Update app.py

app.py

@@ -1,886 +1,248 @@
 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):
-
-        self.name = name
-
-        self.description = description
-
-        self.skills = skills
-
-
-
-
-    def create_agent_prompt(self):
-
-        skills_str = '\n'.join([f"* {skill}" for skill in self.skills])
-
-        agent_prompt = f"""
-
-As an elite expert developer, my name is {self.name}. I possess a comprehensive understanding of the following areas:
-
-{skills_str}
-
-
-
-I am confident that I can leverage my expertise to assist you in developing and deploying cutting-edge web applications. Please feel free to ask any questions or present any challenges you may encounter.
-
-"""
-
-        return agent_prompt
-
-
-
-
-    def autonomous_build(self, chat_history, workspace_projects):
-
-        """
-
-        Autonomous build logic that continues based on the state of chat history and workspace projects.
-
-        """
-
-        summary = "Chat History:\n" + "\n".join([f"User: {u}\nAgent: {a}" for u, a in chat_history])
-
-        summary += "\n\nWorkspace Projects:\n" + "\n".join([f"{p}: {details}" for p, details in workspace_projects.items()])
-
-
-
-
-        next_step = "Based on the current state, the next logical step is to implement the main application logic."
-
-
-
-
-        return summary, next_step
-
-
-
-
-def save_agent_to_file(agent):
-
-    """Saves the agent's prompt to a file locally and then commits to the Hugging Face repository."""
-
-    if not os.path.exists(AGENT_DIRECTORY):
-
-        os.makedirs(AGENT_DIRECTORY)
-
-    file_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}.txt")
-
-    config_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}Config.txt")
-
-    with open(file_path, "w") as file:
-
-        file.write(agent.create_agent_prompt())
-
-    with open(config_path, "w") as file:
-
-        file.write(f"Agent Name: {agent.name}\nDescription: {agent.description}")
-
-    st.session_state.available_agents.append(agent.name)
-
-
-
-
-    commit_and_push_changes(f"Add agent {agent.name}")
-
-
-
-
-def load_agent_prompt(agent_name):
-
-    """Loads an agent prompt from a file."""
-
-    file_path = os.path.join(AGENT_DIRECTORY, f"{agent_name}.txt")
-
-    if os.path.exists(file_path):
-
-        with open(file_path, "r") as file:
-
-            agent_prompt = file.read()
-
-        return agent_prompt
-
-    else:
-
-        return None
-
-
-
-
-def create_agent_from_text(name, text):
-
-    skills = text.split('\n')
-
-    agent = AIAgent(name, "AI agent created from text input.", skills)
-
-    save_agent_to_file(agent)
-
-    return agent.create_agent_prompt()
-
-
-
-
-# Chat interface using a selected agent
-
-def chat_interface_with_agent(input_text, agent_name):
-
-    agent_prompt = load_agent_prompt(agent_name)
-
-    if agent_prompt is None:
-
-        return f"Agent {agent_name} not found."
-
-
-
-
-    # Load the GPT-2 model which is compatible with AutoModelForCausalLM
-
-    model_name = "gpt2"
-
-    try:
-
-        model = AutoModelForCausalLM.from_pretrained(model_name)
-
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-        generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
-
-    except EnvironmentError as e:
-
-        return f"Error loading model: {e}"
-
-
-
-
-    # Combine the agent prompt with user input
-
-    combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:"
-
+import random
+import json
+from datetime import datetime
+
+from huggingface_hub import (
+    InferenceClient,
+    cached_download,
+    hf_hub_url
+)
+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,
     
