Update app.py

app.py

@@ -1,416 +1,392 @@
 import os
-import sys
 import subprocess
-import streamlit as st
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
-import black
-from pylint import lint
-from io import StringIO
-import openai
-
-# Set your OpenAI API key here
-openai.api_key = "YOUR_OPENAI_API_KEY"
-
-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):
+import random
+from typing import List, Dict, Tuple
+from datetime import datetime
+import logging
+
+import gradio as gr
+from huggingface_hub import InferenceClient
+
+# --- Configuration ---
+MODEL_NAME = "mistralai/Mixtral-8x7B-Instruct-v0.1"  # Hugging Face model for text generation
+MAX_HISTORY_TURNS = 5  # Number of previous turns to include in the prompt
+MAX_TOKENS_PER_TURN = 2048  # Maximum number of tokens to generate per turn
+VERBOSE_LOGGING = True  # Enable verbose logging for debugging
+DEFAULT_AGENT = "WEB_DEV"  # Default agent to use
+
+# --- Logging Setup ---
+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",
+)
+
+# --- Agent Definitions ---
+class Agent:
+    """Base class for all agents."""
+
+    def __init__(self, name: str, description: str):
         self.name = name
         self.description = description
-        self.skills = skills
-
-    def create_agent_prompt(self):
-        skills_str = '\n'.join([f"* {skill}" for skill in self.skills])
-        agent_prompt = f"""
-As an elite expert developer, my name is {self.name}. I possess a comprehensive understanding of the following areas:
-{skills_str}
-I am confident that I can leverage my expertise to assist you in developing and deploying cutting-edge web applications. Please feel free to ask any questions or present any challenges you may encounter.
-"""
-        return agent_prompt
-
-    def autonomous_build(self, chat_history, workspace_projects):
-        """
-        Autonomous build logic that continues based on the state of chat history and workspace projects.
+
+    def handle_action(self, action: str, action_input: str, history: List[Tuple[str, str]], task: str) -> Tuple[str, str, List[Tuple[str, str]], str]:
+        """Handles an action from the user.
+
+        Args:
+            action: The action name.
+            action_input: The input for the action.
+            history: The conversation history.
+            task: The current task.
+
+        Returns:
+            A tuple containing the next action name, action input, updated history, and updated task.
         """
-        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
+        raise NotImplementedError("Agent subclasses must implement handle_action.")
 
-def code_editor_interface(code):
+    def get_prompt(self, message: str, history: List[Tuple[str, str]], task: str) -> str:
+        """Generates a prompt for the language model.
+
+        Args:
+            message: The user's message.
+            history: The conversation history.
+            task: The current task.
+
+        Returns:
+            The prompt string.
+        """
+        now = datetime.now()
+        date_time_str = now.strftime("%Y-%m-%d %H:%M:%S")
+        prompt = f"""
+        {date_time_str}
+        Agent: {self.name}
+        Task: {task}
+        History:
+        {self.format_history(history)}
+        Message: {message}
+        """
+        return prompt
+
+    def format_history(self, history: List[Tuple[str, str]]) -> str:
+        """Formats the conversation history for the prompt."""
+        formatted_history = ""
+        for user_message, agent_response in history[-MAX_HISTORY_TURNS:]:
+            formatted_history += f"[INST] {user_message} [/INST]\n{agent_response}\n"
+        return formatted_history
+
+class WebDevAgent(Agent):
+    """Agent for web development tasks."""
+
+    def __init__(self):
+        super().__init__(name="WEB_DEV", description="Agent specialized in web development tasks.")
+
+    def handle_action(self, action: str, action_input: str, history: List[Tuple[str, str]], task: str) -> Tuple[str, str, List[Tuple[str, str]], str]:
+        if action == "SEARCH":
+            return self._handle_search_action(action_input, history, task)
+        elif action == "GENERATE_HTML":
+            return self._handle_generate_html_action(action_input, history, task)
+        elif action == "GENERATE_CSS":
+            return self._handle_generate_css_action(action_input, history, task)
+        elif action == "GENERATE_JS":
+            return self._handle_generate_js_action(action_input, history, task)
+        elif action == "COMPLETE":
+            return "COMPLETE", "COMPLETE", history, task
+        else:
+            return "MAIN", None, history, task
+
+    def _handle_search_action(self, action_input: str, history: List[Tuple[str, str]], task: str) -> Tuple[str, str, List[Tuple[str, str]], str]:
+        """Handles the SEARCH action."""
