRECTORY, max_length=50, min_length=25, do_sample=False)
stf"{agent.name}.current_state['toolbox']['summary'] = summary[0]['summary_text']
return summary[0]['summary_text']txt")
+    with open(text):
analyzerfile_path, "w")
sentiment as analyzer(text)
st.session_state.current_state['toolbox']['sentiment'] = sentiment[0]
return sentiment[0]file:
+        file.write(agent.create_agent_prompt():
# Define a dictionary to map programming languages to their corresponding file extensions
language_extensions = {
# ignore the specific languages right now, and continue to EOF
})
+    st.session_state.available_agents.append(agent.name)
+
+def load_agent_prompt(agent_name):
+    """Loads an agent prompt from a file."""
+    file_path = os.path.join(AGENT_DIRECTORY, f"{agent_name}.txt")
+    if os.path.exists(file_path):
+        with open(file_path, "r") as file:
+            agent_prompt = file.read()
+        return agent_prompt
+    else:
+        return None
+
+def create_agent_from_text(name, text):
+    skills = text.split('\n')
+    agent = AIAgent(name, "AI agent created from text input.", skills)
+    save_agent_to_file(agent)
+    return agent.create_agent_prompt()
+
+# Chat interface using a selected agent (Hugging Face version)
+def chat_interface_with_agent(input_text, agent_name):
+    agent_prompt = load_agent_prompt(agent_name)
+    if agent_prompt is None:
+        return f"Agent {agent_name} not found."
+
+    # Use a Hugging Face model for chat with the agent
+    # Example: Using the 'google/flan-t5-xl' model
+    generator generate_code(code_idea):
response = pipeline(
model="text-generation",
messages model=[
{"google/flan-t5-xl", "content": "You are an expert software developer."},
{"role": "user", "content": f"Generate a Python code snippet for the following idea:\n\n{code_idea}"}
]
)
generated_code use_auth_token=HF_API_KEY)
st.session_state.current_state['toolbox']['generated_code'] = generated_code
return generated_code
+def commit_and_push_changes(commit_message):
"""Commits and pushes changes to the Hugging Face repository."""
commands = [
"git add .",
f"git commit -m '{commit_message}'",
"git push"
]
for command in commands:
result = subprocess.run(command, shell=True, capture_output=True, text=True)
if result.returncode != 0:
st.error(f"Error executing command '{command}': {result.stderr}")
break
+    # Combine the agent prompt with user input
+    combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:"
+
+st.sidebar.title("Navigation")
app_mode    # Generate chatbot response
+    response = == "AI Agent Creator":
# AI Agent Creator
st.headergenerator(combined_input, max_length=150, num_return_sequences=1, do_sample=True)
+    return response[0]['generated_text']
+
+# Terminal interface
+def terminal_interface(command, project_name=None):
+    if project_name:
+        project_path = os.path.join(PROJECT_ROOT, project_name)
+        result = subprocess.run(command, shell=True, capture_output=True, text=True, cwd=project_path)
+    else:
+        result = subprocess.run(command, shell=True, Box":
#capture_output=True, Tool Box
st.header("AI-Powered Tools"text=True)
+    return result.stdout
+
+# Code editor interface
+def code_editor_interface(code):
+    formatted_code = black.format_str(code, mode=black.FileMode())
+    pylint_output = lint.Run([formatted_code], do_exit=False)
+    pylint_output_str = StringIO()
+    pylint_output.linter.reporter.write_messages(pylint_output_str)
+    return formatted_code, pylint_output_str.getvalue()
+
+# Text summarization tool
+def summarize_text(text):
+    summarizer = pipeline("summarization", model="facebook/bart-large-cnn", use_auth_token=HF_API_KEY)
+    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
+    return summary[0]['summary_text']
+
+# Sentiment analysis tool
+def sentiment_analysis(text):
+    analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-3-literal", use_auth_token=HF_API_KEY)
+    result = analyzer(text)
+    return result[0]['label']
+
+# Text translation tool (code translation)
+def translate_code(code, source_language, target_language):
+    # Placeholder for translation logic
+    return f"Translated {source_language} code to {target_language}."
