Update app.py

app.py

@@ -1,330 +1,354 @@
 import os
 import subprocess
-import random
-from huggingface_hub import InferenceClient
-import gradio as gr
-from safe_search import safe_search
-from i_search import google
-from i_search import i_search as i_s
-from datetime import datetime
-import logging
-import json
-import nltk
-from transformers import pipeline
-
-# Ensure NLTK data is downloaded
-nltk.download('punkt')
-
-now = datetime.now()
-date_time_str = now.strftime("%Y-%m-%d %H:%M:%S")
-
-client = InferenceClient(
-    "mistralai/Mixtral-8x7B-Instruct-v0.1"
-)
-
-# --- Set up logging ---
-logging.basicConfig(
-    filename="app.log",  # Name of the log file
-    level=logging.INFO,  # Set the logging level (INFO, DEBUG, etc.)
-    format="%(asctime)s - %(levelname)s - %(message)s",
-)
-
-agents = [
-    "WEB_DEV",
-    "AI_SYSTEM_PROMPT",
-    "PYTHON_CODE_DEV"
-]
-
-VERBOSE = True
-MAX_HISTORY = 5
-
-PREFIX = """
-{date_time_str}
-Purpose: {purpose}
-Safe Search: {safe_search}
-"""
-
-LOG_PROMPT = """
-PROMPT: {content}
-"""
-
-LOG_RESPONSE = """
-RESPONSE: {resp}
-"""
-
-COMPRESS_HISTORY_PROMPT = """
-You are a helpful AI assistant. Your task is to compress the following history into a summary that is no longer than 512 tokens.
-History:
-{history}
-"""
-
-ACTION_PROMPT = """
-You are a helpful AI assistant. You are working on the task: {task}
-Your current history is:
-{history}
-What is your next thought?
-thought: 
-What is your next action?
-action: 
-"""
-
-TASK_PROMPT = """
-You are a helpful AI assistant. Your current history is:
-{history}
-What is the next task?
-task: 
-"""
-
-UNDERSTAND_TEST_RESULTS_PROMPT = """
-You are a helpful AI assistant. The test results are:
-{test_results}
-What do you want to know about the test results?
-thought: 
-"""
-
-def format_prompt(message, history, max_history_turns=5):
-    prompt = "<s>"
-    # Keep only the last 'max_history_turns' turns
-    for user_prompt, bot_response in history[-max_history_turns:]:
-        prompt += f"[INST] {user_prompt} [/INST]"
-        prompt += f" {bot_response}</s> "
-    prompt += f"[INST] {message} [/INST]"
-    return prompt
-
-def run_gpt(
-    prompt_template,
-    stop_tokens,
-    max_tokens,
-    purpose,
-    **prompt_kwargs,
-):
-    seed = random.randint(1,1111111111111111)
-    logging.info(f"Seed: {seed}")  # Log the seed
-    
-    content = PREFIX.format(
-        date_time_str=date_time_str,
-        purpose=purpose,
-        safe_search=safe_search,
-    ) + prompt_template.format(**prompt_kwargs)
-    if VERBOSE:
-        logging.info(LOG_PROMPT.format(content))  # Log the prompt
-    
-    resp = client.text_generation(content, max_new_tokens=max_tokens, stop_sequences=stop_tokens, temperature=0.7, top_p=0.8, repetition_penalty=1.5)
-    if VERBOSE:
-        logging.info(LOG_RESPONSE.format(resp=resp))  # Log the response
-    return resp
-
-def generate(
-    prompt, history, agent_name=agents[0], sys_prompt="", temperature=0.7, max_new_tokens=2048, top_p=0.8, repetition_penalty=1.5, model="mistralai/Mixtral-8x7B-Instruct-v0.1"
-):
-    seed = random.randint(1,1111111111111111)
-
-    if agent_name == "WEB_DEV":
-        agent = "You are a helpful AI assistant. You are a web developer."
-    elif agent_name == "AI_SYSTEM_PROMPT":
-        agent = "You are a helpful AI assistant. You are an AI system."
-    elif agent_name == "PYTHON_CODE_DEV":
-        agent = "You are a helpful AI assistant. You are a Python code developer."
