Update app.py

app.py

@@ -1,367 +1,222 @@
-from huggingface_hub import InferenceClient
-import gradio as gr
-import random
-import os
-import subprocess
-from typing import Dict, Tuple, List
-import dotenv
-import json
-from rich import print as rprint
-from rich.panel import Panel
-from rich.progress import track
-from rich.table import Table
-from rich.prompt import Prompt, Confirm
-from rich.markdown import Markdown
-from rich.traceback import install
-install()  # Enable rich tracebacks for easier debugging
-
-from huggingface_hub import RepositoryInfo
-
-model_info = RepositoryInfo.fetch('huggingface/models/{}/{}'.format(" Meta-Llama-3.1-8B ", MODEL_NAME))
-client = InferenceClient(model_info, token=os.environ.get("HF_TOKEN"))
-
-# Chat Interface Parameters
-DEFAULT_TEMPERATURE = 0.9
-DEFAULT_MAX_NEW_TOKENS = 2048
-DEFAULT_TOP_P = 0.95
-DEFAULT_REPETITION_PENALTY = 1.2
-
-# Local Server
-LOCAL_HOST_PORT = 7860
-
-# --- Agent Roles ---
-
-agent_roles: Dict[str, Dict[str, bool]] = {
-    "Web Developer": {"description": "A master of front-end and back-end web development.", "active": False},
-    "Prompt Engineer": {"description": "An expert in crafting effective prompts for AI models.", "active": False},
-    "Python Code Developer": {"description": "A skilled Python programmer who can write clean and efficient code.", "active": False},
-    "Hugging Face Hub Expert": {"description": "A specialist in navigating and utilizing the Hugging Face Hub.", "active": False},
-    "AI-Powered Code Assistant": {"description": "An AI assistant that can help with coding tasks and provide code snippets.", "active": False},
-}
-
-# --- Initial Prompt ---
-
-initial_prompt = """
-You are an expert agent cluster, consisting of a Web Developer, a Prompt Engineer, a Python Code Developer, a Hugging Face Hub Expert, and an AI-Powered Code Assistant.
-Respond with complete program coding to client requests. 
-Use your combined expertise to research information and explain it clearly.
-Don't answer solely based on what you already know. Always perform a search before providing a response.
-In special cases, like when the user specifies a page to read, there's no need to search.
-Read the provided page and answer the user's question accordingly.
-If you find limited information from search results, try these options:
-- Click on the links of the search results to access and read the content of each page.
-- Change your search query and perform a new search.
-Users are busy, so provide direct answers.
-BAD ANSWER EXAMPLE
-- Please refer to these pages.
-- You can write code referring to these pages.
-- The following page will be helpful.
-GOOD ANSWER EXAMPLE
-- This is the complete code:  -- complete code here --
-- The answer to your question is -- answer here --
-List the URLs of the pages you referenced at the end of your answer for verification.
-Answer in the language used by the user. If the user asks in Japanese, answer in Japanese. If the user asks in Spanish, answer in Spanish.
-Search in English, especially for programming-related questions. ALWAYS SEARCH IN ENGLISH FOR THOSE.
-"""
-
-# --- Custom CSS ---
-
-customCSS = """
-#component-7 { 
-  height: 1600px; 
-  flex-grow: 4;
-}
-.gradio-container {
-  display: flex;
-  flex-direction: column;
-  height: 100vh;
-}
-.gradio-interface {
-  flex-grow: 1;
-  display: flex;
-  flex-direction: column;
-}
-"""
-
-# --- Functions ---
-
-# Function to toggle the active state of an agent
-def toggle_agent(agent_name: str) -> str:
-    """Toggles the active state of an agent."""
-    global agent_roles
-    agent_roles[agent_name]["active"] = not agent_roles[agent_name]["active"]
-    return f"{agent_name} is now {'active' if agent_roles[agent_name]['active'] else 'inactive'}"
-
-# Function to get the active agent cluster
-def get_active_agents() -> List[str]:
-    """Returns a list of active agents."""
-    return [agent for agent, is_active in agent_roles.items() if is_active]
-
-# Function to execute code
-def run_code(code: str) -> str:
-    """Executes the provided code and returns the output."""