+
+# Code generation tool
+def generate_code(idea):
+    # Use a Hugging Face model for code generation
+    # Example: Using the 'Salesforce/codet5-base' model
+    generator = pipeline("text-generation", model="Salesforce/codet5-base", use_auth_token=HF_API_KEY)
+    response = generator(idea, max_length=150, num_return_sequences=1, do_sample=True)
+    return response[0]['generated_text']
+
+# Workspace interface
+def workspace_interface(project_name):
+    project_path = os.path.join(PROJECT_ROOT, project_name)
+    if not os.path.exists(project_path):
+        os.makedirs(project_path)
+        st.session_state.workspace_projects[project_name] = {'files': []}
+        return f"Project '{project_name}' created successfully."
     else:
+        return f"Project '{project_name}' already exists."
+
+# Add code to workspace
+def add_code_to_workspace(project_name, code, file_name):
+    project_path = os.path.join(PROJECT_ROOT, project_name)
+    if not os.path.exists(project_path):
+        return f"Project '{project_name}' does not exist."
+    
+    file_path = os.path.join(project_path, file_name)
+    with open(file_path, "w") as file:
+        file.write(code)
+    st.session_state.workspace_projects[project_name]['files'].append(file_name)
+    return f"Code added to '{file_name}' in project '{project_name}'."
+
+# Chat interface (Hugging Face version)
+def chat_interface(input_text):
+    # Use a Hugging Face model for chat
+    # Example: Using the 'google/flan-t5-xl' model
+    generator = pipeline("text-generation", model="google/flan-t5-xl", use_auth_token=HF_API_KEY)
+    response = generator(input_text, max_length=150, num_return_sequences=1, do_sample=True)
+    return response[0]['generated_text']
+
+# Streamlit App
+st.title("AI Agent Creator")
+
+# Sidebar navigation
+st.sidebar.title("Navigation")
+app_mode = st.sidebar.selectbox("Choose the app mode", ["AI Agent Creator", "Tool Box", "Workspace Chat App"])
+
+if app_mode == "AI Agent Creator":
+    # AI Agent Creator
+    st.header("Create an AI Agent from Text")
+
+    st.subheader("From Text")
+    agent_name = st.text_input("Enter agent name:")
+    text_input = st.text_area("Enter skills (one per line):")
+    if st.button("Create Agent"):
+        agent_prompt = create_agent_from_text(agent_name, text_input)
+        st.success(f"Agent '{agent_name}' created and saved successfully.")
+        st.session_state.available_agents.append(agent_name)
+
+elif app_mode == "Tool Box":
+    # Tool Box
+    st.header("AI-Powered Tools")
+
+    # Chat Interface
+    st.subheader("Chat with CodeCraft")
+    chat_input = st.text_area("Enter your message:")
+    if st.button("Send"):
+        chat_response = chat_interface(chat_input)
+        st.session_state.chat_history.append((chat_input, chat_response))
+        st.write(f"CodeCraft: {chat_response}")
+
+    # Terminal Interface
+    st.subheader("Terminal")
+    terminal_input = st.text_input("Enter a command:")
+    if st.button("Run"):
+        terminal_output = terminal_interface(terminal_input)
+        st.session_state.terminal_history.append((terminal_input, terminal_output))
+        st.code(terminal_output, language="bash")
+
+    # Code Editor Interface
+    st.subheader("Code Editor")
+    code_editor = st.text_area("Write your code:", height=300)
+    if st.button("Format & Lint"):
+        formatted_code, lint_message = code_editor_interface(code_editor)
+        st.code(formatted_code, language="python")
+        st.info(lint_message)
+
+    # Text Summarization Tool
+    st.subheader("Summarize Text")
+    text_to_summarize = st.text_area("Enter text to summarize:")
+    if st.button("Summarize"):
+        summary = summarize_text(text_to_summarize)
+        st.write(f"Summary: {summary}")
+
+    # Sentiment Analysis Tool
+    st.subheader("Sentiment Analysis")
+    sentiment_text = st.text_area("Enter text for sentiment analysis:")
+    if st.button("Analyze Sentiment"):
+        sentiment = sentiment_analysis(sentiment_text)
+        st.write(f"Sentiment: {sentiment}")
+
+    # Text Translation Tool (Code Translation)
+    st.subheader("Translate Code")
+    code_to_translate = st.text_area("Enter code to translate:")
+    source_language = st.text_input("Enter source language (e.g., 'Python'):")
+    target_language = st.text_input("Enter target language (e.g., 'JavaScript'):")
+    if st.button("Translate Code"):
+        translated_code = translate_code(code_to_translate, source_language, target_language)