+import streamlit as st
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+import openai
+
+# Constants
+HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/DevToolKit"
+PROJECT_ROOT = "projects"
+AGENT_DIRECTORY = "agents"
+
+# Initialize session state
+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': {}
+    }
+
+# AI Agent class
+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):
+        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
+
+# Functions for agent management
+def save_agent_to_file(agent):
+    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):
+    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:
-        agent = "You are a helpful AI assistant."
-
-    system_prompt = agent
-    temperature = float(temperature)
-    if temperature < 1e-2:
-        temperature = 1e-2
-    top_p = float(top_p)
-
-    formatted_prompt = f"{system_prompt} {prompt}"
-    formatted_prompt = format_prompt(formatted_prompt, history, max_history_turns=5)  # Truncated history
-    logging.info(f"Formatted Prompt: {formatted_prompt}")
-    stream = client.text_generation(formatted_prompt, temperature=temperature, max_new_tokens=max_new_tokens, top_p=top_p, repetition_penalty=repetition_penalty, stream=True, details=True, return_full_text=False)
-    resp = ""
-    for response in stream:
-        resp += response.token.text
-
-    if VERBOSE:
-        logging.info(LOG_RESPONSE.format(resp=resp))  # Log the response
-    return resp
-
-def compress_history(purpose, task, history, directory):
-    resp = run_gpt(
-        COMPRESS_HISTORY_PROMPT,
-        stop_tokens=["observation:", "task:", "action:", "thought:"],
-        max_tokens=512,
-        purpose=purpose,
-        task=task,
-        history=history,
-    )
-    history = "observation: {}\n".format(resp)
-    return history
-    
-def call_search(purpose, task, history, directory, action_input):
-    logging.info(f"CALLING SEARCH: {action_input}")
-    try:
-        if "http" in action_input:
-            if "<" in action_input:
-                action_input = action_input.strip("<")
-            if ">" in action_input:
-                action_input = action_input.strip(">")
-            
-            response = i_s(action_input)
-            logging.info(f"Search Result: {response}")
-            history += "observation: search result is: {}\n".format(response)
-        else:
-            history += "observation: I need to provide a valid URL to 'action: SEARCH action_input=https://URL'\n"
-    except Exception as e:
-        history += "observation: {}'\n".format(e)
-    return "MAIN", None, history, task
-
-def call_main(purpose, task, history, directory, action_input):
-    logging.info(f"CALLING MAIN: {action_input}")
-    resp = run_gpt(
-        ACTION_PROMPT,
-        stop_tokens=["observation:", "task:", "action:","thought:"],
-        max_tokens=32000,
-        purpose=purpose,
-        task=task,
-        history=history,
-    )
-    lines = resp.strip().strip("\n").split("\n")
-    for line in lines:
-        if line == "":
-            continue
-        if line.startswith("thought: "):
-            history += "{}\n".format(line)
-            logging.info(f"Thought: {line}")
-        elif line.startswith("action: "):
-            action_name, action_input = parse_action(line)
-            logging.info(f"Action: {action_name} - {action_input}")
-            history += "{}\n".format(line)
-            if "COMPLETE" in action_name or "COMPLETE" in action_input:
-                task = "END"
-                return action_name, action_input, history, task
-            else:
-                return action_name, action_input, history, task
-        else:
-            history += "{}\n".format(line)
-            logging.info(f"Other Output: {line}")
-    return "MAIN", None, history, task
-
-def call_set_task(purpose, task, history, directory, action_input):
-    logging.info(f"CALLING SET_TASK: {action_input}")
-    task = run_gpt(
-        TASK_PROMPT,
-        stop_tokens=[],
