Update app.py

app.py

@@ -2,19 +2,26 @@ import os
 import sys
 import subprocess
 import streamlit as st
+from huggingface_hub import InferenceClient
+import gradio as gr
+import random
+import prompts
 from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 import black
 from pylint import lint
 from io import StringIO
-from transformers import pipeline
 
+# Initialize the InferenceClient for Mixtral-8x7B-Instruct-v0.1
+client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
+
+# Initialize the pipeline for Llama-3-8B-Instruct-Coder-GGUF
 pipe = pipeline("text-generation", model="bartowski/Llama-3-8B-Instruct-Coder-GGUF")
 
 HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/DevToolKit"
 PROJECT_ROOT = "projects"
 AGENT_DIRECTORY = "agents"
 
-# Global state to manage communication between Tool Box and Workspace Chat App
+# Global state management
 if 'chat_history' not in st.session_state:
     st.session_state.chat_history = []
 if 'terminal_history' not in st.session_state:
@@ -29,6 +36,17 @@ if 'current_state' not in st.session_state:
         'workspace_chat': {}
     }
 
+# Define the agents
+agents = [
+    "WEB_DEV",
+    "AI_SYSTEM_PROMPT",
+    "PYTHON_CODE_DEV",
+    "CODE_REVIEW_ASSISTANT",
+    "CONTENT_WRITER_EDITOR",
+    "QUESTION_GENERATOR",
+    "HUGGINGFACE_FILE_DEV",
+]
+
 class AIAgent:
     def __init__(self, name, description, skills):
         self.name = name
@@ -45,372 +63,161 @@ I am confident that I can leverage my expertise to assist you in developing and
         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()])
-
         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()
+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 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 = getattr(prompts, agent_name, prompts.WEB_DEV_SYSTEM_PROMPT)
+    system_prompt = agent
+
+    generate_kwargs = dict(
+        temperature=float(temperature),
+        max_new_tokens=max_new_tokens,
+        top_p=float(top_p),
+        repetition_penalty=repetition_penalty,
+        do_sample=True,
+        seed=seed,
+    )
 
-# 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."
+    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 = ""
 
-    # Load the GPT-2 model which is compatible with AutoModelForCausalLM
-    model_name = "gpt2"
-    try:
-        model = AutoModel.from_pretrained("InferenceIllusionist/Meta-Llama-3.1-8B-Claude-iMat-GGUF")
-        tokenizer = AutoTokenizer.from_pretrained("InferenceIllusionist/Meta-Llama-3.1-8B-Claude-iMat-GGUF")
-        generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
-    except EnvironmentError as e:
-        return f"Error loading model: {e}"
+    for response in stream:
+        output += response.token.text
+        yield output
+    return output
 
-    # Combine the agent prompt with user input
-    combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:"
-    
-    # Truncate input text to avoid exceeding the model's maximum length
-    max_input_length = 900
-    input_ids = tokenizer.encode(combined_input, return_tensors="pt")
-    if input_ids.shape[1] > max_input_length:
-        input_ids = input_ids[:, :max_input_length]
+def chat_interface(chat_input):
+    response = generate(chat_input, st.session_state.chat_history)
+    return response
 
-    # 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)
+def chat_interface_with_agent(chat_input, agent_name):
+    agent_prompt = getattr(prompts, agent_name, prompts.WEB_DEV_SYSTEM_PROMPT)
+    response = generate(chat_input, st.session_state.chat_history, agent_name=agent_name, sys_prompt=agent_prompt)
     return response
 
