Update app.py

app.py

@@ -1,181 +1,411 @@
 import os
-
-import streamlit as st
 import subprocess
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, AutoModel, RagRetriever, AutoModelForSeq2SeqLM
+import streamlit as st
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 import black
 from pylint import lint
-import sys
-import torch
-from huggingface_hub import hf_hub_url, cached_download, HfApi
-import base64
-
-# Set your Hugging Face API key here
-# hf_token = "YOUR_HUGGING_FACE_API_KEY"  # Replace with your actual token
-# Get Hugging Face token from secrets.toml - this line should already be in the main code
-hf_token = st.secrets["huggingface"]["hf_token"]
+from io import StringIO
 
 HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/DevToolKit"
 PROJECT_ROOT = "projects"
-    return refined_response
+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, 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  # Store the token here
 
-    @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 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}
 
-    def has_valid_hf_token(self):
-        return bool(self._hf_token)
-
-    async def autonomous_build(self, chat_history, workspace_projects, project_name, selected_model, hf_token):
-        self._hf_token = hf_token
-        # Continuation of previous methods
+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()])
 
-            st.error(f"Build Error: {e}")
+        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:"
     
-    def deploy_built_space_to_hf(self):
-        if not self._hf_api or not self._hf_token:
-            raise ValueError("Cannot deploy the Space since no valid Hugoging Face API connection was established.")
+    # 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]
 
-        # Assuming you have a function to get the files for your Space
-        repository_name = f"my-awesome-space_{datetime.now().timestamp()}" 
-        files = get_built_space_files() # Placeholder - you'll need to define this function
+    # 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
 
-        # Create the Space
-        create_space(self.hf_api, repository_name, "Description", True, files) 
+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."
 
-        st.markdown("## Congratulations! Successfully deployed Space 🚀 ##")
-        st.markdown(f"[Check out your new Space here](https://huggingface.co/spaces/{repository_name})")
+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
 
-# Add any missing functions from your original code (e.g., get_built_space_files)
-def get_built_space_files():
-    # Replace with your logic to gather the files you want to deploy
-    return {
-        "app.py": "# Your Streamlit app code here",
-        "requirements.txt": "streamlit\ntransformers" 
-        # Add other files as needed
-    }
+def 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 save_agent_to_file(agent):
-    """Saves the agent's prompt to a file."""
-    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(api, name, description, public, files, entrypoint="launch.py"):
-    url = f"{hf_hub_url()}spaces/{name}/prepare-repo"
-    headers = {"Authorization": f"Bearer {api.access_token}"}
-    payload = {
-        "public": public,
-        "gitignore_template": "web",
-        "default_branch": "main",
-        "archived": False,
-        "files": []
+def sentiment_analysis(text):
+    analyzer = pipeline("sentiment-analysis")
+    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 = {
+        
     }
-    for filename, contents in files.items():
-        data = {
-            "content": contents,
-            "path": filename,
-            "encoding": "utf-8",
-            "mode": "overwrite" if "#\{random.randint(0, 1)\}" not in contents else "merge",
-        }
-        payload["files"].append(data)
-    response = requests.post(url, json=payload, headers=headers)
-    response.raise_for_status()
-    location = response.headers.get("Location")
-    # wait_for_processing(location, api)  # You might need to implement this if it's not already defined
-
-    return Repository(name=name, api=api)
+
+    # 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")
 
-elif app_mode == "Workspace Chat App":
-    # Workspace Chat App
-    st.header("Workspace Chat App")
-def get_built_space_files():
-    """
-    Gathers the necessary files for the Hugging Face Space, 
-    handling different project structures and file types.
-    """
-    files = {}
-
-    # Get the current project name (adjust as needed)
-    project_name = st.session_state.get('project_name', 'my_project') 
-    project_path = os.path.join(PROJECT_ROOT, project_name)
+# Sidebar navigation
+st.sidebar.title("Navigation")
+app_mode = st.sidebar.selectbox("Choose the app mode", ["AI Agent Creator", "Tool Box", "Workspace Chat App"])
 
-    # Define a list of files/directories to search for
-    targets = [
-        "app.py", 
-        "requirements.txt",
-        "Dockerfile",  
-        "docker-compose.yml", # Example YAML file
-        "src",          # Example subdirectory
-        "assets"        # Another example subdirectory
-    ]
+if app_mode == "AI Agent Creator":
+    # AI Agent Creator
+    st.header("Create an AI Agent from Text")
 
-    # Iterate through the targets
-    for target in targets:
-        target_path = os.path.join(project_path, target)
+    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)
 
-        # If the target is a file, add it to the files dictionary
-        if os.path.isfile(target_path):
-            add_file_to_dictionary(files, target_path)
+elif app_mode == "Tool Box":
+    # Tool Box
+    st.header("AI-Powered Tools")
 
-        # If the target is a directory, recursively search for files within it
-        elif os.path.isdir(target_path):
-            for root, _, filenames in os.walk(target_path):
-                for filename in filenames:
-                    file_path = os.path.join(root, filename)
-                    add_file_to_dictionary(files, file_path)
+    # 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}")
 
-    return files
+    # 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")
 
-def add_file_to_dictionary(files, file_path):
-    """Helper function to add a file to the files dictionary."""
-    filename = os.path.relpath(file_path, PROJECT_ROOT) # Get relative path
+    # 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")
 
-    # Handle text and binary files
-    if filename.endswith((".py", ".txt", ".json", ".html", ".css", ".yml", ".yaml")):
-        with open(file_path, "r") as f:
-            files[filename] = f.read()
-    else:
-        with open(file_path, "rb") as f:
-            file_content = f.read()
-            files[filename] = base64.b64encode(file_content).decode("utf-8")
     # 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)
-    
-    # Using the modified and extended class and functions, update the callback for the 'Automate' button in the Streamlit UI:
-    if st.button("Automate", args=(hf_token,)):
-    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, project_name, selected_model, hf_token)
-    st.write("Autonomous Build Summary:")
-    st.write(summary)
-    st.write("Next Step:")
-    st.write(next_step)
-
-    # If everything went well, proceed to deploy the Space
-    if agent._hf_api and agent.has_valid_hf_token():
-        agent.deploy_built_space_to_hf() 
-        # Use the hf_token to interact with the Hugging Face API
-        api = HfApi(token=hf_token)
-        # Function to create a Space on Hugging Face
+
+# Display current state for debugging
+st.sidebar.subheader("Current State")
+st.sidebar.json(st.session_state.current_state)