-        max_tokens=64,
-        purpose=purpose,
-        task=task,
-        history=history,
-    ).strip("\n")
-    history += "observation: task has been updated to: {}\n".format(task)
-    return "MAIN", None, history, task
-
-def end_fn(purpose, task, history, directory, action_input):
-    logging.info(f"CALLING END_FN: {action_input}")
-    task = "END"
-    return "COMPLETE", "COMPLETE", history, task
-
-NAME_TO_FUNC = {
-    "MAIN": call_main,
-    "UPDATE-TASK": call_set_task,
-    "SEARCH": call_search,
-    "COMPLETE": end_fn,
-}
-
-def run_action(purpose, task, history, directory, action_name, action_input):
-    logging.info(f"RUNNING ACTION: {action_name} - {action_input}")
-    try:
-        if "RESPONSE" in action_name or "COMPLETE" in action_name:
-            action_name="COMPLETE"
-            task="END"
-            return action_name, "COMPLETE", history, task
-    
-        # compress the history when it is long
-        if len(history.split("\n")) > MAX_HISTORY:
-            logging.info("COMPRESSING HISTORY")
-            history = compress_history(purpose, task, history, directory)
-        if not action_name in NAME_TO_FUNC:
-            action_name="MAIN"
-        if action_name == "" or action_name == None:
-            action_name="MAIN"
-        assert action_name in NAME_TO_FUNC
-    
-        logging.info(f"RUN: {action_name} - {action_input}")
-        return NAME_TO_FUNC[action_name](purpose, task, history, directory, action_input)
-    except Exception as e:
-        history += "observation: the previous command did not produce any useful output, I need to check the commands syntax, or use a different command\n"
-        logging.error(f"Error in run_action: {e}")
-        return "MAIN", None, history, task
-
-def run(purpose, history):
-    task = None
-    directory = "./"
-    if history:
-        history = str(history).strip("[]")
-    if not history:
-        history = ""
-    
-    action_name = "UPDATE-TASK" if task is None else "MAIN"
-    action_input = None
-    while True:
-        logging.info(f"---")
-        logging.info(f"Purpose: {purpose}")
-        logging.info(f"Task: {task}")
-        logging.info(f"---")
-        logging.info(f"History: {history}")
-        logging.info(f"---")
-
-        action_name, action_input, history, task = run_action(
-            purpose,
-            task,
-            history,
-            directory,
-            action_name,
-            action_input,
-        )
-        yield (history)
-        if task == "END":
-            return (history)
-
-def generate_text_chunked(input_text, model, generation_parameters, max_tokens_to_generate):
-    """Generates text in chunks to avoid token limit errors."""
-    sentences = nltk.sent_tokenize(input_text)
-    generated_text = []
-    generator = pipeline('text-generation', model=model)
-
-    for sentence in sentences:
-        # Tokenize the sentence and check if it's within the limit
-        tokens = generator.tokenizer(sentence).input_ids
-        if len(tokens) + max_tokens_to_generate <= 32768:
-            # Generate text for this chunk
-            response = generator(sentence, max_length=max_tokens_to_generate, **generation_parameters)
-            generated_text.append(response[0]['generated_text'])
-        else:
-            # Handle cases where the sentence is too long
-            print(f"Sentence too long: {sentence}") 
-
-    return ''.join(generated_text)
-
-# Gradio Interface
-def gradio_interface(purpose, history):
-    try:
-        history = json.loads(history) if history else []
-    except json.JSONDecodeError:
-        history = []
-    result = run(purpose, history)
-    return next(result)
-
-iface = gr.Interface(
-    fn=gradio_interface,
-    inputs=[
-        gr.Textbox(lines=2, placeholder="Enter the purpose here..."),
-        gr.Textbox(lines=10, placeholder="Enter the history here (JSON format)...")
-    ],
-    outputs="text",
-    title="AI Assistant",
-    description="An AI assistant that helps with various tasks."
