Update app.py

app.py

@@ -1,480 +1,304 @@
 import os
-import sys
 import subprocess
-import base64
-import json
-from io import StringIO
-from typing import Dict, List
-import streamlit as st
-from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer
-from pylint import lint
+import random
 from huggingface_hub import InferenceClient
 import gradio as gr
-import random
-import prompts
-client = InferenceClient(
-    "mistralai/Mixtral-8x7B-v0.1"
+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,
+    LOG_PROMPT,
+    LOG_RESPONSE,
+    MODIFY_PROMPT,
+    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
 
-def format_prompt(message, history):
-  prompt = "<s>"
-  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
-
-# Replace st.secrets with os.environ
-hf_token = os.environ.get("huggingface_token")
-
-if not hf_token:
-    st.error("Hugging Face API key not found. Please set the HUGGINGFACE_API_KEY environment variable.")
-    st.stop()
-
-# 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 = {}
-
-# Load pre-trained RAG retriever
-rag_retriever = pipeline("text-generation", model="mistralai/Mixtral-8x7B-v0.1")
-
-# Load pre-trained chat model
-chat_model = AutoModelForSeq2SeqLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1")
-
-# Load tokenizer
-tokenizer = AutoTokenizer.from_pretrained("gpt2")
-
-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():
-        output = rag_retriever(input_ids, attention_mask=attention_mask)
-        response = output.generator_outputs[0].sequences[0]
-
-    # Chat model: Refine response
-    chat_input = tokenizer(response, return_tensors="pt")
-    chat_input["input_ids"] = chat_input["input_ids"].unsqueeze(0)
-    chat_input["attention_mask"] = chat_input["attention_mask"].unsqueeze(0)
-    with torch.no_grad():
-        chat_output = chat_model(**chat_input)
-        refined_response = chat_output.sequences[0]
-
-    # Output pipeline: Return final response
-    return refined_response
-
-class AIAgent:
-    def __init__(self, name: str, description: str, skills: List[str], hf_api=None):
-        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)
-
-    async def autonomous_build(self, chat_history: List[str], workspace_projects: Dict[str, str], project_name: str, selected_model: str):
-        # Continuation of previous methods
-        summary = "Chat History:\n" + "\n".join(chat_history)
-        summary += "\n\nWorkspace Projects:\n" + "\n".join(workspace_projects.items())
-
-        # Analyze chat history and workspace projects to suggest actions
-        # Example:
-        # - Check if the user has requested to create a new file
-        # - Check if the user has requested to install a package
-        # - Check if the user has requested to run a command
-        # - Check if the user has requested to generate code
-        # - Check if the user has requested to translate code
-        # - Check if the user has requested to summarize text
-
-        # Generate a response based on the analysis
-        next_step = "Based on the current state, the next logical step is to implement the main application logic."
+now = datetime.now()
+date_time_str = now.strftime("%Y-%m-%d %H:%M:%S")
 
-        # Ensure project folder exists
-        project_path = os.path.join(PROJECT_ROOT, project_name)
-        if not os.path.exists(project_path):
-            os.makedirs(project_path)
+client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
 
-        # Create requirements.txt if it doesn't exist
-        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")
+VERBOSE = True
+MAX_HISTORY = 100
 
