Update app.py

app.py

@@ -1,486 +1,474 @@
 import os
+import sys
 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 agent import (
-    ACTION_PROMPT,
-    ADD_PROMPT,
-    COMPRESS_HISTORY_PROMPT,
-    LOG_PROMPT,
-    LOG_RESPONSE,
-    MODIFY_PROMPT,
-    PREFIX,
-    SEARCH_QUERY,
-    READ_PROMPT,
-    TASK_PROMPT,
-    UNDERSTAND_TEST_RESULTS_PROMPT,
-)
-from utils import parse_action, parse_file_content, read_python_module_structure
-from datetime import datetime
-now = datetime.now()
-date_time_str = now.strftime("%Y-%m-%d %H:%M:%S")
-
-client = InferenceClient(
-    "mistralai/Mixtral-8x7B-Instruct-v0.1"
-)
-
-############################################
-
-
-VERBOSE = True
-MAX_HISTORY = 100
-#MODEL = "gpt-3.5-turbo"  # "gpt-4"
-
-
-def format_prompt(message, history):
-  prompt = "<s>"
-  for user_prompt, bot_response in history:
-    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,
-    module_summary,
-    purpose,
-    **prompt_kwargs,
-):
-    seed = random.randint(1,1111111111111111)
-
-    generate_kwargs = dict(
-        temperature=0.9,
-        max_new_tokens=1048,
-        top_p=0.95,
-        repetition_penalty=1.0,
-        do_sample=True,
-        seed=seed,
-    )
-
-    
-    content = PREFIX.format(
-        date_time_str=date_time_str,
-        purpose=purpose,
-        safe_search=safe_search,
-    ) + prompt_template.format(**prompt_kwargs)
-    if VERBOSE:
-        print(LOG_PROMPT.format(content))
-    
-    
-    #formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
-    #formatted_prompt = format_prompt(f'{content}', history)
-
-    stream = client.text_generation(content, **generate_kwargs, stream=True, details=True, return_full_text=False)
-    resp = ""
-    for response in stream:
-        resp += response.token.text
-
-    if VERBOSE:
-        print(LOG_RESPONSE.format(resp))
-    return resp
-
-
-def compress_history(purpose, task, history, directory):
-    module_summary, _, _ = read_python_module_structure(directory)
-    resp = run_gpt(
-        COMPRESS_HISTORY_PROMPT,
-        stop_tokens=["observation:", "task:", "action:", "thought:"],
-        max_tokens=512,
-        module_summary=module_summary,
-        purpose=purpose,
-        task=task,
-        history=history,
-    )
-    history = "observation: {}\n".format(resp)
-    return history
-    
-def call_search(purpose, task, history, directory, action_input):
-    print("CALLING SEARCH")
+import base64
+import json
+from io import StringIO
+from typing import Dict, List
+
+import streamlit as st
+from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer
+from pylint import lint
+
+# Add your Hugging Face API token here
+hf_token = st.secrets["huggingface"]
+
+# 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 = {}
+
+# Load pre-trained RAG retriever
+rag_retriever = pipeline("retrieval-question-answering", model="facebook/rag-token-base")
+
+# Load pre-trained chat model
+chat_model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/DialoGPT-medium")
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+
+def process_input(user_input: str) -> str:
+    # Input pipeline: Tokenize and preprocess user input
+    input_ids = tokenizer(user_input, return_tensors="pt").input_ids
+    attention_mask = tokenizer(user_input, return_tensors="pt").attention_mask
+
+    # RAG model: Generate response
+    with torch.no_grad():
+        output = rag_retriever(input_ids, attention_mask=attention_mask)
+        response = output.generator_outputs[0].sequences[0]
+
+    # Chat model: Refine response
+    chat_input = tokenizer(response, return_tensors="pt")
+    chat_input["input_ids"] = chat_input["input_ids"].unsqueeze(0)
+    chat_input["attention_mask"] = chat_input["attention_mask"].unsqueeze(0)
+    with torch.no_grad():
+        chat_output = chat_model(**chat_input)
+        refined_response = chat_output.sequences[0]
+
+    # Output pipeline: Return final response
+    return refined_response
+
+class AIAgent:
+    def __init__(self, name: str, description: str, skills: List[str], hf_api=None):