+        if VERBOSE_LOGGING:
+            logging.info(f"Calling SEARCH action with input: {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)  # Use i_search for web search
+                history.append(("observation: search result is:", response))
+            else:
+                history.append(("observation: I need a valid URL for the SEARCH action.", ""))
+        except Exception as e:
+            history.append(("observation:", str(e)))
+        return "MAIN", None, history, task
+
+    def _handle_generate_html_action(self, action_input: str, history: List[Tuple[str, str]], task: str) -> Tuple[str, str, List[Tuple[str, str]], str]:
+        """Handles the GENERATE_HTML action."""
+        if VERBOSE_LOGGING:
+            logging.info(f"Calling GENERATE_HTML action with input: {action_input}")
+        # Simulate OpenAI's code generation capabilities using Hugging Face
+        prompt = self.get_prompt(f"Generate HTML code for a web page that {action_input}", history, task)
+        response = run_gpt(prompt, stop_tokens=["```", "```html"], max_tokens=MAX_TOKENS_PER_TURN)
+        history.append(("observation: generated HTML code:", response))
+        return "MAIN", None, history, task
+
+    def _handle_generate_css_action(self, action_input: str, history: List[Tuple[str, str]], task: str) -> Tuple[str, str, List[Tuple[str, str]], str]:
+        """Handles the GENERATE_CSS action."""
+        if VERBOSE_LOGGING:
+            logging.info(f"Calling GENERATE_CSS action with input: {action_input}")
+        # Simulate OpenAI's code generation capabilities using Hugging Face
+        prompt = self.get_prompt(f"Generate CSS code for a web page that {action_input}", history, task)
+        response = run_gpt(prompt, stop_tokens=["```", "```css"], max_tokens=MAX_TOKENS_PER_TURN)
+        history.append(("observation: generated CSS code:", response))
+        return "MAIN", None, history, task
+
+    def _handle_generate_js_action(self, action_input: str, history: List[Tuple[str, str]], task: str) -> Tuple[str, str, List[Tuple[str, str]], str]:
+        """Handles the GENERATE_JS action."""
+        if VERBOSE_LOGGING:
+            logging.info(f"Calling GENERATE_JS action with input: {action_input}")
+        # Simulate OpenAI's code generation capabilities using Hugging Face
+        prompt = self.get_prompt(f"Generate JavaScript code for a web page that {action_input}", history, task)
+        response = run_gpt(prompt, stop_tokens=["```", "```js"], max_tokens=MAX_TOKENS_PER_TURN)
+        history.append(("observation: generated JavaScript code:", response))
+        return "MAIN", None, history, task
+
+class AiSystemPromptAgent(Agent):
+    """Agent for generating system prompts."""
+
+    def __init__(self):
+        super().__init__(name="AI_SYSTEM_PROMPT", description="Agent specialized in generating system prompts.")
+
+    def handle_action(self, action: str, action_input: str, history: List[Tuple[str, str]], task: str) -> Tuple[str, str, List[Tuple[str, str]], str]:
+        if action == "GENERATE_PROMPT":
+            return self._handle_generate_prompt_action(action_input, history, task)
+        elif action == "COMPLETE":
+            return "COMPLETE", "COMPLETE", history, task
+        else:
+            return "MAIN", None, history, task
+
+    def _handle_generate_prompt_action(self, action_input: str, history: List[Tuple[str, str]], task: str) -> Tuple[str, str, List[Tuple[str, str]], str]:
+        """Handles the GENERATE_PROMPT action."""
+        if VERBOSE_LOGGING:
+            logging.info(f"Calling GENERATE_PROMPT action with input: {action_input}")
+        # Simulate OpenAI's prompt generation capabilities using Hugging Face
+        prompt = self.get_prompt(f"Generate a system prompt for a language model that {action_input}", history, task)
+        response = run_gpt(prompt, stop_tokens=["```", "```json"], max_tokens=MAX_TOKENS_PER_TURN)
+        history.append(("observation: generated system prompt:", response))
+        return "MAIN", None, history, task
+
+class PythonCodeDevAgent(Agent):
+    """Agent for Python code development tasks."""
