Update app.py

app.py

@@ -3,80 +3,25 @@ import gradio as gr
 import random
 import os
 import subprocess
+import threading
+import time
+import shutil
+from typing import Dict, Tuple
 
+# Constants
 API_URL = "https://api-inference.huggingface.co/models/"
+MODEL_NAME = "mistralai/Mixtral-8x7B-Instruct-v0.1"  # Replace with your desired model
+DEFAULT_TEMPERATURE = 0.9
+DEFAULT_MAX_NEW_TOKENS = 2048
+DEFAULT_TOP_P = 0.95
+DEFAULT_REPETITION_PENALTY = 1.2
+LOCAL_HOST_PORT = 7860
 
-client = InferenceClient(
-    "mistralai/Mixtral-8x7B-Instruct-v0.1" 
-)
+# Initialize the InferenceClient
+client = InferenceClient(MODEL_NAME)
 
-def format_prompt(message, history, agent_roles):
-    """Formats the prompt with the selected agent roles and conversation history."""
-    prompt = f"""
-You are an expert agent cluster, consisting of {', '.join(agent_roles)}. 
-Respond with complete program coding to client requests. 
-Using available tools, please explain the researched information.
-Please don't answer based solely on what you already know. Always perform a search before providing a response.
-In special cases, such as when the user specifies a page to read, there's no need to search.
-Please read the provided page and answer the user's question accordingly.
-If you find that there's not much information just by looking at the search results page, consider these two options and try them out:
-- Try clicking 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 extremely busy and not as free as you are.
-Therefore, to save the user's effort, please provide direct answers.
-BAD ANSWER EXAMPLE
-- Please refer to these pages.
-- You can write code referring these pages.
-- Following page will be helpful.
-GOOD ANSWER EXAMPLE
-- This is the complete code:  -- complete code here --
-- The answer of you question is -- answer here --
-Please make sure to list the URLs of the pages you referenced at the end of your answer. (This will allow users to verify your response.)
-Please make sure to answer in the language used by the user. If the user asks in Japanese, please answer in Japanese. If the user asks in Spanish, please answer in Spanish.
-But, you can go ahead and search in English, especially for programming-related questions. PLEASE MAKE SURE TO ALWAYS SEARCH IN ENGLISH FOR THOSE.
-"""
-
-    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
-
-def generate(prompt, history, agent_roles, temperature=0.9, max_new_tokens=2048, top_p=0.95, repetition_penalty=1.0):
-    """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
-
-def change_agent(agent_name):
-    """Updates the selected agent role."""
-    global selected_agent
-    selected_agent = agent_name
-    return f"Agent switched to: {agent_name}"
-
-# Define the available agent roles
-agent_roles = {
+# Define agent roles and their initial states
+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},
@@ -87,7 +32,7 @@ agent_roles = {
 # Initialize the selected agent
 selected_agent = list(agent_roles.keys())[0]
 
-# Define the initial prompt for the selected agent
+# Initial prompt for the selected agent
 initial_prompt = f"""
 You are an expert {selected_agent} who responds with complete program coding to client requests. 
 Using available tools, please explain the researched information.
@@ -111,6 +56,7 @@ Please make sure to answer in the language used by the user. If the user asks in
 But, you can go ahead and search in English, especially for programming-related questions. PLEASE MAKE SURE TO ALWAYS SEARCH IN ENGLISH FOR THOSE.
 """
 
+# Custom CSS for the chat interface
 customCSS = """
 #component-7 { # dies ist die Standardelement-ID des Chatkomponenten
   height: 1600px; # passen Sie die Höhe nach Bedarf an
@@ -118,17 +64,20 @@ customCSS = """
 }
 """
 
-def toggle_agent(agent_name):
+# 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'}"
 
-def get_agent_cluster():
+# Function to get the active agent cluster
+def get_agent_cluster() -> Dict[str, bool]:
     """Returns a dictionary of active agents."""
     return {agent: agent_roles[agent]["active"] for agent in agent_roles}
 
-def run_code(code):
+# Function to execute code
+def run_code(code: str) -> str:
     """Executes the provided code and returns the output."""
     try:
         output = subprocess.check_output(
@@ -140,7 +89,69 @@ def run_code(code):
     except subprocess.CalledProcessError as e:
         return f"Error: {e.output}"
 
