Update app.py

app.py

@@ -1,418 +1,246 @@
-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 streamlit as"https://huggingface.co/spaces/acecalisto3/DevToolKit"
PROJECT_ROOT st= "projects"
AGENT_DIRECTORY = "agents"
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
-import black'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': {}
}
-import osAIAgent:
def init(self, name, description, skills):
self.name = name
self.description = description
self.skills = skills
-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. 
-        For now, it provides a simple summary and suggests the next step.
-        """
-        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 information to files."""
-    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)
-
-    # (Optional) Commit and push if you have set up Hugging Face integration.
-    # 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):
-    """Creates an AI agent from the provided text input."""
-    skills = text.split('\n')
-    agent = AIAgent(name, "AI agent created from text input.", skills)
-    save_agent_to_file(agent)
-    return agent.create_agent_prompt()
-
-
-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 
-    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 agent prompt and user input (truncate if necessary)
-    combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:"
-    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 response
-    outputs = model.generate(
-        input_ids, 
-        max_new_tokens=50, 
-        num_return_sequences=1, 
-        do_sample=True,
-        pad_token_id=tokenizer.eos_token_id 
-    )
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return response
-
-
-# Basic chat interface (no agent)
-def chat_interface(input_text):
-    # Load the GPT-2 model 
-    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}"
-
-    # Generate response
-    outputs = generator(input_text, max_new_tokens=50, num_return_sequences=1, do_sample=True)
-    response = outputs[0]['generated_text']
-    return response
-
-
-def workspace_interface(project_name):
-    """Manages project creation."""
-    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
-        # (Optional) Commit and push if you have set up Hugging Face integration.
-        # 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):
-    """Adds code to a file in the specified project."""
-    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}
-        # (Optional) Commit and push if you have set up Hugging Face integration.
-        # 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):
-    """Executes commands in the terminal, optionally within a project's directory."""
-    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)
+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
 
-    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 summarize_text(text):
-    """Summarizes text using a Hugging Face pipeline."""
-    summarizer = pipeline("summarization")
-    summary = summarizer(text, max_length=100, 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):
-    """Analyzes sentiment of text using a Hugging Face pipeline."""
-    analyzer = pipeline("sentiment-analysis")
-    sentiment = analyzer(text)
-    st.session_state.current_state['toolbox']['sentiment'] = sentiment[0]
-    return sentiment[0]
-
-
-def code_editor_interface(code):
-    """Formats and lints Python code."""
-    try:
-        formatted_code = black.format_str(code, mode=black.FileMode())
-        lint_result = StringIO()
-        lint.Run([
-            '--disable=C0114,C0115,C0116', 
-            '--output-format=text',
-            '--reports=n',
-            '-' 
-        ]) 
-        lint_message = lint_result.getvalue()
-        return formatted_code, lint_message
-    except Exception as e:
-        return code, f"Error formatting or linting code: {e}"
-
-
-def translate_code(code, input_language, output_language):
-    """Translates code between programming languages."""
-    try:
-        translator = pipeline("translation", model=f"{input_language}-to-{output_language}")
-        translated_code = translator(code, max_length=10000)[0]['translation_text']
-        st.session_state.current_state['toolbox']['translated_code'] = translated_code
-        return translated_code
-    except Exception as e:
-        return f"Error translating code: {e}"
-
-
-def generate_code(code_idea):
-    """Generates code from a user idea using a Hugging Face pipeline."""
-    try:
-        generator = pipeline('text-generation', model='gpt2') 
-        generated_code = generator(f"```python\n{code_idea}\n```", max_length=1000, num_return_sequences=1)[0][
-            'generated_text']
-
-        # Extract code from the generated text
-        start_index = generated_code.find("```python") + len("```python")
-        end_index = generated_code.find("```", start_index)
-        if start_index != -1 and end_index != -1:
-            generated_code = generated_code[start_index:end_index].strip()
-
-        st.session_state.current_state['toolbox']['generated_code'] = generated_code
-        return generated_code
-    except Exception as e:
-        return f"Error generating code: {e}"
-
-
-def commit_and_push_changes(commit_message):
-    """(Optional) Commits and pushes changes. 
-    Needs to be configured for your 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
 
+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
 
-# --- Streamlit App ---
+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")
-
-#st.sidebar.title("Navigation")
app_mode Sidebar navigation
-if app_mode == "AI Agent Creator":
# AI Agent Creator
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":
-    st.header("Create an AI Agent 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 savedBox":
# successfullyTool Box
st.")
-        st.session_state.available_agents.append(agent_name)
-
-elif app_mode == "Tool Box":
-    st.header("AI-Powered Tools")
-
-    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:] 
-            chat_input = " ".join(chat_input.split(" ")[1:])  
-            chat_response = chat_interface_with_agent(chat_input, agent_name)
-        else:
-            chat_response = chat_interface(chat_input)
-        st.session_state.chat_history.append((chat_input, chat_response))
-        st.write(f"CodeCraft: {chat_response}")
-
-    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")
-
-    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)
-
-    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}")
-
-    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}")
-
-    st.subheader("Translate Code")
-    code_to_translate = st.text_area("Enter code to translate:")
-    source_language = st.selectbox("Source Language", ["en", "fr", "de", "es", "zh", "ja", "ko", "ru"])
-    target_language = st.selectbox("Target Language", ["en", "fr", "de", "es", "zh", "ja", "ko", "ru"])
-    if st.button("Translate Code"):
-        translated_code = translate_code(code_to_translate, source_language, target_language)
-        st.code(translated_code, language=target_language.lower())
-
-    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")
-
-    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":
-    st.header("Workspace Chat App")
-
-    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)
-
-    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)
-
-    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")
-
-    st.subheader("Chat with CodeCraft for Guidance")
-    chat_input = st.text_area("Enter your message for guidance:")
-    if st.button("Get Guidance"):
+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)
+    else:
         chat_response = chat_interface(chat_input)
-        st.session_state.chat_history.append((chat_input, chat_response))
-        st.write(f"CodeCraft: {chat_response}")
-
-    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}")
-
-    st.subheader("Terminal History")
-    for command, output in st.session_state.terminal_history:
-        st.write(f"Command: {command}")
-        st.code(output, language="bash")
-
-    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}")
-
-    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}")
-
-    st.subheader("Automate Build Process")
-    if st.button("Automate"):
-        if selected_agent:
-            agent = AIAgent(selected_agent, "", []) 
-            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)
-        else:
-            st.warning("Please select an AI agent first.")
+    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)