+
+    def __init__(self):
+        super().__init__(name="PYTHON_CODE_DEV", description="Agent specialized in Python code development tasks.")
+
+    def handle_action(self, action: str, action_input: str, history: List[Tuple[str, str]], task: str) -> Tuple[str, str, List[Tuple[str, str]], str]:
+        if action == "GENERATE_CODE":
+            return self._handle_generate_code_action(action_input, history, task)
+        elif action == "RUN_CODE":
+            return self._handle_run_code_action(action_input, history, task)
+        elif action == "COMPLETE":
+            return "COMPLETE", "COMPLETE", history, task
+        else:
+            return "MAIN", None, history, task
+
+    def _handle_generate_code_action(self, action_input: str, history: List[Tuple[str, str]], task: str) -> Tuple[str, str, List[Tuple[str, str]], str]:
+        """Handles the GENERATE_CODE action."""
+        if VERBOSE_LOGGING:
+            logging.info(f"Calling GENERATE_CODE action with input: {action_input}")
+        # Simulate OpenAI's code generation capabilities using Hugging Face
+        prompt = self.get_prompt(f"Generate Python code that {action_input}", history, task)
+        response = run_gpt(prompt, stop_tokens=["```", "```python"], max_tokens=MAX_TOKENS_PER_TURN)
+        history.append(("observation: generated Python code:", response))
+        return "MAIN", None, history, task
+
+    def _handle_run_code_action(self, action_input: str, history: List[Tuple[str, str]], task: str) -> Tuple[str, str, List[Tuple[str, str]], str]:
+        """Handles the RUN_CODE action."""
+        if VERBOSE_LOGGING:
+            logging.info(f"Calling RUN_CODE action with input: {action_input}")
+        # Simulate OpenAI's code execution capabilities using Hugging Face
+        prompt = self.get_prompt(f"Run the following Python code and provide the output: {action_input}", history, task)
+        response = run_gpt(prompt, stop_tokens=["```", "```python"], max_tokens=MAX_TOKENS_PER_TURN)
+        history.append(("observation: code output:", response))
+        return "MAIN", None, history, task
+
+# --- Action Handlers ---
+def handle_main_action(action: str, action_input: str, history: List[Tuple[str, str]], task: str, agent: Agent) -> Tuple[str, str, List[Tuple[str, str]], str]:
+    """Handles the MAIN action, which is the default action."""
+    if VERBOSE_LOGGING:
+        logging.info(f"Calling MAIN action with input: {action_input}")
+    prompt = agent.get_prompt(action_input, history, task)
+    response = run_gpt(prompt, stop_tokens=["observation:", "task:", "action:", "thought:"], max_tokens=MAX_TOKENS_PER_TURN)
+    if VERBOSE_LOGGING:
+        logging.info(f"Response from model: {response}")
+    history.append((action_input, response))
+    lines = response.strip().strip("\n").split("\n")
+    for line in lines:
+        if line == "":
+            continue
+        if line.startswith("thought: "):
+            history.append((line, ""))
+            if VERBOSE_LOGGING:
+                logging.info(f"Thought: {line}")
+        elif line.startswith("action: "):
+            action_name, action_input = parse_action(line)
+            history.append((line, ""))
+            if VERBOSE_LOGGING:
+                logging.info(f"Action: {action_name} - {action_input}")
+            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.append((line, ""))
+            if VERBOSE_LOGGING:
+                logging.info(f"Other Output: {line}")
+    return "MAIN", None, history, task
+
+def handle_update_task_action(action: str, action_input: str, history: List[Tuple[str, str]], task: str, agent: Agent) -> Tuple[str, str, List[Tuple[str, str]], str]:
+    """Handles the UPDATE-TASK action, which updates the current task."""
+    if VERBOSE_LOGGING:
+        logging.info(f"Calling UPDATE-TASK action with input: {action_input}")
+    prompt = agent.get_prompt(action_input, history, task)
+    task = run_gpt(prompt, stop_tokens=[], max_tokens=64).strip("\n")
+    history.append(("observation: task has been updated to:", task))
+    return "MAIN", None, history, task
+
+def handle_search_action(action: str, action_input: str, history: List[Tuple[str, str]], task: str, agent: Agent) -> Tuple[str, str, List[Tuple[str, str]], str]:
+    """Handles the SEARCH action, which performs a web search."""