-
-    # Truncate input text to avoid exceeding the model's maximum length
-
-    max_input_length = 900
-
-    input_ids = tokenizer.encode(combined_input, return_tensors="pt")
-
-    if input_ids.shape[1] > max_input_length:
-
-        input_ids = input_ids[:, :max_input_length]
-
-
-
-
-    # Generate chatbot response
-
-    outputs = model.generate(
-
-        input_ids, max_new_tokens=50, num_return_sequences=1, do_sample=True, pad_token_id=tokenizer.eos_token_id # Set pad_token_id to eos_token_id
-
-    )
-
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-    return response
-
-
-
-
-def workspace_interface(project_name):
-
-    project_path = os.path.join(PROJECT_ROOT, project_name)
-
-    if not os.path.exists(PROJECT_ROOT):
-
-        os.makedirs(PROJECT_ROOT)
-
-    if not os.path.exists(project_path):
-
-        os.makedirs(project_path)
-
-        st.session_state.workspace_projects[project_name] = {"files": []}
-
-        st.session_state.current_state['workspace_chat']['project_name'] = project_name
-
-        commit_and_push_changes(f"Create project {project_name}")
-
-        return f"Project {project_name} created successfully."
-
-    else:
-
-        return f"Project {project_name} already exists."
-
-
-
-
-def add_code_to_workspace(project_name, code, file_name):
-
-    project_path = os.path.join(PROJECT_ROOT, project_name)
-
-    if os.path.exists(project_path):
-
-        file_path = os.path.join(project_path, file_name)
-
-        with open(file_path, "w") as file:
-
-            file.write(code)
-
-        st.session_state.workspace_projects[project_name]["files"].append(file_name)
-
-        st.session_state.current_state['workspace_chat']['added_code'] = {"file_name": file_name, "code": code}
-
-        commit_and_push_changes(f"Add code to {file_name} in project {project_name}")
-
-        return f"Code added to {file_name} in project {project_name} successfully."
-
-    else:
-
-        return f"Project {project_name} does not exist."
-
-
-
-
-def terminal_interface(command, project_name=None):
-
-    if project_name:
-
-        project_path = os.path.join(PROJECT_ROOT, project_name)
-
-        if not os.path.exists(project_path):
-
-            return f"Project {project_name} does not exist."
-
-        result = subprocess.run(command, cwd=project_path, shell=True, capture_output=True, text=True)
-
-    else:
-
-        result = subprocess.run(command, shell=True, capture_output=True, text=True)
-
-    if result.returncode == 0:
-
-        st.session_state.current_state['toolbox']['terminal_output'] = result.stdout
-
-        return result.stdout
-
-    else:
-
-        st.session_state.current_state['toolbox']['terminal_output'] = result.stderr
-
-        return result.stderr
-
-
-
-
-def code_editor_interface(code):
-
-    try:
-
-        formatted_code = black.format_str(code, mode=black.FileMode())
-
-    except black.NothingChanged:
-
-        formatted_code = code
-
-    result = StringIO()
-
-    sys.stdout = result
-
-    sys.stderr = result
-
-    (pylint_stdout, pylint_stderr) = lint.py_run(code, return_std=True)
-
-    sys.stdout = sys.__stdout__
-
-    sys.stderr = sys.__stderr__
-
-    lint_message = pylint_stdout.getvalue() + pylint_stderr.getvalue()
-
-    st.session_state.current_state['toolbox']['formatted_code'] = formatted_code
-
-    st.session_state.current_state['toolbox']['lint_message'] = lint_message
-
-    return formatted_code, lint_message
-
-
-
-
-def summarize_text(text):
-
-    summarizer = pipeline("summarization")
-
-    summary = summarizer(text, max_length=50, min_length=25, do_sample=False)
-
-    st.session_state.current_state['toolbox']['summary'] = summary[0]['summary_text']
-
-    return summary[0]['summary_text']
-
-
-
-
-def sentiment_analysis(text):
-
-    analyzer = pipeline("sentiment-analysis")
-
-    sentiment = analyzer(text)
-
-    st.session_state.current_state['toolbox']['sentiment'] = sentiment[0]
-
-    return sentiment[0]
-
-
-
-
-def translate_code(code, input_language, output_language):
-
-    # Define a dictionary to map programming languages to their corresponding file extensions
-
-    language_extensions = {
-
-        # ignore the specific languages right now, and continue to EOF
-
+    LOG_PROMPT,
+    LOG_RESPONSE,
+    MODIFY_PROMPT,
+    PRE_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 
+import json
+
+#--- Global Variables for App State ---
+app_state = {"components": []} 
+
+terminal_history = ""
+#--- Component Library ---
+components_registry = { "Button": { "properties": {"label": "Click Me", "onclick": ""}, "description": "A clickable button", "code_snippet": 'gr.Button(value="{label}", variant="primary")', }, "Text Input": { "properties": {"value": "", "placeholder": "Enter text"}, "description": "A field for entering text", "code_snippet": 'gr.Textbox(label="{placeholder}")', }, "Image": { "properties": {"src": "#", "alt": "Image"}, "description": "Displays an image", "code_snippet": 'gr.Image(label="{alt}")', }, "Dropdown": { "properties": {"choices": ["Option 1", "Option 2"], "value": ""}, "description": "A dropdown menu for selecting options", "code_snippet": 'gr.Dropdown(choices={choices}, label="Dropdown")', }, # Add more components here... }
+
+#--- NLP Model (Example using Hugging Face) ---
+nlp_model_name = "google/flan-t5-small"
+
+# Check if the model exists in the cache
+try: cached_download(hf_hub_url(nlp_model_name, revision="main")) nlp_model = InferenceClient(nlp_model_name) except: nlp_model = None
+
+#--- Function to get NLP model response ---
+def get_nlp_response(input_text): if nlp_model: response = nlp_model.text_generation(input_text) return response.generated_text else: return "NLP model not available."
+
+# --- Component Class ---
+class Component: def init(self, type, properties=None, id=None): self.id = id or random.randint(1000, 9999) self.type = type self.properties = properties or components_registry[type]["properties"].copy()
+
+def to_dict(self):
+    return {
+        "id": self.id,
+        "type": self.type,
+        "properties": self.properties,
     }
 