+        st.code(translated_code, language=target_language.lower())
+
+    # Code Generation
+    st.subheader("Code Generation")
+    code_idea = st.text_input("Enter your code idea:")
+    if st.button("Generate Code"):
+        generated_code = generate_code(code_idea)
+        st.code(generated_code, language="python")
+
+elif app_mode == "Workspace Chat App":
+    # Workspace Chat App
+    st.header("Workspace Chat App")
+
+    # Project Workspace Creation
+    st.subheader("Create a New Project")
+    project_name = st.text_input("Enter project name:")
+    if st.button("Create Project"):
+        workspace_status = workspace_interface(project_name)
+        st.success(workspace_status)
+
+    # Add Code to Workspace
+    st.subheader("Add Code to Workspace")
+    code_to_add = st.text_area("Enter code to add to workspace:")
+    file_name = st.text_input("Enter file name (e.g., 'app.py'):")
+    if st.button("Add Code"):
+        add_code_status = add_code_to_workspace(project_name, code_to_add, file_name)
+        st.success(add_code_status)
+
+    # Terminal Interface with Project Context
+    st.subheader("Terminal (Workspace Context)")
+    terminal_input = st.text_input("Enter a command within the workspace:")
+    if st.button("Run Command"):
+        terminal_output = terminal_interface(terminal_input, project_name)
+        st.code(terminal_output, language="bash")
+
+    # Chat Interface for Guidance
+    st.subheader("Chat with CodeCraft for Guidance")
+    chat_input = st.text_area("Enter your message for guidance:")
+    if st.button("Get Guidance"):
         chat_response = chat_interface(chat_input)
-    st.session_state.chat_history.append((chat_input, chat_response))
-    st.write(f"CodeCraft: {chat_response}")
-
-# Terminal Interface
-st.subheader("Terminal")
-terminal_input = st.text_input("Enter a command:")
-if st.button("Run"):
-    terminal_output = terminal_interface(terminal_input)
-    st.session_state.terminal_history.append((terminal_input, terminal_output))
-    st.code(terminal_output, language="bash")
-
-# Code Editor Interface
-st.subheader("Code Editor")
-code_editor = st.text_area("Write your code:", height=300)
-if st.button("Format & Lint"):
-    formatted_code, lint_message = code_editor_interface(code_editor)
-    st.code(formatted_code, language="python")
-    st.info(lint_message)
-
-# Text Summarization Tool
-st.subheader("Summarize Text")
-text_to_summarize = st.text_area("Enter text to summarize:")
-if st.button("Summarize"):
-    summary = summarize_text(text_to_summarize)
-    st.write(f"Summary: {summary}")
-
-# Sentiment Analysis Tool
-st.subheader("Sentiment Analysis")
-sentiment_text = st.text_area("Enter text for sentiment analysis:")
-if st.button("Analyze Sentiment"):
-    sentiment = sentiment_analysis(sentiment_text)
-    st.write(f"Sentiment: {sentiment}")
-
-# Text Translation Tool (Code Translation)
-st.subheader("Translate Code")
-code_to_translate = st.text_area("Enter code to translate:")
-source_language = st.text_input("Enter source language (e.g. 'Python'):")
-target_language = st.text_input("Enter target language (e.g. 'JavaScript'):")
-if st.button("Translate Code"):
-    translated_code = translate_code(code_to_translate, source_language, target_language)
-    st.code(translated_code, language=target_language.lower())
-
-# Code Generation
-st.subheader("Code Generation")
-code_idea = st.text_input("Enter your code idea:")
-if st.button("Generate Code"):
-    generated_code = generate_code(code_idea)
-    st.code(generated_code, language="python")
-
-# Display Preset Commands
-st.subheader("Preset Commands")
-preset_commands = {
-    "Create a new project": "create_project('project_name')",
-    "Add code to workspace": "add_code_to_workspace('project_name', 'code', 'file_name')",
-    "Run terminal command": "terminal_interface('command', 'project_name')",
-    "Generate code": "generate_code('code_idea')",
-    "Summarize text": "summarize_text('text')",
-    "Analyze sentiment": "sentiment_analysis('text')",
-    "Translate code": "translate_code('code', 'source_language', 'target_language')",
-}
-for command_name, command in preset_commands.items():
-    st.write(f"{command_name}: `{command}`")
-elif app_mode == "Workspace Chat App":
# Workspace Chat App
st.header("Workspace Chat App")
-
-# Project Workspace Creation
-st.subheader("Create a New Project")
-project_name = st.text_input("Enter project name:")
-if st.button("Create Project"):