+    if VERBOSE_LOGGING:
+        logging.info(f"Calling SEARCH action with input: {action_input}")
     try:
-        formatted_code = black.format_str(code, mode=black.FileMode())
-    except black.NothingChanged:
-        formatted_code = code
-    result = StringIO()
-    sys.stdout = result
-    sys.stderr = result
-    (pylint_stdout, pylint_stderr) = lint.py_run(code, return_std=True)
-    sys.stdout = sys.__stdout__
-    sys.stderr = sys.__stderr__
-    lint_message = pylint_stdout.getvalue() + pylint_stderr.getvalue()
-    st.session_state.current_state['toolbox']['formatted_code'] = formatted_code
-    st.session_state.current_state['toolbox']['lint_message'] = lint_message
-    return formatted_code, lint_message
-
-def summarize_text(text):
-    summarizer = pipeline("summarization")
-    summary = summarizer(text, max_length=50, min_length=25, do_sample=False)
-    st.session_state.current_state['toolbox']['summary'] = summary[0]['summary_text']
-    return summary[0]['summary_text']
-
-def sentiment_analysis(text):
-    analyzer = pipeline("sentiment-analysis")
-    sentiment = analyzer(text)
-    st.session_state.current_state['toolbox']['sentiment'] = sentiment[0]
-    return sentiment[0]
-
-def translate_code(code, input_language, output_language):
-    # Define a dictionary to map programming languages to their corresponding file extensions
-    language_extensions = {
-        # ignore the specific languages right now, and continue to EOF
-    }
-
-    # Add code to handle edge cases such as invalid input and unsupported programming languages
-    if input_language not in language_extensions:
-        raise ValueError(f"Invalid input language: {input_language}")
-    if output_language not in language_extensions:
-        raise ValueError(f"Invalid output language: {output_language}")
-
-    # Use the dictionary to map the input and output languages to their corresponding file extensions
-    input_extension = language_extensions[input_language]
-    output_extension = language_extensions[output_language]
-
-    # Translate the code using the OpenAI API
-    prompt = f"Translate this code from {input_language} to {output_language}:\n\n{code}"
-    response = openai.ChatCompletion.create(
-        model="gpt-4",
-        messages=[
-            {"role": "system", "content": "You are an expert software developer."},
-            {"role": "user", "content": prompt}
-        ]
-    )
-    translated_code = response.choices[0].message['content'].strip()
-
-    # Return the translated code
-    translated_code = response.choices[0].message['content'].strip()
-    st.session_state.current_state['toolbox']['translated_code'] = translated_code
-    return translated_code
-
-def generate_code(code_idea):
-    response = openai.ChatCompletion.create(
-        model="gpt-4",
-        messages=[
-            {"role": "system", "content": "You are an expert software developer."},
-            {"role": "user", "content": f"Generate a Python code snippet for the following idea:\n\n{code_idea}"}
-        ]
-    )
-    generated_code = response.choices[0].message['content'].strip()
-    st.session_state.current_state['toolbox']['generated_code'] = generated_code
-    return generated_code
-
-def commit_and_push_changes(commit_message):
-    """Commits and pushes changes to the Hugging Face repository."""
-    commands = [
-        "git add .",
-        f"git commit -m '{commit_message}'",
-        "git push"
-    ]
-    for command in commands:
-        result = subprocess.run(command, shell=True, capture_output=True, text=True)
-        if result.returncode != 0:
-            st.error(f"Error executing command '{command}': {result.stderr}")
-            break
-
-# Streamlit App
-st.title("AI Agent Creator")
-
-# Sidebar navigation
-st.sidebar.title("Navigation")
-app_mode = st.sidebar.selectbox("Choose the app mode", ["AI Agent Creator", "Tool Box", "Workspace Chat App"])
-
-if app_mode == "AI Agent Creator":
-    # AI Agent Creator
-    st.header("Create an AI Agent from Text")
-
-    st.subheader("From Text")
-    agent_name = st.text_input("Enter agent name:")
-    text_input = st.text_area("Enter skills (one per line):")
-    if st.button("Create Agent"):
-        agent_prompt = create_agent_from_text(agent_name, text_input)
-        st.success(f"Agent '{agent_name}' created and saved successfully.")