+        self.name = name
+        self.description = description
+        self.skills = skills
+        self._hf_api = hf_api
+        self._hf_token = hf_token
+
+    @property
+    def hf_api(self):
+        if not self._hf_api and self.has_valid_hf_token():
+            self._hf_api = HfApi(token=self._hf_token)
+        return self._hf_api
+
+    def has_valid_hf_token(self):
+        return bool(self._hf_token)
+
+    async def autonomous_build(self, chat_history: List[str], workspace_projects: Dict[str, str], project_name: str, selected_model: str):
+        # Continuation of previous methods
+        summary = "Chat History:\n" + "\n".join(chat_history)
+        summary += "\n\nWorkspace Projects:\n" + "\n".join(workspace_projects.items())
+
+        # Analyze chat history and workspace projects to suggest actions
+        # Example:
+        # - Check if the user has requested to create a new file
+        # - Check if the user has requested to install a package
+        # - Check if the user has requested to run a command
+        # - Check if the user has requested to generate code
+        # - Check if the user has requested to translate code
+        # - Check if the user has requested to summarize text
+        # - Check if the user has requested to analyze sentiment
+
+        # Generate a response based on the analysis
+        next_step = "Based on the current state, the next logical step is to implement the main application logic."
+
+        # Ensure project folder exists
+        project_path = os.path.join(PROJECT_ROOT, project_name)
+        if not os.path.exists(project_path):
+            os.makedirs(project_path)
+
+        # Create requirements.txt if it doesn't exist
+        requirements_file = os.path.join(project_path, "requirements.txt")
+        if not os.path.exists(requirements_file):
+            with open(requirements_file, "w") as f:
+                f.write("# Add your project's dependencies here\n")
+
+        # Create app.py if it doesn't exist
+        app_file = os.path.join(project_path, "app.py")
+        if not os.path.exists(app_file):
+            with open(app_file, "w") as f:
+                f.write("# Your project's main application logic goes here\n")
+
+        # Generate GUI code for app.py if requested
+        if "create a gui" in summary.lower():
+            gui_code = generate_code(
+                "Create a simple GUI for this application", selected_model)
+            with open(app_file, "a") as f:
+                f.write(gui_code)
+
+        # Run the default build process
+        build_command = "pip install -r requirements.txt && python app.py"
+        try:
+            result = subprocess.run(
+                build_command, shell=True, capture_output=True, text=True, cwd=project_path)
+            st.write(f"Build Output:\n{result.stdout}")
+            if result.stderr:
+                st.error(f"Build Errors:\n{result.stderr}")
+        except Exception as e:
+            st.error(f"Build Error: {e}")
+
+        return summary, next_step
+
+def get_built_space_files() -> Dict[str, str]:
+    # Replace with your logic to gather the files you want to deploy
+    return {
+        "app.py": "# Your Streamlit app code here",
+        "requirements.txt": "streamlit\ntransformers"
+        # Add other files as needed
+    }
+
+def save_agent_to_file(agent: AIAgent):
+    """Saves the agent's prompt to a file."""
+    if not os.path.exists(AGENT_DIRECTORY):
+        os.makedirs(AGENT_DIRECTORY)
+    file_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}.txt")
+    with open(file_path, "w") as file:
+        file.write(agent.create_agent_prompt())
+    st.session_state.available_agents.append(agent.name)
+
+def load_agent_prompt(agent_name: str) -> str:
+    """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: str, text: str) -> str:
+    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: str, agent_name: str) -> str:
+    agent_prompt = load_agent_prompt(agent_name)
+    if agent_prompt is None:
+        return f"Agent {agent_name} not found."