+def render(self):
+    # Properly format choices for Dropdown
+    if self.type == "Dropdown":
+        self.properties["choices"] = (
+            str(self.properties["choices"])
+            .replace("[", "")
+            .replace("]", "")
+            .replace("'", "")
+        )
+    return components_registry[self.type]["code_snippet"].format(
+        **self.properties
+    )
+# --- Function to update the app canvas (for preview) ---
+def update_app_canvas(): components_html = "".join( [ f"<div>Component ID: {component['id']}, Type: {component['type']}, Properties: {component['properties']}</div>" for component in app_state["components"] ] ) return components_html
+
+# --- Function to handle component addition ---
+def add_component(component_type): if component_type in components_registry: new_component = Component(component_type) app_state["components"].append(new_component.to_dict()) return ( update_app_canvas(), f"System: Added component: {component_type}\n", ) else: return None, f"Error: Invalid component type: {component_type}\n"
+
+# --- Function to handle terminal input ---
+def run_terminal_command(command, history): global terminal_history output = "" try: # Basic command parsing (expand with NLP) if command.startswith("add "): component_type = command.split("add ", 1)[1].strip() _, output = add_component(component_type) elif command.startswith("set "): _, output = set_component_property(command) elif command.startswith("search "): search_query = command.split("search ", 1)[1].strip() output = i_s(search_query) elif command.startswith("deploy "): app_name = command.split("deploy ", 1)[1].strip() output = deploy_to_huggingface(app_name) else: # Attempt to execute command as Python code try: result = subprocess.check_output( command, shell=True, stderr=subprocess.STDOUT, text=True ) output = result except Exception as e: output = f"Error executing Python code: {str(e)}" except Exception as e: output = f"Error: {str(e)}" finally: terminal_history += f"User: {command}\n" terminal_history += f"{output}\n" return terminal_history
+
+def set_component_property(command): try: # Improved 'set' command parsing set_parts = command.split(" ", 2)[1:] if len(set_parts) != 2: raise ValueError("Invalid 'set' command format.")
+
+component_id = int(set_parts[0])  # Use component ID
+    property_name, property_value = set_parts[1].split("=", 1)
+
+    # Find component by ID
+    component_found = False
+    for component in app_state["components"]:
+        if component["id"] == component_id:
+            if property_name in component["properties"]:
+                component["properties"][
+                    property_name.strip()
+                ] = property_value.strip()
+                component_found = True
+                return (
+                    update_app_canvas(),
+                    f"System: Property '{property_name}' set to '{property_value}' for component {component_id}\n",
+                )
+            else:
+                return (
+                    None,
+                    f"Error: Property '{property_name}' not found in component {component_id}\n",
+                )
+    if not component_found:
+        return (
+            None,
+            f"Error: Component with ID {component_id} not found.\n",
+        )
+
+except Exception as e:
+    return None, f"Error: Invalid 'set' command format or error setting property: {str(e)}\n"
+#--- Function to handle chat interaction ---
+def run_chat(message, history): global terminal_history if message.startswith("!"): command = message[1:] terminal_history = run_terminal_command(command, history) return history, terminal_history else: # ... (Your regular chat response generation) return history, terminal_history
+
+# --- Code Generation ---
+def generate_python_code(app_name): code = f""" import gradio as gr
+Define your Gradio components here
+with gr.Blocks() as {app_name}: """ for component in app_state["components"]: code += " " + Component(**component).render() + "\n"
+
+code += f"""
+{app_name}.launch() """ return code
+
+# --- Save/Load App State ---
+def save_app_state(filename="app_state.json"): with open(filename, "w") as f: json.dump(app_state, f)
+
+# --- Hugging Face Deployment --- def deploy_to_huggingface(app_name): # Generate Python code code = generate_python_code(app_name)
+def load_app_state(filename="app_state.json"): global app_state try: with open(filename, "r") as f: app_state = json.load(f) except FileNotFoundError: print("App state file not found. Starting with a blank slate.")
+
+# Create requirements.txt
+with open("requirements.txt", "w") as f:
+    f.write("gradio==3.32.0\n")
+
+# Create the app.py file
+with open("app.py", "w") as f:
+    f.write(code)
+
+# Execute the deployment command
+try:
+    subprocess.run(
+        [
+            "huggingface-cli",
+            "repo",
+            "create",
+            "--type",
+            "space",
+            "--space_sdk",
+            "gradio",
+            app_name,
+        ],
+        check=True,
+    )
+    subprocess.run(
+        ["git", "init"], cwd=f"./{app_name}", check=True
+    )
+    subprocess.run(
+        ["git", "add", "."], cwd=f"./{app_name}", check=True
+    )
+    subprocess.run(
+        ['git', 'commit', '-m', '"Initial commit"'], cwd=f"./{user_name}/{app_name}", check=True
+    )
+    subprocess.run(
+        ["git", "push", "https://huggingface.co/spaces/" + app_name, "main"], cwd=f"./{app_name}", check=True
+    )
+    return (
+        f"Successfully deployed to Hugging Face Spaces: https://huggingface.co/spaces/{app_name}"
+    )
+except Exception as e:
+    return f"Error deploying to Hugging Face Spaces: {e}"
+--- Gradio Interface ---
+with gr.Blocks() as iface: with gr.Row(): # --- Chat Interface --- chat_history = gr.Chatbot(label="Chat with Agent") chat_input = gr.Textbox(label="Your Message") chat_button = gr.Button("Send")
+
+chat_button.click(
+        run_chat,
+        inputs=[chat_input, chat_history],
+        outputs=[chat_history, terminal_output],
+    )
 