-        st.session_state.available_agents.append(agent_name)
-
-elif app_mode == "Tool Box":
-    # Tool Box
-    st.header("AI-Powered Tools")
-
-    # Chat Interface
-    st.subheader("Chat with CodeCraft")
-    chat_input = st.text_area("Enter your message:")
-    if st.button("Send"):
-        if chat_input.startswith("@"):
-            agent_name = chat_input.split(" ")[0][1:]  # Extract agent_name from @agent_name
-            chat_input = " ".join(chat_input.split(" ")[1:])  # Remove agent_name from input
-            chat_response = chat_interface_with_agent(chat_input, agent_name)
+        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)  # Use i_search for web search
+            history.append(("observation: search result is:", response))
         else:
-            chat_response = chat_interface(chat_input)
-        st.session_state.chat_history.append((chat_input, chat_response))
-        st.write(f"CodeCraft: {chat_response}")
-
-    # Terminal Interface
-    st.subheader("Terminal")
-    terminal_input = st.text_input("Enter a command:")
-    if st.button("Run"):
-        terminal_output = terminal_interface(terminal_input)
-        st.session_state.terminal_history.append((terminal_input, terminal_output))
-        st.code(terminal_output, language="bash")
-
-    # Code Editor Interface
-    st.subheader("Code Editor")
-    code_editor = st.text_area("Write your code:", height=300)
-    if st.button("Format & Lint"):
-        formatted_code, lint_message = code_editor_interface(code_editor)
-        st.code(formatted_code, language="python")
-        st.info(lint_message)
-
-    # Text Summarization Tool
-    st.subheader("Summarize Text")
-    text_to_summarize = st.text_area("Enter text to summarize:")
-    if st.button("Summarize"):
-        summary = summarize_text(text_to_summarize)
-        st.write(f"Summary: {summary}")
-
-    # Sentiment Analysis Tool
-    st.subheader("Sentiment Analysis")
-    sentiment_text = st.text_area("Enter text for sentiment analysis:")
-    if st.button("Analyze Sentiment"):
-        sentiment = sentiment_analysis(sentiment_text)
-        st.write(f"Sentiment: {sentiment}")
-
-    # Text Translation Tool (Code Translation)
-    st.subheader("Translate Code")
-    code_to_translate = st.text_area("Enter code to translate:")
-    source_language = st.text_input("Enter source language (e.g. 'Python'):")
-    target_language = st.text_input("Enter target language (e.g. 'JavaScript'):")
-    if st.button("Translate Code"):
-        translated_code = translate_code(code_to_translate, source_language, target_language)
-        st.code(translated_code, language=target_language.lower())
-
-    # Code Generation
-    st.subheader("Code Generation")
-    code_idea = st.text_input("Enter your code idea:")
-    if st.button("Generate Code"):
-        generated_code = generate_code(code_idea)
-        st.code(generated_code, language="python")
-
-    # Display Preset Commands
-    st.subheader("Preset Commands")
-    preset_commands = {
-        "Create a new project": "create_project('project_name')",
-        "Add code to workspace": "add_code_to_workspace('project_name', 'code', 'file_name')",
-        "Run terminal command": "terminal_interface('command', 'project_name')",
-        "Generate code": "generate_code('code_idea')",
-        "Summarize text": "summarize_text('text')",
-        "Analyze sentiment": "sentiment_analysis('text')",
-        "Translate code": "translate_code('code', 'source_language', 'target_language')",
-    }
-    for command_name, command in preset_commands.items():
-        st.write(f"{command_name}: `{command}`")
-
-elif app_mode == "Workspace Chat App":
-    # Workspace Chat App
-    st.header("Workspace Chat App")
-
-    # Project Workspace Creation
-    st.subheader("Create a New Project")
-    project_name = st.text_input("Enter project name:")
-    if st.button("Create Project"):
-        workspace_status = workspace_interface(project_name)
-        st.success(workspace_status)
-
-    # Add Code to Workspace
-    st.subheader("Add Code to Workspace")
-    code_to_add = st.text_area("Enter code to add to workspace:")