+
+    model_name = "MaziyarPanahi/Codestral-22B-v0.1-GGUF"
     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)
-            #response = google(search_return)
-            print(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"
+        generator = pipeline("text-generation", model=model_name)
+        generator.tokenizer.pad_token = generator.tokenizer.eos_token
+        generated_response = generator(
+            f"{agent_prompt}\n\nUser: {input_text}\nAgent:", max_length=100, do_sample=True, top_k=50)[0]["generated_text"]
+        return generated_response
     except Exception as e:
-        history += "observation: {}'\n".format(e)
-    return "MAIN", None, history, task
-
-def call_main(purpose, task, history, directory, action_input):
-    module_summary, _, _ = read_python_module_structure(directory)
-    resp = run_gpt(
-        ACTION_PROMPT,
-        stop_tokens=["observation:", "task:"],
-        max_tokens=256,
-        module_summary=module_summary,
-        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)
-        elif line.startswith("action: "):
-            
-            action_name, action_input = parse_action(line)
-            print (f'ACTION_NAME :: {action_name}')
-            print (f'ACTION_INPUT :: {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)
-            #history += "observation: the following command did not produce any useful output: '{}', I need to check the commands syntax, or use a different command\n".format(line)
-            
-            #return action_name, action_input, history, task
-            #assert False, "unknown action: {}".format(line)
-    return "MAIN", None, history, task
-
-
-def call_test(purpose, task, history, directory, action_input):
-    result = subprocess.run(
-        ["python", "-m", "pytest", "--collect-only", directory],
-        capture_output=True,
-        text=True,
-    )
-    if result.returncode != 0:
-        history += "observation: there are no tests! Test should be written in a test folder under {}\n".format(
-            directory
-        )
-        return "MAIN", None, history, task
-    result = subprocess.run(
-        ["python", "-m", "pytest", directory], capture_output=True, text=True
-    )
-    if result.returncode == 0:
-        history += "observation: tests pass\n"
-        return "MAIN", None, history, task
-    module_summary, content, _ = read_python_module_structure(directory)
-    resp = run_gpt(
-        UNDERSTAND_TEST_RESULTS_PROMPT,
-        stop_tokens=[],
-        max_tokens=256,
-        module_summary=module_summary,
-        purpose=purpose,
-        task=task,
-        history=history,
-        stdout=result.stdout[:5000],  # limit amount of text
-        stderr=result.stderr[:5000],  # limit amount of text
-    )
-    history += "observation: tests failed: {}\n".format(resp)
-    return "MAIN", None, history, task
-
-
-def call_set_task(purpose, task, history, directory, action_input):
-    module_summary, content, _ = read_python_module_structure(directory)
-    task = run_gpt(
-        TASK_PROMPT,
-        stop_tokens=[],
-        max_tokens=64,
-        module_summary=module_summary,
-        purpose=purpose,
-        task=task,
-        history=history,
-    ).strip("\n")
-    history += "observation: task has been updated to: {}\n".format(task)
-    return "MAIN", None, history, task
-
-
-def call_read(purpose, task, history, directory, action_input):
-    if not os.path.exists(action_input):
-        history += "observation: file does not exist\n"
-        return "MAIN", None, history, task
-    module_summary, content, _ = read_python_module_structure(directory)
-    f_content = (
-        content[action_input] if content[action_input] else "< document is empty >"
-    )
-    resp = run_gpt(
-        READ_PROMPT,
-        stop_tokens=[],
-        max_tokens=256,
-        module_summary=module_summary,
-        purpose=purpose,
-        task=task,
-        history=history,
-        file_path=action_input,
-        file_contents=f_content,
-    ).strip("\n")
-    history += "observation: {}\n".format(resp)
-    return "MAIN", None, history, task
-
-
-def call_modify(purpose, task, history, directory, action_input):
-    if not os.path.exists(action_input):
-        history += "observation: file does not exist\n"
-        return "MAIN", None, history, task
-    (
-        module_summary,
-        content,
-        _,
-    ) = read_python_module_structure(directory)
-    f_content = (