-        # Create app.py if it doesn't exist
-        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")
-
-        # Generate GUI code for app.py if requested
-        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)
-
-        # Run the default build process
-        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 get_built_space_files() -> Dict[str, str]:
-    # 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 save_agent_to_file(agent: AIAgent):
-    """Saves the agent's prompt to a file."""
-    if not os.path.exists(AGENT_DIRECTORY):
-        os.makedirs(AGENT_DIRECTORY)
-    file_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}.txt")
-    with open(file_path, "w") as file:
-        file.write(agent.create_agent_prompt())
-    st.session_state.available_agents.append(agent.name)
-
-def load_agent_prompt(agent_name: str) -> str:
-    """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: str, text: str) -> str:
-    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:
-    agent_prompt = load_agent_prompt(agent_name)
-    if agent_prompt is None:
-        return f"Agent {agent_name} not found."
-
-    model_name = ""
+def format_prompt(message, history):
+    prompt = "<s>"
+    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 run_gpt(prompt_template, stop_tokens, max_tokens, purpose, **prompt_kwargs):
+    seed = random.randint(1, 1111111111111111)
+    print(seed)
+    generate_kwargs = dict(
+        temperature=1.0,
+        max_new_tokens=2096,
+        top_p=0.99,
+        repetition_penalty=1.0,
+        do_sample=True,
+        seed=seed,
+    )
+
+    content = PREFIX.format(
+        date_time_str=date_time_str,
+        purpose=purpose,
+        safe_search=safe_search,
+    ) + prompt_template.format(**prompt_kwargs)
+    if VERBOSE:
+        print(LOG_PROMPT.format(content))
+
+    stream = client.text_generation(content, **generate_kwargs, stream=True, details=True, return_full_text=False)
+    resp = ""
+    for response in stream:
+        resp += response.token.text
+
+    if VERBOSE:
+        print(LOG_RESPONSE.format(resp))
+    return resp
+
+def compress_history(purpose, task, history, directory):
+    resp = run_gpt(
+        COMPRESS_HISTORY_PROMPT,
+        stop_tokens=["observation:", "task:", "action:", "thought:"],
+        max_tokens=512,
+        purpose=purpose,
+        task=task,
+        history=history,
+    )
+    history = "observation: {}\n".format(resp)
+    return history
+
+def call_search(purpose, task, history, directory, action_input):
+    print("CALLING SEARCH")
     try:
-        generator = pipeline("text-generation", model=model_name)
-        generator.tokenizer.pad_token = generator.tokenizer.eos_token
-        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
+        if "http" in action_input:
+            if "<" in action_input:
+                action_input = action_input.strip("<")
+            if ">" in action_input:
+                action_input = action_input.strip(">")
+            response = i_s(action_input)
+            print(response)
+            history += "observation: search result is: {}\n".format(response)
+        else:
+            history += "observation: I need to provide a valid URL to 'action: SEARCH action_input=https://URL'\n"
     except Exception as e:
-        return f"Error loading model: {e}"
-
-def terminal_interface(command: str, project_name: str = None) -> str:
-    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)
-    else:
-        result = subprocess.run(command, shell=True, capture_output=True, text=True)
-    return result.stdout
-
-def code_editor_interface(code: str) -> str:
+        history += "observation: {}'\n".format(e)
+    return "MAIN", None, history, task
+
+def call_main(purpose, task, history, directory, action_input):
+    resp = run_gpt(
+        ACTION_PROMPT,
+        stop_tokens=["observation:", "task:", "action:", "thought:"],
+        max_tokens=2096,
+        purpose=purpose,
+        task=task,
+        history=history,
+    )
+    lines = resp.strip().strip("\n").split("\n")
+    for line in lines:
+        if line == "":
+            continue
+        if line.startswith("thought: "):
+            history += "{}\n".format(line)
+        elif line.startswith("action: "):
+            action_name, action_input = parse_action(line)
+            print(f'ACTION_NAME :: {action_name}')
+            print(f'ACTION_INPUT :: {action_input}')
+            history += "{}\n".format(line)
+            if "COMPLETE" in action_name or "COMPLETE" in action_input:
+                task = "END"
+                return action_name, action_input, history, task
+            else:
+                return action_name, action_input, history, task
+        else:
+            history += "{}\n".format(line)
+    return "MAIN", None, history, task
+
+def call_set_task(purpose, task, history, directory, action_input):
+    task = run_gpt(
+        TASK_PROMPT,
+        stop_tokens=[],
+        max_tokens=64,
+        purpose=purpose,
+        task=task,
+        history=history,
+    ).strip("\n")
+    history += "observation: task has been updated to: {}\n".format(task)
+    return "MAIN", None, history, task
+
+def end_fn(purpose, task, history, directory, action_input):
+    task = "END"
+    return "COMPLETE", "COMPLETE", history, task
+
+NAME_TO_FUNC = {
+    "MAIN": call_main,
+    "UPDATE-TASK": call_set_task,
+    "SEARCH": call_search,
+    "COMPLETE": end_fn,
+}
+
+def run_action(purpose, task, history, directory, action_name, action_input):
+    print(f'action_name::{action_name}')
     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()
-
-    return formatted_code, lint_message
-
-def summarize_text(text: str) -> str:
-    summarizer = pipeline("summarization")
-    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
-    return summary[0]['summary_text']
-
-
-def translate_code(code: str, source_language: str, target_language: str) -> str:
-    # Use a Hugging Face translation model instead of OpenAI
-    # Example: English to Spanish
-    translator = pipeline(
-        "translation", model="mistralai/Mixtral-8x7B-Instruct-v0.1")
-    translated_code = translator(code, target_lang=target_language)[0]['translation_text']
-    return translated_code
-
-def generate_code(code_idea: str, model_name: str) -> str:
-    """Generates code using the selected model."""
-    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
+        if "RESPONSE" in action_name or "COMPLETE" in action_name:
+            action_name = "COMPLETE"
+            task = "END"
+            return action_name, "COMPLETE", history, task
+
+        if len(history.split("\n")) > MAX_HISTORY:
+            if VERBOSE:
+                print("COMPRESSING HISTORY")
+            history = compress_history(purpose, task, history, directory)
+        if not action_name in NAME_TO_FUNC:
+            action_name = "MAIN"
+        if action_name == "" or action_name is None:
+            action_name = "MAIN"
+        assert action_name in NAME_TO_FUNC
+
+        print("RUN: ", action_name, action_input)
+        return NAME_TO_FUNC[action_name](purpose, task, history, directory, action_input)
     except Exception as e:
-        return f"Error generating code: {e}"
-
-def chat_interface(input_text: str) -> str:
-    """Handles general chat interactions with the user."""
-    # Use a Hugging Face chatbot model or your own logic
-    chatbot = pipeline("text-generation", model="mistralai/Mixtral-8x7B-Instruct-v0.1")
-    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="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": []
-    }
-    for filename, contents in files.items():
-        data = {
-            "content": contents,
-            "path": filename,
-            "encoding": "utf-8",
-            "mode": "overwrite"
-        }
-        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)
-
-# 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"):
-        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}")
+        history += "observation: the previous command did not produce any useful output, I need to check the commands syntax, or use a different command\n"
+        return "MAIN", None, history, task
+
+def run(purpose, history):
+    task = None
+    directory = "./"
+    if history:
+        history = str(history).strip("[]")
+    if not history:
+        history = ""
+
+    action_name = "UPDATE-TASK" if task is None else "MAIN"
+    action_input = None
+    while True:
+        print("")
+        print("")
+        print("---")
+        print("purpose:", purpose)
+        print("task:", task)
+        print("---")
+        print(history)
+        print("---")
+
+        action_name, action_input, history, task = run_action(
+            purpose,
+            task,
+            history,
+            directory,
+            action_name,
+            action_input,
+        )
+        yield (history)
+        if task == "END":
+            return (history)
 