-)
+        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()
+
+# OpenAI GPT-3 API setup for text generation
+openai.api_key = st.secrets["OPENAI_API_KEY"]
+
+# Initialize the Hugging Face model and tokenizer
+model_name = "gpt2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
+
+# Tool Box UI elements
+def toolbox():
+    st.header("Tool Box")
+
+    # List available agents
+    for agent in st.session_state.available_agents:
+        st.markdown(f"### {agent}")
+        st.write(agent.description)
+        if st.button(f'Chat with {agent}'):
+            chat_with_agent(agent)
+
+    # Add new agents
+    if st.session_state['toolbox'].get('new_agent') is None:
+        st.session_state['toolbox']['new_agent'] = {}
+
+    st.text_input("Agent Name", key='name', on_change=update_agent)
+    st.text_area("Agent Description", key='description', on_change=update_agent)
+    st.text_input("Skills (comma-separated)", key='skills', on_change=update_agent)
+
+    if st.button('Create New Agent'):
+        skills = [s.strip() for s in st.session_state['toolbox']['new_agent'].get('skills', '').split(',')]
+        new_agent = AIAgent(st.session_state['toolbox']['new_agent'].get('name'),
+                            st.session_state['toolbox']['new_agent'].get('description'), skills)
+        st.session_state.available_agents.append(new_agent)
+
+def update_agent():
+    st.session_state['toolbox']['new_agent'] = {
+        'name': st.session_state.name,
+        'description': st.session_state.description,
+        'skills': st.session_state.skills
+    }
+
+def chat_with_agent(agent_name):
+    st.subheader(f"Chat with {agent_name}")
+    chat_input = st.text_area("Enter your message:")
+    if st.button("Send"):
+        chat_response = chat_interface_with_agent(chat_input, agent_name)
+        st.session_state.chat_history.append((chat_input, chat_response))
+        st.write(f"{agent_name}: {chat_response}")
+
+# Workspace UI elements
+def workspace():
+    st.header("Workspace")
+
+    # Project selection and interaction
+    for project, details in st.session_state.workspace_projects.items():
+        st.write(f"Project: {project}")
+        for file in details['files']:
+            st.write(f" - {file}")
+
+    if st.button('Add New Project'):
+        new_project = {'name': '', 'description': '', 'files': []}
+        st.session_state.workspace_projects[new_project['name']] = new_project
+
+# Main function to display the app
+def main():
+    toolbox()
+    workspace()
 
 if __name__ == "__main__":
-    iface.launch()
+    main()
+
+# Additional functionalities
+def commit_and_push_changes(commit_message):
+    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 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."
+
+    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]
+
+    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
+
+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 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 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 translate_code(code, input_language, output_language):
+    language_extensions = {
+        "Python": ".py",
+        "JavaScript": ".js",
+        # Add more languages and their extensions here
+    }
+    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}")
+
+    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()
+    st.session_state.current_state['toolbox']['translated_code'] = translated_code
+    return translated_code
+
+# 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)
+            st.session_state.chat_history.append((chat_input, chat_response))
+            st.write(f"{agent_name}: {chat_response}")
+
+    # Code Generation
+    st.subheader("Generate Code")
+    code_idea = st.text_area("Enter your code idea:")
+    if st.button("Generate Code"):
+        generated_code = generate_code(code_idea)
+        st.code(generated_code, language='python')
+
+    # Code Translation
+    st.subheader("Translate Code")
+    code = st.text_area("Enter your code:")
+    input_language = st.selectbox("Input Language", ["Python", "JavaScript"])
+    output_language = st.selectbox("Output Language", ["Python", "JavaScript"])
+    if st.button("Translate Code"):
+        translated_code = translate_code(code, input_language, output_language)
+        st.code(translated_code, language=output_language.lower())
+
+    # Summarization
+    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(summary)
+
+    # Sentiment Analysis
+    st.subheader("Sentiment Analysis")
+    text_to_analyze = st.text_area("Enter text for sentiment analysis:")
+    if st.button("Analyze Sentiment"):
+        sentiment = sentiment_analysis(text_to_analyze)
+        st.write(sentiment)
+
+elif app_mode == "Workspace Chat App":
+    # Workspace Chat App
+    st.header("Workspace Chat App")
+
+    # Project Management
+    st.subheader("Manage Projects")
+    project_name = st.text_input("Enter project name:")
+    if st.button("Create Project"):
+        project_message = workspace_interface(project_name)
+        st.success(project_message)
+
+    # Add Code to Project
+    st.subheader("Add Code to Project")
+    project_name_for_code = st.text_input("Enter project name for code:")
+    code_content = st.text_area("Enter code content:")
+    file_name = st.text_input("Enter file name:")
+    if st.button("Add Code"):
+        add_code_message = add_code_to_workspace(project_name_for_code, code_content, file_name)
+        st.success(add_code_message)
+
+    # Terminal Interface
+    st.subheader("Terminal Interface")
+    terminal_command = st.text_area("Enter terminal command:")
+    project_name_for_terminal = st.text_input("Enter project name for terminal (optional):")
+    if st.button("Run Command"):
+        terminal_output = terminal_interface(terminal_command, project_name_for_terminal)
+        st.text(terminal_output)