-    try:
-        output = subprocess.check_output(
-            ['python', '-c', code],
-            stderr=subprocess.STDOUT,
-            universal_newlines=True,
-        )
-        return output
-    except subprocess.CalledProcessError as e:
-        return f"Error: {e.output}"
-
-# Function to format the prompt
-def format_prompt(message: str, history: list[Tuple[str, str]], agent_roles: list[str]) -> str:
-    """Formats the prompt with the selected agent roles and conversation history."""
-    prompt = initial_prompt  # Use the global initial prompt
-
-    for user_prompt, bot_response in history:
-        prompt += f"[INST] {user_prompt} [/INST]"
-        prompt += f" {bot_response}</s> "
-    
-    prompt += f"[INST] {message} [/INST]"
-    return prompt
-
-# Function to generate a response
-def generate(prompt: str, history: list[Tuple[str, str]], agent_roles: list[str], temperature: float = DEFAULT_TEMPERATURE, max_new_tokens: int = DEFAULT_MAX_NEW_TOKENS, top_p: float = DEFAULT_TOP_P, repetition_penalty: float = DEFAULT_REPETITION_PENALTY) -> str:
-    """Generates a response using the selected agent roles and parameters."""
-    temperature = float(temperature)
-    if temperature < 1e-2:
-        temperature = 1e-2
-    top_p = float(top_p)
-
-    generate_kwargs = dict(
-        temperature=temperature,
-        max_new_tokens=max_new_tokens,
-        top_p=top_p,
-        repetition_penalty=repetition_penalty,
-        do_sample=True,
-        seed=random.randint(0, 10**7),
-    )
-
-    formatted_prompt = format_prompt(prompt, history, agent_roles)
-
-    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
-    output = ""
-
-    for response in stream:
-        output += response.token.text
-        yield output
-    return output
-
-# Function to handle user input and generate responses
-def chat_interface(message: str, history: list[Tuple[str, str]], temperature: float, max_new_tokens: int, top_p: float, repetition_penalty: float) -> Tuple[str, str]:
-    """Handles user input and generates responses."""
-    if message.startswith("python"): 
-        # User entered code, execute it 
-        code = message[9:-3] 
-        output = run_code(code) 
-        return (message, output) 
-    else:
-        # User entered a normal message, generate a response
-        active_agents = get_active_agents()
-        response = generate(message, history, active_agents, temperature, max_new_tokens, top_p, repetition_penalty)
-        return (message, response)
-
-# Function to create a new web app instance
-def create_web_app(app_name: str, code: str) -> None:
-    """Creates a new web app instance with the given name and code."""
-    # Create a new directory for the app
-    os.makedirs(app_name, exist_ok=True)
-
-    # Create the app.py file
-    with open(os.path.join(app_name, 'app.py'), 'w') as f:
-        f.write(code)
-
-    # Create the requirements.txt file
-    with open(os.path.join(app_name, 'requirements.txt'), 'w') as f:
-        f.write("gradio\nhuggingface_hub\nrich")
-
-    # Print a success message
-    print(f"Web app '{app_name}' created successfully!")
-
-# Function to handle the "Create Web App" button click
-def create_web_app_button_click(app_name: str, code: str) -> str:
-    """Handles the "Create Web App" button click."""
-    # Validate the app name
-    if not app_name:
-        return "Please enter a valid app name."
-
-    # Create the web app instance
-    create_web_app(app_name, code)
-
-    # Return a success message
-    return f"Web app '{app_name}' created successfully!"
-
-# Function to handle the "Deploy" button click
-def deploy_button_click(app_name: str, code: str) -> str:
-    """Handles the "Deploy" button click."""
-    # Validate the app name
-    if not app_name:
-        return "Please enter a valid app name."
-
-    # Deploy the web app instance
-    # ... (Implement deployment logic here)
-
-    # Return a success message
-    return f"Web app '{app_name}' deployed successfully!"
-
-# Function to handle the "Local Host" button click
-def local_host_button_click(app_name: str, code: str) -> str:
-    """Handles the "Local Host" button click."""