-        content[action_input] if content[action_input] else "< document is empty >"
-    )
-    resp = run_gpt(
-        MODIFY_PROMPT,
-        stop_tokens=["action:", "thought:", "observation:"],
-        max_tokens=2048,
-        module_summary=module_summary,
-        purpose=purpose,
-        task=task,
-        history=history,
-        file_path=action_input,
-        file_contents=f_content,
-    )
-    new_contents, description = parse_file_content(resp)
-    if new_contents is None:
-        history += "observation: failed to modify file\n"
-        return "MAIN", None, history, task
-
-    with open(action_input, "w") as f:
-        f.write(new_contents)
-
-    history += "observation: file successfully modified\n"
-    history += "observation: {}\n".format(description)
-    return "MAIN", None, history, task
-
-
-def call_add(purpose, task, history, directory, action_input):
-    d = os.path.dirname(action_input)
-    if not d.startswith(directory):
-        history += "observation: files must be under directory {}\n".format(directory)
-    elif not action_input.endswith(".py"):
-        history += "observation: can only write .py files\n"
+        return f"Error loading model: {e}"
+
+def terminal_interface(command: str, project_name: str = None) -> str:
+    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, shell=True, capture_output=True, text=True, cwd=project_path)
     else:
-        if d and not os.path.exists(d):
-            os.makedirs(d)
-        if not os.path.exists(action_input):
-            module_summary, _, _ = read_python_module_structure(directory)
-            resp = run_gpt(
-                ADD_PROMPT,
-                stop_tokens=["action:", "thought:", "observation:"],
-                max_tokens=2048,
-                module_summary=module_summary,
-                purpose=purpose,
-                task=task,
-                history=history,
-                file_path=action_input,
-            )
-            new_contents, description = parse_file_content(resp)
-            if new_contents is None:
-                history += "observation: failed to write file\n"
-                return "MAIN", None, history, task
-
-            with open(action_input, "w") as f:
-                f.write(new_contents)
-
-            history += "observation: file successfully written\n"
-            history += "obsertation: {}\n".format(description)
-        else:
-            history += "observation: file already exists\n"
-    return "MAIN", None, history, task
-def end_fn(purpose, task, history, directory, action_input):
-    task = "END"
-    return "COMPLETE", None, 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):
-    if "RESPONSE" in action_name:
-        task="END"
-        return action_name, action_input, history, task
-
-    # compress the history when it is long
-    if len(history.split("\n")) > MAX_HISTORY:
-        if VERBOSE:
-            print("COMPRESSING HISTORY")
-        history = compress_history(purpose, task, history, directory)
-
-    assert action_name in NAME_TO_FUNC
-
-    print("RUN: ", action_name, action_input)
-    return NAME_TO_FUNC[action_name](purpose, task, history, directory, action_input)
-
-
-def run(purpose,hist):
-    
-    print(purpose)
-    print(hist)
-    task=None
-    directory="./"
-    history = ""
-    action_name = "UPDATE-TASK" if task is None else "MAIN"
-    action_input = None
-    while True:
-        print("")
-        print("")
-        print("---")
-        print("purpose:", purpose)
-        print("task:", task)
-        print("---")
-        print(history)
-        print("---")
-
-        action_name, action_input, history, task = run_action(
-            purpose,
-            task,
-            history,
-            directory,
-            action_name,
-            action_input,
-        )
-        if task == "END":
-            return history
-
-
-
-################################################
-
-def format_prompt(message, history):
-  prompt = "<s>"
-  for user_prompt, bot_response in history:
-    prompt += f"[INST] {user_prompt} [/INST]"
-    prompt += f" {bot_response}</s> "
-  prompt += f"[INST] {message} [/INST]"
-  return prompt
-agents =[
-    "WEB_DEV",
-    "AI_SYSTEM_PROMPT",
-    "PYTHON_CODE_DEV"
-]
-def generate(
-        prompt, history, agent_name=agents[0], sys_prompt="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
-):
-    seed = random.randint(1,1111111111111111)
-
-    agent=prompts.WEB_DEV
-    if agent_name == "WEB_DEV":
