Update app.py

app.py

@@ -1,159 +1,72 @@
 import os
-import sys
 import subprocess
-import base64
-import json
-from io import StringIO
-from typing import Dict, List
-
 import streamlit as st
-import torch
-from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer, HfApi
-from pylint import lint
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 import black
+from pylint import lint
+from io import StringIO
 
-from setuptools import setup, Extension
-from Cython.Build import cythonize
-
-extensions = [
-    Extension("app", ["app.pyx"]),
-]
-
-setup(
-    ext_modules=cythonize(extensions),
-)
-
-# Add your Hugging Face API token here
-hf_token = st.secrets["huggingface"]
-
-# Constants
-PROJECT_ROOT = "./projects"
-AGENT_DIRECTORY = "./agents"
-AVAILABLE_CODE_GENERATIVE_MODELS = ["codegen", "gpt-neo", "codeparrot"]
+HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/DevToolKit"
+PROJECT_ROOT = "projects"
+AGENT_DIRECTORY = "agents"
 
-# Global state management
-if "chat_history" not in st.session_state:
+# 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:
+if 'terminal_history' not in st.session_state:
     st.session_state.terminal_history = []
-if "workspace_projects" not in st.session_state:
+if 'workspace_projects' not in st.session_state:
     st.session_state.workspace_projects = {}
-if "available_agents" not in st.session_state:
+if 'available_agents' not in st.session_state:
     st.session_state.available_agents = []
-
-# Load pre-trained models
-@st.cache(allow_output_mutation=True)
-def load_models():
-    rag_retriever = pipeline("question-answering", model="facebook/rag-token-nq")
-    chat_model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/DialoGPT-medium")
-    tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
-    return rag_retriever, chat_model, tokenizer
-
-rag_retriever, chat_model, tokenizer = load_models()
-
-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():
-        rag_output = rag_retriever(question=user_input, context=user_input)
-        rag_answer = rag_output['answer']
-
-    # Chat model: Refine response
-    chat_input = tokenizer(rag_answer, return_tensors="pt")
-    with torch.no_grad():
-        chat_output = chat_model.generate(**chat_input)
-        refined_response = tokenizer.decode(chat_output[0], skip_special_tokens=True)
-
-    return refined_response
+if 'current_state' not in st.session_state:
+    st.session_state.current_state = {
+        'toolbox': {},
+        'workspace_chat': {}
+    }
 
 class AIAgent:
-    def __init__(self, name: str, description: str, skills: List[str], hf_api=None):
+    def __init__(self, name, description, skills):
         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)
 
     def create_agent_prompt(self):
-        return f"Name: {self.name}\nDescription: {self.description}\nSkills:\n" + "\n".join(self.skills)
+        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
 
-    async def autonomous_build(self, chat_history: List[str], workspace_projects: Dict[str, str], project_name: str, selected_model: str):
-        summary = "Chat History:\n" + "\n".join(chat_history)
-        summary += "\n\nWorkspace Projects:\n" + "\n".join(workspace_projects.items())
+    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."
 
-        project_path = os.path.join(PROJECT_ROOT, project_name)
-        if not os.path.exists(project_path):
-            os.makedirs(project_path)
-
-        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")
-
-        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)
-
-        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 deploy_built_space_to_hf(self):
-        if not self.has_valid_hf_token():
-            st.error("Invalid Hugging Face token. Please check your configuration.")
-            return
-
-        try:
-            files = get_built_space_files()
-            create_space_on_hugging_face(self.hf_api, self.name, self.description, True, files)
-            st.success(f"Successfully deployed {self.name} to Hugging Face Spaces!")
-        except Exception as e:
-            st.error(f"Error deploying to Hugging Face Spaces: {str(e)}")
-
-def get_built_space_files() -> Dict[str, str]:
-    return {
-        "app.py": "# Your Streamlit app code here",
-        "requirements.txt": "streamlit\ntransformers"
-    }
-
-def save_agent_to_file(agent: AIAgent):
+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)
 
-def load_agent_prompt(agent_name: str) -> str:
+    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:
@@ -162,127 +75,177 @@ def load_agent_prompt(agent_name: str) -> str:
     else:
         return None
 
-def create_agent_from_text(name: str, text: str) -> str:
-    skills = text.split("\n")
+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()
 
-def chat_interface_with_agent(input_text: str, agent_name: str) -> str:
+# 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."
 
-    model_name = "microsoft/DialoGPT-medium"
+    # Load the GPT-2 model which is compatible with AutoModelForCausalLM
+    model_name = "gpt2"
     try:
-        generator = pipeline("text-generation", model=model_name)
-        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:
+        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}"
 
-def terminal_interface(command: str, project_name: str = None) -> str:
+    # 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, shell=True, capture_output=True, text=True, cwd=project_path)
+        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)
-    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
-
-    lint.Run(['--rcfile=/dev/null', '-'], exit=False)
-    lint_message = result.getvalue()
-
-    sys.stdout = sys.__stdout__
-    sys.stderr = sys.__stderr__
-
-    return formatted_code, lint_message
+    if result.returncode == 0:
+        st.session_state.current_state['toolbox']['terminal_output'] = result.stdout
+        return result.stdout
+    else:
+        st.session_state.current_state['toolbox']['terminal_output'] = result.stderr
+        return result.stderr
 
-def summarize_text(text: str) -> str:
+def summarize_text(text):
     summarizer = pipeline("summarization")
-    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
+    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: str) -> str:
+def sentiment_analysis(text):
     analyzer = pipeline("sentiment-analysis")
-    result = analyzer(text)
-    return result[0]['label']
+    sentiment = analyzer(text)
+    st.session_state.current_state['toolbox']['sentiment'] = sentiment[0]
+    return sentiment[0]
+
+# ... [rest of the translate_code function, but remove the OpenAI API call and replace it with your own logic] ...
+
+def generate_code(code_idea):
+    # Replace this with a call to a Hugging Face model or your own logic
+    # For example, using a text-generation pipeline:
+    generator = pipeline('text-generation', model='gpt4o')
+    generated_code = generator(code_idea, max_length=10000, num_return_sequences=1)[0]['generated_text']
+    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}"}
+        ]
+    st.session_state.current_state['toolbox']['generated_code'] = generated_code
+    
+    return generated_code
+    
+def translate_code(code, input_language, output_language):
+    # Define a dictionary to map programming languages to their corresponding file extensions
+    language_extensions = {
+        
+    }
 
-def translate_code(code: str, source_language: str, target_language: str) -> str:
-    translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ROMANCE")
-    translated_code = translator(code, max_length=512)[0]['translation_text']
+    # 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: str, model_name: str) -> str:
-    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:
-    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="app.py"):
-    try:
-        repo = api.create_repo(name, exist_ok=True, private=not public)
-        for filename, content in files.items():
-            api.upload_file(
-                path_or_fileobj=content.encode(),
-                path_in_repo=filename,
-                repo_id=repo.repo_id,
-                repo_type="space",
-            )
-        return repo
-    except Exception as e:
-        st.error(f"Error creating Hugging Face Space: {str(e)}")
-        return None
+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"])
+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")
@@ -294,23 +257,31 @@ if app_mode == "AI Agent Creator":
         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)
+        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.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"):
@@ -318,42 +289,121 @@ elif app_mode == "Tool Box":
         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'):")
+    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)
+        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, "gpt2")
+        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:")
+    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")