-def chat_interface(message, history, agent_cluster, temperature=0.9, max_new_tokens=2048, top_p=0.95, repetition_penalty=1.0):
+# 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 = f"""
+You are an expert agent cluster, consisting of {', '.join(agent_roles)}. 
+Respond with complete program coding to client requests. 
+Using available tools, please explain the researched information.
+Please don't answer based solely on what you already know. Always perform a search before providing a response.
+In special cases, such as when the user specifies a page to read, there's no need to search.
+Please read the provided page and answer the user's question accordingly.
+If you find that there's not much information just by looking at the search results page, consider these two options and try them out:
+- Try clicking 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 extremely busy and not as free as you are.
+Therefore, to save the user's effort, please provide direct answers.
+BAD ANSWER EXAMPLE
+- Please refer to these pages.
+- You can write code referring these pages.
+- Following page will be helpful.
+GOOD ANSWER EXAMPLE
+- This is the complete code:  -- complete code here --
+- The answer of you question is -- answer here --
+Please make sure to list the URLs of the pages you referenced at the end of your answer. (This will allow users to verify your response.)
+Please make sure to answer in the language used by the user. If the user asks in Japanese, please answer in Japanese. If the user asks in Spanish, please answer in Spanish.
+But, you can go ahead and search in English, especially for programming-related questions. PLEASE MAKE SURE TO ALWAYS SEARCH IN ENGLISH FOR THOSE.
+"""
+
+    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]], agent_cluster: Dict[str, bool], temperature: float = DEFAULT_TEMPERATURE, max_new_tokens: int = DEFAULT_MAX_NEW_TOKENS, top_p: float = DEFAULT_TOP_P, repetition_penalty: float = DEFAULT_REPETITION_PENALTY) -> Tuple[str, str]:
     """Handles user input and generates responses."""
     if message.startswith("python"): 
         # User entered code, execute it 
@@ -153,54 +164,205 @@ def chat_interface(message, history, agent_cluster, temperature=0.9, max_new_tok
         response = generate(message, history, active_agents, temperature, max_new_tokens, top_p, repetition_penalty)
         return (message, response)
 
-with gr.Blocks(theme='ParityError/Interstellar') as demo: 
+# 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")
+
+    # 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(code: str) -> str:
+    """Handles the "Create Web App" button click."""
+    # Get the app name from the user
+    app_name = gr.Textbox.get().strip()
+
+    # 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."""
+    # Get the app name from the user
+    app_name = gr.Textbox.get().strip()
+
+    # 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."""
+    # Get the app name from the user
+    app_name = gr.Textbox.get().strip()
+
+    # 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."""
+    # Get the app name from the user
+    app_name = gr.Textbox.get().strip()
+
+    # 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!"
+
+# Create the Gradio interface
+with gr.Blocks(theme='ParityError/Interstellar') as demo:
+    # Agent selection area
     with gr.Row():
         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 area
+    with gr.Row():
+        chatbot = gr.Chatbot()
+        chat_interface_input = gr.Textbox(label="Enter your message", placeholder="Ask me anything!")
+        chat_interface_output = gr.Textbox(label="Response", interactive=False)
+
+        # Parameters for the chat interface
+        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 for the chat interface
+        submit_button = gr.Button("Submit")
+
+        # Create the chat interface
+        submit_button.click(
+            chat_interface,
+            inputs=[
+                chat_interface_input,
+                chatbot,
+                get_agent_cluster,
+                temperature_slider,
+                max_new_tokens_slider,
+                top_p_slider,
+                repetition_penalty_slider,
+            ],
+            outputs=[
+                chatbot,
+                chat_interface_output,
+            ],
+        )
+
+    # Web app creation area
     with gr.Row():
-        gr.ChatInterface(
-            fn=chat_interface,
-            chatbot=gr.Chatbot(),  # Asegúrate de crear una instancia de Chatbot si es necesario
-            additional_inputs=[
-                gr.Slider(
-                    label="Temperature",
-                    value=0.9,
-                    minimum=0.0,
-                    maximum=1.0,
-                    step=0.05,
-                    interactive=True,
-                    info="Higher values generate more diverse outputs",
-                ),
-                gr.Slider(
-                    label="Maximum New Tokens",
-                    value=2048,
-                    minimum=64,
-                    maximum=4096,
-                    step=64,
-                    interactive=True,
-                    info="The maximum number of new tokens",
-                ),
-                gr.Slider(
-                    label="Top-p (Nucleus Sampling)",
-                    value=0.90,
-                    minimum=0.0,
-                    maximum=1,
-                    step=0.05,
-                    interactive=True,
-                    info="Higher values sample more low-probability tokens",
-                ),
-                gr.Slider(
-                    label="Repetition Penalty",
-                    value=1.2,
-                    minimum=1.0,
-                    maximum=2.0,
-                    step=0.05,
-                    interactive=True,
-                    info="Penalize repeated tokens",
-                )
-            ]
+        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")
+
+        # Create the web app creation interface
+        create_web_app_button.click(
+            create_web_app_button_click,
+            inputs=[code_output],
+            outputs=[gr.Textbox(label="Status", interactive=False)],
         )
-demo.queue().launch(debug=True)
+
+        # Deploy the web app
+        deploy_button.click(
+            deploy_button_click,
+            inputs=[app_name_input, code_output],
+            outputs=[gr.Textbox(label="Status", interactive=False)],
+        )
+
+        # 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)],
+        )
+
+        # Ship the web app
+        ship_button.click(
+            ship_button_click,
+            inputs=[app_name_input, code_output],
+            outputs=[gr.Textbox(label="Status", interactive=False)],
+        )
+
+    # Connect the chat interface output to the code output
+    chat_interface_output.change(
+        lambda x: x,
+        inputs=[chat_interface_output],
+        outputs=[code_output],
+    )
+
+    # Launch the Gradio interface
+    demo.queue().launch(debug=True)