-        agent = prompts.WEB_DEV
-    if agent_name == "AI_SYSTEM_PROMPT":
-        agent = prompts.AI_SYSTEM_PROMPT
-    if agent_name == "PYTHON_CODE_DEV":
-        agent = prompts.PYTHON_CODE_DEV        
-    system_prompt=agent
-    temperature = float(temperature)
-    if temperature < 1e-2:
-        temperature = 1e-2
-    top_p = float(top_p)
-
-    generate_kwargs = dict(
-        temperature=temperature,
-        max_new_tokens=max_new_tokens,
-        top_p=top_p,
-        repetition_penalty=repetition_penalty,
-        do_sample=True,
-        seed=seed,
-    )
-
-    formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
-    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
-    output = ""
-
-    for response in stream:
-        output += response.token.text
-        yield output
-    return output
-
-
-additional_inputs=[
-    gr.Dropdown(
-        label="Agents",
-        choices=[s for s in agents],
-        value=agents[0],
-        interactive=True,
-        ),
-    gr.Textbox(
-        label="System Prompt",
-        max_lines=1,
-        interactive=True,
-    ),
-    gr.Slider(
-        label="Temperature",
-        value=0.9,
-        minimum=0.0,
-        maximum=1.0,
-        step=0.05,
-        interactive=True,
-        info="Higher values produce more diverse outputs",
-    ),
-
-    gr.Slider(
-        label="Max new tokens",
-        value=1048*10,
-        minimum=0,
-        maximum=1048*10,
-        step=64,
-        interactive=True,
-        info="The maximum numbers of new tokens",
-    ),
-    gr.Slider(
-        label="Top-p (nucleus sampling)",
-        value=0.90,
-        minimum=0.0,
-        maximum=1,
-        step=0.05,
-        interactive=True,
-        info="Higher values sample more low-probability tokens",
-    ),
-    gr.Slider(
-        label="Repetition penalty",
-        value=1.2,
-        minimum=1.0,
-        maximum=2.0,
-        step=0.05,
-        interactive=True,
-        info="Penalize repeated tokens",
-    ),
-
-
-]
-
-examples=[["What are the biggest news stories today?", None, None, None, None, None, ],
-          ["When is the next full moon?", None, None, None, None, None, ],
-          ["I'm planning a vacation to Japan. Can you suggest a one-week itinerary including must-visit places and local cuisines to try?", None, None, None, None, None, ],
-          ["Can you write a short story about a time-traveling detective who solves historical mysteries?", None, None, None, None, None,],
-          ["I'm trying to learn French. Can you provide some common phrases that would be useful for a beginner, along with their pronunciations?", None, None, None, None, None,],
-          ["I have chicken, rice, and bell peppers in my kitchen. Can you suggest an easy recipe I can make with these ingredients?", None, None, None, None, None,],
-          ["Can you explain how the QuickSort algorithm works and provide a Python implementation?", None, None, None, None, None,],
-          ["What are some unique features of Rust that make it stand out compared to other systems programming languages like C++?", None, None, None, None, None,],
-         ]
-
-
-gr.ChatInterface(
-    fn=run,
-    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
-    title="Mixtral 46.7B\nMicro-Agent\nInternet Search <br> development test",
-    examples=examples,
-    concurrency_limit=20,
-).launch(show_api=False)
-
+        result = subprocess.run(command, shell=True, capture_output=True, text=True)
+    return result.stdout
 
+def code_editor_interface(code: str) -> str:
+    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()
+
+    return formatted_code, lint_message
+
+def summarize_text(text: str) -> str:
+    summarizer = pipeline("summarization")
+    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
+    return summary[0]['summary_text']
+
+def sentiment_analysis(text: str) -> str:
+    analyzer = pipeline("sentiment-analysis")
+    result = analyzer(text)
+    return result[0]['label']
+
+def translate_code(code: str, source_language: str, target_language: str) -> str:
+    # Use a Hugging Face translation model instead of OpenAI
+    # Example: English to Spanish
+    translator = pipeline(
+        "translation", model="bartowski/Codestral-22B-v0.1-GGUF")
+    translated_code = translator(code, target_lang=target_language)[0]['translation_text']
+    return translated_code
+
+def generate_code(code_idea: str, model_name: str) -> str:
+    """Generates code using the selected model."""