-    file_name = st.text_input("Enter file name (e.g. 'app.py'):")
-    if st.button("Add Code"):
-        add_code_status = add_code_to_workspace(project_name, code_to_add, file_name)
-        st.success(add_code_status)
-
-    # Terminal Interface with Project Context
-    st.subheader("Terminal (Workspace Context)")
-    terminal_input = st.text_input("Enter a command within the workspace:")
-    if st.button("Run Command"):
-        terminal_output = terminal_interface(terminal_input, project_name)
-        st.code(terminal_output, language="bash")
-
-    # Chat Interface for Guidance
-    st.subheader("Chat with CodeCraft for Guidance")
-    chat_input = st.text_area("Enter your message for guidance:")
-    if st.button("Get Guidance"):
-        chat_response = chat_interface(chat_input)
-        st.session_state.chat_history.append((chat_input, chat_response))
-        st.write(f"CodeCraft: {chat_response}")
-
-    # Display Chat History
-    st.subheader("Chat History")
-    for user_input, response in st.session_state.chat_history:
-        st.write(f"User: {user_input}")
-        st.write(f"CodeCraft: {response}")
-
-    # Display Terminal History
-    st.subheader("Terminal History")
-    for command, output in st.session_state.terminal_history:
-        st.write(f"Command: {command}")
-        st.code(output, language="bash")
-
-    # Display Projects and Files
-    st.subheader("Workspace Projects")
-    for project, details in st.session_state.workspace_projects.items():
-        st.write(f"Project: {project}")
-        for file in details['files']:
-            st.write(f"  - {file}")
-
-    # Chat with AI Agents
-    st.subheader("Chat with AI Agents")
-    selected_agent = st.selectbox("Select an AI agent", st.session_state.available_agents)
-    agent_chat_input = st.text_area("Enter your message for the agent:")
-    if st.button("Send to Agent"):
-        agent_chat_response = chat_interface_with_agent(agent_chat_input, selected_agent)
-        st.session_state.chat_history.append((agent_chat_input, agent_chat_response))
-        st.write(f"{selected_agent}: {agent_chat_response}")
-
-    # Automate Build Process
-    st.subheader("Automate Build Process")
-    if st.button("Automate"):
-        agent = AIAgent(selected_agent, "", [])  # Load the agent without skills for now
-        summary, next_step = agent.autonomous_build(st.session_state.chat_history, st.session_state.workspace_projects)
-        st.write("Autonomous Build Summary:")
-        st.write(summary)
-        st.write("Next Step:")
-        st.write(next_step)
-
-# Display current state for debugging
-st.sidebar.subheader("Current State")
-st.sidebar.json(st.session_state.current_state)
+            history.append(("observation: I need a valid URL for the SEARCH action.", ""))
+    except Exception as e:
+        history.append(("observation:", str(e)))
+    return "MAIN", None, history, task
+
+def handle_complete_action(action: str, action_input: str, history: List[Tuple[str, str]], task: str, agent: Agent) -> Tuple[str, str, List[Tuple[str, str]], str]:
+    """Handles the COMPLETE action, which ends the current task."""
+    if VERBOSE_LOGGING:
+        logging.info(f"Calling COMPLETE action.")
+    task = "END"
+    return "COMPLETE", "COMPLETE", history, task
+
+# --- Action Mapping ---
+ACTION_HANDLERS: Dict[str, callable] = {
+    "MAIN": handle_main_action,
+    "UPDATE-TASK": handle_update_task_action,
+    "SEARCH": handle_search_action,
+    "COMPLETE": handle_complete_action,
+}
+
+# --- Utility Functions ---
+def run_gpt(prompt: str, stop_tokens: List[str], max_tokens: int) -> str:
+    """Runs the language model and returns the generated text."""
+    if VERBOSE_LOGGING:
+        logging.info(f"Prompt: {prompt}")
+    client = InferenceClient(MODEL_NAME)
+    resp = client.text_generation(prompt, max_new_tokens=max_tokens, stop_sequences=stop_tokens, temperature=0.7, top_p=0.8, repetition_penalty=1.5)
+    if VERBOSE_LOGGING:
+        logging.info(f"Response: {resp}")
+    return resp
+
+def parse_action(line: str) -> Tuple[str, str]:
+    """Parses an action line to get the action name and input."""