-    # Validate the app name
-    if not app_name:
-        return "Please enter a valid app name."
-
-    # Start the local server
-    os.chdir(app_name)
-    subprocess.Popen(['gradio', 'run', 'app.py', '--share', '--server_port', str(LOCAL_HOST_PORT)])
-
-    # Return a success message
-    return f"Web app '{app_name}' running locally on port {LOCAL_HOST_PORT}!"
-
-# Function to handle the "Ship" button click
-def ship_button_click(app_name: str, code: str) -> str:
-    """Handles the "Ship" button click."""
-    # Validate the app name
-    if not app_name:
-        return "Please enter a valid app name."
-
-    # Ship the web app instance
-    # ... (Implement shipping logic here)
-
-    # Return a success message
-    return f"Web app '{app_name}' shipped successfully!"
-
-# --- Gradio Interface ---
-
-with gr.Blocks(css=customCSS, theme='ParityError/Interstellar') as demo:
-    gr.Markdown(
-        """
-        # AI-Powered Code Generation and Web App Creation
-        This application allows you to interact with an AI agent cluster to generate code and create web apps.
-        """
-    )
-
-    # --- Agent Selection ---
-    with gr.Row():
-        gr.Markdown("## Select Your Agent Cluster")
-        for agent_name, agent_data in agent_roles.items():
-            button = gr.Button(agent_name, variant="secondary")
-            textbox = gr.Textbox(agent_data["description"], interactive=False)
-            button.click(toggle_agent, inputs=[button], outputs=[textbox])
-
-    # --- Chat Interface ---
-    with gr.Row():
-        gr.Markdown("## Chat with the AI")
-        chatbot = gr.Chatbot()
-        chat_interface_input = gr.Textbox(label="Enter your message", placeholder="Ask me anything!")
-
-        # Parameters
-        with gr.Accordion("Advanced Parameters", open=False):
-            temperature_slider = gr.Slider(
-                label="Temperature",
-                value=DEFAULT_TEMPERATURE,
-                minimum=0.0,
-                maximum=1.0,
-                step=0.05,
-                interactive=True,
-                info="Higher values generate more diverse outputs",
-            )
-            max_new_tokens_slider = gr.Slider(
-                label="Maximum New Tokens",
-                value=DEFAULT_MAX_NEW_TOKENS,
-                minimum=64,
-                maximum=4096,
-                step=64,
-                interactive=True,
-                info="The maximum number of new tokens",
-            )
-            top_p_slider = gr.Slider(
-                label="Top-p (Nucleus Sampling)",
-                value=DEFAULT_TOP_P,
-                minimum=0.0,
-                maximum=1,
-                step=0.05,
-                interactive=True,
-                info="Higher values sample more low-probability tokens",
-            )
-            repetition_penalty_slider = gr.Slider(
-                label="Repetition Penalty",
-                value=DEFAULT_REPETITION_PENALTY,
-                minimum=1.0,
-                maximum=2.0,
-                step=0.05,
-                interactive=True,
-                info="Penalize repeated tokens",
-            )
-
-        # Submit Button
-        submit_button = gr.Button("Submit")
-
-        # Chat Interface Logic
-        submit_button.click(
-            chat_interface,
-            inputs=[
-                chat_interface_input,
-                chatbot,
-                temperature_slider,
-                max_new_tokens_slider,
-                top_p_slider,
-                repetition_penalty_slider,
-            ],
-            outputs=[
-                chatbot,
-            ],
+import asyncio
+import logging
+from typing import Dict, Any
+from functools import partial
+
+from flask import Flask, request, jsonify
+from langchain import PromptTemplate, LLMChain
+from langchain.llms import OpenAI
+from langchain.chains import ConversationChain
+from langchain.memory import ConversationBufferMemory
+from langchain.vectorstores import Chroma
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.document_loaders import TextLoader
+
+logging.basicConfig(level=logging.INFO)
+
+# Define core component classes
+class Task:
+    def __init__(self, task_name: str, input_data: Any, agent_name: str):
+        self.task_name = task_name
+        self.input_data = input_data
+        self.agent_name = agent_name
+
+class ModelManager:
+    def __init__(self):
+        self.model = None
+
+    async def start(self):
+        logging.info("Starting model.")