+    try:
+        generator = pipeline('text-generation', model=model_name)
+        generated_code = generator(code_idea, max_length=1000, num_return_sequences=1)[0]['generated_text']
+        return generated_code
+    except Exception as e:
+        return f"Error generating code: {e}"
+
+def chat_interface(input_text: str) -> str:
+    """Handles general chat interactions with the user."""
+    # Use a Hugging Face chatbot model or your own logic
+    chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium")
+    response = chatbot(input_text, max_length=50, num_return_sequences=1)[0]['generated_text']
+    return response
+
+def workspace_interface(project_name: str) -> str:
+    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."
+
+def add_code_to_workspace(project_name: str, code: str, file_name: str) -> str:
+    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}'."
+
+def create_space_on_hugging_face(api, name, description, public, files, entrypoint="launch.py"):
+    url = f"{hf_hub_url()}spaces/{name}/prepare-repo"
+    headers = {"Authorization": f"Bearer {api.access_token}"}
+    payload = {
+        "public": public,
+        "gitignore_template": "web",
+        "default_branch": "main",
+        "archived": False,
+        "files": []
+    }
+    for filename, contents in files.items():
+        data = {
+            "content": contents,
+            "path": filename,
+            "encoding": "utf-8",
+            "mode": "overwrite"
+        }
+        payload["files"].append(data)
+    response = requests.post(url, json=payload, headers=headers)
+    response.raise_for_status()
+    location = response.headers.get("Location")
+    # wait_for_processing(location, api)  # You might need to implement this if it's not already defined
+
+    return Repository(name=name, api=api)
+
+# 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)
+
+        # Automatically create requirements.txt and app.py
+        project_path = os.path.join(PROJECT_ROOT, project_name)
+        requirements_file = os.path.join(project_path, "requirements.txt")
+        if not os.path.exists(requirements_file):
+            with open(requirements_file, "w") as f:
+                f.write("# Add your project's dependencies here\n")
+
+        app_file = os.path.join(project_path, "app.py")
+        if not os.path.exists(app_file):
+            with open(app_file, "w") as f:
+                f.write("# Your project's main application logic goes here\n")
+
+    # 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.session_state.terminal_history.append(
+            (f"Add Code: {code_to_add}", add_code_status))
+        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.session_state.terminal_history.append(
+            (terminal_input, terminal_output))
+        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}")
+
+    # Code Generation
+    st.subheader("Code Generation")
+    code_idea = st.text_input("Enter your code idea:")
+
+    # Model Selection Menu
+    selected_model = st.selectbox(
+        "Select a code-generative model", AVAILABLE_CODE_GENERATIVE_MODELS)
+
+    if st.button("Generate Code"):
+        generated_code = generate_code(code_idea, selected_model)
+        st.code(generated_code, language="python")
+
+    # Automate Build Process
+    st.subheader("Automate Build Process")
+    if st.button("Automate"):
+        # Load the agent without skills for now
+        agent = AIAgent(selected_agent, "", [])
+        summary, next_step = agent.autonomous_build(
+            st.session_state.chat_history, st.session_state.workspace_projects, project_name, selected_model)
+        st.write("Autonomous Build Summary:")
+        st.write(summary)
+        st.write("Next Step:")
+        st.write(next_step)
+
+    # If everything went well, proceed to deploy the Space
+    if agent._hf_api and agent.has_valid_hf_token():
+        agent.deploy_built_space_to_hf()
+        # Use the hf_token to interact with the Hugging Face API
+        api = HfApi(token="hf_token")        # Function to create a Space on Hugging Face
+        create_space_on_hugging_face(api, agent.name, agent.description, True, get_built_space_files())