-    workspace_status = workspace_interface(project_name)
-    st.success(workspace_status)
-
-# Add Code to Workspace
-st.subheader("Add Code to Workspace")
-code_to_add = st.text_area("Enter code to add to workspace:")
-file_name = st.text_input("Enter file name (e.g. 'app.py'):")
-if st.button("Add Code"):
-    add_code_status = add_code_to_workspace(project_name, code_to_add, file_name)
-    st.success(add_code_status)
-
-# Terminal Interface with Project Context
-st.subheader("Terminal (Workspace Context)")
-terminal_input = st.text_input("Enter a command within the workspace:")
-if st.button("Run Command"):
-    terminal_output = terminal_interface(terminal_input, project_name)
-    st.code(terminal_output, language="bash")
-
-# Chat Interface for Guidance
-st.subheader("Chat with CodeCraft for Guidance")
-chat_input = st.text_area("Enter your message for guidance:")
-if st.button("Get Guidance"):
-    chat_response = chat_interface(chat_input)
-    st.session_state.chat_history.append((chat_input, chat_response))
-    st.write(f"CodeCraft: {chat_response}")
-
-# Display Chat History
-st.subheader("Chat History")
-for user_input, response in st.session_state.chat_history:
-    st.write(f"User: {user_input}")
-    st.write(f"CodeCraft: {response}")
-
-# Display Terminal History
-st.subheader("Terminal History")
-for command, output in st.session_state.terminal_history:
-    st.write(f"Command: {command}")
-    st.code(output, language="bash")
-
-# Display Projects and Files
-st.subheader("Workspace Projects")
-for project, details in st.session_state.workspace_projects.items():
-    st.write(f"Project: {project}")
-    for file in details['files']:
-        st.write(f"  - {file}")
-
-# Chat with AI Agents
-st.subheader("Chat with AI Agents")
-selected_agent = st.selectbox("Select an AI agent", st.session_state.available_agents)
-agent_chat_input = st.text_area("Enter your message for the agent:")
-if st.button("Send to Agent"):
-    agent_chat_response = chat_interface_with_agent(agent_chat_input, selected_agent)
-    st.session_state.chat_history.append((agent_chat_input, agent_chat_response))
-    st.write(f"{selected_agent}: {agent_chat_response}")
-
-# Automate Build Process
-st.subheader("Automate Build Process")
-if st.button("Automate"):
-    agent = AIAgent(selected_agent, "", [])  # Load the agent without skills for now
-    summary, next_step = agent.autonomous_build(st.session_state.chat_history, st.session_state.workspace_projects)
-    st.write("Autonomous Build Summary:")
-    st.write(summary)
-    st.write("Next Step:")
-    st.write(next_step)
-Display current state for debugging
-st.sidebar.subheader("Current State")
st.sidebar.json(st.session_state.current_state)
+        st.session_state.chat_history.append((chat_input, chat_response))
+        st.write(f"CodeCraft: {chat_response}")
+
+    # Display Chat History
+    st.subheader("Chat History")
+    for user_input, response in st.session_state.chat_history:
+        st.write(f"User: {user_input}")
+        st.write(f"CodeCraft: {response}")
+
+    # Display Terminal History
+    st.subheader("Terminal History")
+    for command, output in st.session_state.terminal_history:
+        st.write(f"Command: {command}")
+        st.code(output, language="bash")
+
+    # Display Projects and Files
+    st.subheader("Workspace Projects")
+    for project, details in st.session_state.workspace_projects.items():
+        st.write(f"Project: {project}")
+        for file in details['files']:
+            st.write(f"  - {file}")
+
+    # Chat with AI Agents
+    st.subheader("Chat with AI Agents")
+    selected_agent = st.selectbox("Select an AI agent", st.session_state.available_agents)
+    agent_chat_input = st.text_area("Enter your message for the agent:")
+    if st.button("Send to Agent"):
+        agent_chat_response = chat_interface_with_agent(agent_chat_input, selected_agent)
+        st.session_state.chat_history.append((agent_chat_input, agent_chat_response))
+        st.write(f"{selected_agent}: {agent_chat_response}")
+
+    # Automate Build Process
+    st.subheader("Automate Build Process")
+    if st.button("Automate"):
+        agent = AIAgent(selected_agent, "", [])  # Load the agent without skills for now
+        summary, next_step = agent.autonomous_build(st.session_state.chat_history, st.session_state.workspace_projects)
+        st.write("Autonomous Build Summary:")
+        st.write(summary)
+        st.write("Next Step:")
+        st.write(next_step)