+        await asyncio.sleep(1)  # Simulate loading time
+
+    async def stop(self):
+        logging.info("Unloading model.")
+
+class CodeArchitect:
+    def __init__(self, model_manager: ModelManager, model=None):
+        self.model_manager = model_manager
+        self.generator = model if model else pipeline("text-generation", model="gpt2")
+
+    async def start(self):
+        await self.model_manager.start()
+
+    async def stop(self):
+        await self.model_manager.stop()
+
+    async def generate_code(self, text_input: str) -> str:
+        response = self.generator(text_input, max_length=5000, num_return_sequences=1)[0]['generated_text']
+        return response
+
+class UIUXWizard:
+    def __init__(self, model_manager: ModelManager, vector_store=None):
+        self.model_manager = model_manager
+        self.vector_store = vector_store
+        self.conversation_chain = ConversationChain(
+            llm=OpenAI(temperature=0.7),
+            memory=ConversationBufferMemory(),
         )
 
-    # --- Web App Creation ---
-    with gr.Row():
-        gr.Markdown("## Create Your Web App")
-        app_name_input = gr.Textbox(label="App Name", placeholder="Enter your app name")
-        code_output = gr.Textbox(label="Code", interactive=False)
-        create_web_app_button = gr.Button("Create Web App")
-        deploy_button = gr.Button("Deploy")
-        local_host_button = gr.Button("Local Host")
-        ship_button = gr.Button("Ship")
-
-        # Web App Creation Logic
-        create_web_app_button.click(
-            create_web_app_button_click,
-            inputs=[app_name_input, code_output],
-            outputs=[gr.Textbox(label="Status", interactive=False)],
-        )
-
-        # Deploy the web app
-        deploy_button.click(
-            deploy_button_click,
-            inputs=[app_name_input, code_output],
-            outputs=[gr.Textbox(label="Status", interactive=False)],
-        )
+    async def start(self):
+        await self.model_manager.start()
+
+    async def stop(self):
+        await self.model_manager.stop()
+
+    def get_memory_response(self, query):
+        if self.vector_store is None:
+            return "No memory available."
+        else:
+            results = self.vector_store.similarity_search(query, k=3)
+            return "\n".join(results)
+
+    def get_conversation_response(self, query):
+        return self.conversation_chain.run(query)
+
+# Define VersionControl class
+class VersionControl:
+    def __init__(self, system_name: str):
+        self.system_name = system_name
+
+    async def start(self):
+        logging.info(f"Starting version control system: {self.system_name}")
+        await asyncio.sleep(1)  # Simulate initialization time
+
+    async def stop(self):
+        logging.info(f"Stopping version control system: {self.system_name}")
+
+# Define Documentation class
+class Documentation:
+    def __init__(self, system_name: str):
+        self.system_name = system_name
+
+    async def start(self):
+        logging.info(f"Starting documentation system: {self.system_name}")
+        await asyncio.sleep(1)  # Simulate initialization time
+
+    async def stop(self):
+        logging.info(f"Stopping documentation system: {self.system_name}")
+
+class BuildAutomation:
+    def __init__(self, system_name: str):
+        self.system_name = system_name
+
+    async def start(self):
+        logging.info(f"Starting build automation system: {self.system_name}")
+        await asyncio.sleep(1)  # Simulate initialization time
+
+    async def stop(self):
+        logging.info(f"Stopping build automation system: {self.system_name}")
+
+# Define EliteDeveloperCluster class
+class EliteDeveloperCluster:
+    def __init__(self, config: Dict[str, Any], model):
+        self.config = config
+        self.model_manager = ModelManager()
+        self.code_architect = CodeArchitect(self.model_manager, model)
+        self.uiux_wizard = UIUXWizard(self.model_manager)
+        self.version_control = VersionControl(config["version_control_system"])
+        self.documentation = Documentation(config["documentation_system"])
+        self.build_automation = BuildAutomation(config["build_automation_system"])
+        self.task_queue = asyncio.Queue()
+
+    async def start(self):
+        await self.code_architect.start()