+with gr.Row():
+    # --- App Builder Section ---
+    app_canvas = gr.HTML(
+        "<div>App Canvas Preview:</div>", label="App Canvas"
+    )
+    with gr.Column():
+        component_list = gr.Dropdown(
+            choices=list(components_registry.keys()), label="Components"
+        )
+        add_button = gr.Button("Add Component")
+
+        add_button.click(
+            add_component,
+            inputs=component_list,
+            outputs=[app_canvas, terminal_output],
+        )
+
+with gr.Row():
+    # --- Terminal ---
+    terminal_output = gr.Textbox(
+        lines=8, label="Terminal", value=terminal_history
+    )
+    terminal_input = gr.Textbox(label="Enter Command")
+    terminal_button = gr.Button("Run")
 
+    terminal_button.click(
+        run_terminal_command,
+        inputs=[terminal_input, terminal_output],
+        outputs=terminal_output,
+    )
 
-    # 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}"}
-
-        ]
+with gr.Row():
+    # --- Code Generation ---
+    code_output = gr.Code(
+        generate_python_code("app_name"),
+        language="python",
+        label="Generated Code",
+    )
+    app_name_input = gr.Textbox(label="App Name")
+    generate_code_button = gr.Button("Generate Code")
+    generate_code_button.click(
+        generate_python_code,
+        inputs=[app_name_input],
+        outputs=code_output,
+    )
 
+with gr.Row():
+    # --- Save/Load Buttons ---
+    save_button = gr.Button("Save App State")
+    load_button = gr.Button("Load App State")
+
+    save_button.click(save_app_state)
+    load_button.click(load_app_state)
+
+with gr.Row():
+    # --- Deploy Button ---
+    deploy_button = gr.Button("Deploy to Hugging Face")
+    deploy_output = gr.Textbox(label="Deployment Output")
+    deploy_button.click(
+        deploy_to_huggingface,
+        inputs=[app_name_input],
+        outputs=[deploy_output],
     )
-
-    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)
-
-        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)
+iface.launch()