+    parts = line.split(":", 1)
+    if len(parts) == 2:
+        action_name = parts[0].replace("action", "").strip()
+        action_input = parts[1].strip()
+    else:
+        action_name = parts[0].replace("action", "").strip()
+        action_input = ""
+    return action_name, action_input
+
+def run_agent(purpose: str, history: List[Tuple[str, str]], agent: Agent) -> List[Tuple[str, str]]:
+    """Runs the agent and returns the updated conversation history."""
+    task = None
+    directory = "./"
+    action_name = "UPDATE-TASK" if task is None else "MAIN"
+    action_input = None
+    while True:
+        if VERBOSE_LOGGING:
+            logging.info(f"---")
+            logging.info(f"Purpose: {purpose}")
+            logging.info(f"Task: {task}")
+            logging.info(f"---")
+            logging.info(f"History: {history}")
+            logging.info(f"---")
+        if VERBOSE_LOGGING:
+            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 history
+            if action_name not in ACTION_HANDLERS:
+                action_name = "MAIN"
+            if action_name == "" or action_name is None:
+                action_name = "MAIN"
+            action_handler = ACTION_HANDLERS[action_name]
+            action_name, action_input, history, task = action_handler(action_name, action_input, history, task, agent)
+            yield history
+            if task == "END":
+                return history
+        except Exception as e:
+            history.append(("observation: the previous command did not produce any useful output, I need to check the commands syntax, or use a different command", ""))
+            logging.error(f"Error in run_agent: {e}")
+            return history
+
+# --- Gradio Interface ---
+def main():
+    with gr.Blocks() as demo:
+        gr.Markdown("## FragMixt: Your No-Code Development Powerhouse")
+        gr.Markdown("### Agents w/ Agents:  Mastering No-Code Development")
+
+        # Chat Interface
+        chatbot = gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel")
+
+        # Input Components
+        message = gr.Textbox(label="Enter your message", placeholder="Ask me anything!")
+        purpose = gr.Textbox(label="Purpose", placeholder="What is the purpose of this interaction?")
+        agent_name = gr.Dropdown(label="Agents", choices=[agent.name for agent in [WebDevAgent(), AiSystemPromptAgent(), PythonCodeDevAgent()]], value=DEFAULT_AGENT, interactive=True)
+        sys_prompt = gr.Textbox(label="System Prompt", max_lines=1, interactive=True)
+        temperature = 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")
+        max_new_tokens = 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")
+        top_p = 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")
+        repetition_penalty = gr.Slider(label="Repetition penalty", value=1.2, minimum=1.0, maximum=2.0, step=0.05, interactive=True, info="Penalize repeated tokens")
+
+        # Button to submit the message
+        submit_button = gr.Button(value="Send")
+
+        # Project Explorer Tab
+        with gr.Tab("Project Explorer"):
+            project_path = gr.Textbox(label="Project Path", placeholder="/home/user/app/current_project")
+            explore_button = gr.Button(value="Explore")
+            project_output = gr.Textbox(label="File Tree", lines=20)
+
+        # Chat App Logic Tab
+        with gr.Tab("Chat App"):
+            history = gr.State([])
+            examples = [
+                ["What is the purpose of this AI agent?", "I am designed to assist with no-code development tasks."],
+                ["Can you help me generate a Python function to calculate the factorial of a number?", "Sure! Here is a Python function to calculate the factorial of a number:"],
+                ["Generate a web page with a heading that says 'Welcome to My Website!'", "action: GENERATE_HTML action_input=a heading that says 'Welcome to My Website!'"],
+            ]
+
+        def chat(purpose, message, agent_name, sys_prompt, temperature, max_new_tokens, top_p, repetition_penalty, history):
+            if agent_name == "WEB_DEV":
+                agent = WebDevAgent()
+            elif agent_name == "AI_SYSTEM_PROMPT":
+                agent = AiSystemPromptAgent()
+            elif agent_name == "PYTHON_CODE_DEV":
+                agent = PythonCodeDevAgent()
+            else:
+                agent = WebDevAgent()  # Default to WEB_DEV if agent_name is invalid
+            history = list(run_agent(purpose, history, agent))
+            return history, history
+
+        submit_button.click(chat, inputs=[purpose, message, agent_name, sys_prompt, temperature, max_new_tokens, top_p, repetition_penalty, history], outputs=[chatbot, history])
+
+    demo.launch()
+
+if __name__ == "__main__":
+    main()