+        await self.uiux_wizard.start()
+        await self.version_control.start()
+        await self.documentation.start()
+        await self.build_automation.start()
+
+    async def stop(self):
+        await self.code_architect.stop()
+        await self.uiux_wizard.stop()
+        await self.version_control.stop()
+        await self.documentation.stop()
+        await self.build_automation.stop()
+
+    async def process_task(self, task: Task):
+        if task.task_name == "generate_code":
+            response = await self.code_architect.generate_code(task.input_data)
+            return response
+        elif task.task_name == "get_memory_response":
+            response = self.uiux_wizard.get_memory_response(task.input_data)
+            return response
+        elif task.task_name == "get_conversation_response":
+            response = self.uiux_wizard.get_conversation_response(task.input_data)
+            return response
+        else:
+            return f"Unknown task: {task.task_name}"
+
+    async def process_tasks(self):
+        while True:
+            task = await self.task_queue.get()
+            response = await self.process_task(task)
+            logging.info(f"Processed task: {task.task_name} for agent: {task.agent_name}")
+            self.task_queue.task_done()
+            yield response
+
+    def route_request(self, query: str) -> str:
+        # TODO: Implement logic to determine the appropriate agent based on query
+        # For now, assume all requests are for the UIUXWizard
+        return self.uiux_wizard.get_conversation_response(query)
+
+# Flask App for handling agent requests
+app = Flask(__name__)
+
+@app.route('/agent', methods=['POST'])
+def agent_request():
+    data = request.get_json()
+    if data.get('input_value'):
+        # Process request from any agent (Agent 2, Agent 3, etc.)
+        task = Task(f"Process request from {data.get('agent_name', 'unknown agent')}", data.get('input_value'), data.get('agent_name', 'unknown agent'))
+        cluster.task_queue.put_nowait(task)
+        return jsonify({'response': 'Received input: from an agent, task added to queue.'})
+    else:
+        return jsonify({'response': 'Invalid input'})
 
-        # Local host the web app
-        local_host_button.click(
-            local_host_button_click,
-            inputs=[app_name_input, code_output],
-            outputs=[gr.Textbox(label="Status", interactive=False)],
-        )
+# Chat Interface
+def get_response(query: str) -> str:
+    return cluster.route_request(query)
 
-        # Ship the web app
-        ship_button.click(
-            ship_button_click,
-            inputs=[app_name_input, code_output],
-            outputs=[gr.Textbox(label="Status", interactive=False)],
-        )
+def response_streaming(text: str):
+    try:
+        for char in text:
+            yield char
+    except Exception as e:
+        logging.error(f"Error in response streaming: {e}")
+        yield "Error occurred while streaming the response."
+
+class ChatApp:
+    def __init__(self, cluster: EliteDeveloperCluster):
+        self.cluster = cluster
+
+    async def start(self):
+        await self.cluster.start()
+
+    async def stop(self):
+        await self.cluster.stop()
+
+    async def handle_request(self, query: str) -> str:
+        response = await self.cluster.process_tasks()
+        return response
+
+# Configuration
+config = {
+    "version_control_system": "Git",
+    "testing_framework": "PyTest",
+    "documentation_system": "Sphinx",
+    "build_automation_system": "Jenkins",
+    "redis_host": "localhost",
+    "redis_port": 6379,
+    "max_workers": 4,
+}
 
-    # --- Connect Chat Output to Code Output ---
-    chatbot.change(
-        lambda x: x[-1][1] if x else "",
-        inputs=[chatbot],
-        outputs=[code_output],
-    )
+if __name__ == "__main__":
+    # Initialize the cluster
+    cluster = EliteDeveloperCluster(config, model=None)
 
-    # --- Initialize Hugging Face Client ---
-    client = InferenceClient(repo_id="MODEL_NAME", token=os.environ.get("HF_TOKEN"))
+    # Start the cluster and task processing loop
+    asyncio.run(cluster.start())
+    asyncio.run(cluster.process_tasks())
 
-    # --- Launch Gradio ---
-    demo.queue().launch(debug=True)
+    # Run Flask app
+    app.run(debug=True)