-    # 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")
-
-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)
-
-        # Automatically create requirements.txt and app.py
-        project_path = os.path.join(PROJECT_ROOT, project_name)
-        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")
-
-    # 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.session_state.terminal_history.append(
-            (f"Add Code: {code_to_add}", add_code_status))
-        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.session_state.terminal_history.append(
-            (terminal_input, terminal_output))
-        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}")
-
-    # Code Generation
-    st.subheader("Code Generation")
-    code_idea = st.text_input("Enter your code idea:")
-
-    # Model Selection Menu
-    selected_model = st.selectbox(
-        "Select a code-generative model", AVAILABLE_CODE_GENERATIVE_MODELS)
-
-    if st.button("Generate Code"):
-        generated_code = generate_code(code_idea, selected_model)
-        st.code(generated_code, language="python")
-
-    # Automate Build Process
-    st.subheader("Automate Build Process")
-    if st.button("Automate"):
-        # Load the agent without skills for now
-        agent = AIAgent(selected_agent, "", [])
-        summary, next_step = agent.autonomous_build(
-            st.session_state.chat_history, st.session_state.workspace_projects, project_name, selected_model)
-        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
-        create_space_on_hugging_face(api, agent.name, agent.description, True, get_built_space_files())
+def format_prompt(message, history):
+    prompt = "<s>"
+    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
+
+agents = [
+    "WEB_DEV",
+   agents = [
+    "WEB_DEV",
+    "AI_SYSTEM_PROMPT",
+    "PYTHON_CODE_DEV"
+]
+
+def generate(
+    prompt, history, agent_name=agents[0], sys_prompt="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
+):
+    seed = random.randint(1, 1111111111111111)
+
+    agent = prompts.WEB_DEV
+    if agent_name == "WEB_DEV":
+        agent = prompts.WEB_DEV
+    if agent_name == "AI_SYSTEM_PROMPT":
+        agent = prompts.AI_SYSTEM_PROMPT
+    if agent_name == "PYTHON_CODE_DEV":
+        agent = prompts.PYTHON_CODE_DEV
+    system_prompt = agent
+    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=seed,
+    )
+
+    formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
+    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
+
+additional_inputs = [
+    gr.Dropdown(
+        label="Agents",
+        choices=[s for s in agents],
+        value=agents[0],
+        interactive=True,
+    ),
+    gr.Textbox(
+        label="System Prompt",
+        max_lines=1,
+        interactive=True,
+    ),
+    gr.Slider(
+        label="Temperature",
+        value=0.9,
+        minimum=0.0,
+        maximum=1.0,
+        step=0.05,
+        interactive=True,
+        info="Higher values produce more diverse outputs",
+    ),
+    gr.Slider(
+        label="Max new tokens",
+        value=1048 * 10,
+        minimum=0,
+        maximum=1048 * 10,
+        step=64,
+        interactive=True,
+        info="The maximum numbers 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,
+