-def workspace_interface(project_name):
-    project_path = os.path.join(PROJECT_ROOT, project_name)
-    if not os.path.exists(PROJECT_ROOT):
-        os.makedirs(PROJECT_ROOT)
-    if not os.path.exists(project_path):
-        os.makedirs(project_path)
-        st.session_state.workspace_projects[project_name] = {"files": []}
-        st.session_state.current_state['workspace_chat']['project_name'] = project_name
-        commit_and_push_changes(f"Create project {project_name}")
-        return f"Project {project_name} created successfully."
-    else:
-        return f"Project {project_name} already exists."
+def terminal_interface(command, project_name):
+    # Implement terminal functionality here
+    return f"Executed command: {command} in project: {project_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."
+    if project_name not in st.session_state.workspace_projects:
+        st.session_state.workspace_projects[project_name] = {'files': []}
+    st.session_state.workspace_projects[project_name]['files'].append(file_name)
+    return f"Added {file_name} to {project_name}"
+
+# Streamlit UI
+st.title("DevToolKit: AI-Powered Development Environment")
+
+# Project Management
+st.header("Project Management")
+project_name = st.text_input("Enter project name:")
+if st.button("Create Project"):
+    if project_name not in st.session_state.workspace_projects:
+        st.session_state.workspace_projects[project_name] = {'files': []}
+        st.success(f"Created project: {project_name}")
     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
-
-def code_editor_interface(code):
-    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()
-    st.session_state.current_state['toolbox']['formatted_code'] = formatted_code
-    st.session_state.current_state['toolbox']['lint_message'] = lint_message
-    return formatted_code, lint_message
-
-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 sentiment_analysis(text):
-    analyzer = pipeline("sentiment-analysis")
-    sentiment = analyzer(text)
-    st.session_state.current_state['toolbox']['sentiment'] = sentiment[0]
-    return sentiment[0]
-
-def translate_code(code, input_language, output_language):
-    # Define a dictionary to map programming languages to their corresponding file extensions
-    language_extensions = {
-        # ignore the specific languages right now, and continue to EOF
-    }
-
-    # 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")
-
-# 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"):
-        if chat_input.startswith("@"):
-            agent_name = chat_input.split(" ")[0][1:]  # Extract agent_name from @agent_name
-            chat_input = " ".join(chat_input.split(" ")[1:])  # Remove agent_name from input
-            chat_response = chat_interface_with_agent(chat_input, agent_name)
-        else:
-            chat_response = chat_interface(chat_input)
-        st.session_state.chat_history.append((chat_input, chat_response))
-        st.write(f"CodeCraft: {chat_response}")
-
-    # Terminal Interface
-    st.subheader("Terminal")
-    terminal_input = st.text_input("Enter a command:")
-    if st.button("Run"):
-        terminal_output = terminal_interface(terminal_input)
-        st.session_state.terminal_history.append((terminal_input, terminal_output))
-        st.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}")
-
-    # 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")
-
-    # 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)
+        st.warning(f"Project {project_name} already exists")
+
+# Code Addition
+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
+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
+st.subheader("Chat with DevToolKit 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"DevToolKit: {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"DevToolKit: {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", agents)
+agent_chat_input = st.text_area("Enter your message for the agent:")
+if st.button("Send to Agent"):
+    agent_chat_response = chat_interface_with_agent(agent_chat_input, selected_agent)
+    st.session_state.chat_history.append((agent_chat_input, agent_chat_response))
+    st.write(f"{selected_agent}: {agent_chat_response}")
+
+# Automate Build Process
+st.subheader("Automate Build Process")
+if st.button("Automate"):
+    agent = AIAgent(selected_agent, "", [])  # Load the agent without skills for now
+    summary, next_step = agent.autonomous_build(st.session_state.chat_history, st.session_state.workspace_projects)
+    st.write("Autonomous Build Summary:")
+    st.write(summary)
+    st.write("Next Step:")
+    st.write(next_step)
 
 # Display current state for debugging
 st.sidebar.subheader("Current State")
 st.sidebar.json(st.session_state.current_state)
 
+# Gradio Interface
+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=1000*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, interactive=True, info="Penalize repeated tokens"),
+]
+
+examples = [
+    ["Create a simple web application using Flask", agents[0], None, None, None, None, ],
+    ["Generate a Python script to perform a linear regression analysis", agents[2], None, None, None, None, ],
+    ["Create a Dockerfile for a Node.js application", agents[1], None, None, None, None, ],
+    # Add more examples as needed
+]
+
+gr.ChatInterface(
+    fn=generate,
+    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
+    additional_inputs=additional_inputs,
+    title="DevToolKit AI Assistant",
+    examples=examples,
+    concurrency_limit=20,
+).launch(show_api=True)