Update app.py

app.py

@@ -1,356 +1,110 @@
-import os
-import subprocess
 import streamlit as st
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
-import black
-from pylint import lint
-from io import StringIO
-import together
-import sys
-
-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
-if 'chat_history' not in st.session_state:
-    st.session_state.chat_history = []
-if 'terminal_history' not in st.session_state:
-    st.session_state.terminal_history = []
-if 'workspace_projects' not in st.session_state:
-    st.session_state.workspace_projects = {}
-if 'available_agents' not in st.session_state:
-    st.session_state.available_agents = []
-if 'current_state' not in st.session_state:
-    st.session_state.current_state = {
-        'toolbox': {},
-        'workspace_chat': {}
-    }
-
-class AIAgent:
-    def __init__(self, name, description, skills):
-        self.name = name
-        self.description = description
-        self.skills = skills
-
-    def create_agent_prompt(self):
-        skills_str = '\n'.join([f"* {skill}" for skill in self.skills])
-        agent_prompt = (
-            f"""
-As an elite expert developer, my name is {self.name}. I possess a comprehensive understanding of the following areas:
-{skills_str}
-I am confident that I can leverage my expertise to assist you in developing and deploying cutting-edge web applications. Please feel free to ask any questions or present any challenges you may encounter.
-"""
-        )
-        return agent_prompt
+import gradio as gr
+from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer
+import subprocess
+import os
 
-    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()])
-        )
+# Initialize Hugging Face pipelines
+text_generator = pipeline("text-generation", model="gpt2")
+code_generator = pipeline("text2text-generation", model="microsoft/CodeGPT-small-py")
 
-        # 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
-        # - Check if the user has requested to analyze sentiment
+# Streamlit App
+st.title("AI Dev Tool Kit")
 
-        # 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."
+# Sidebar for Navigation
+st.sidebar.title("Navigation")
+app_mode = st.sidebar.selectbox("Choose the app mode", ["Explorer", "In-Chat Terminal", "Tool Box"])
+
+if app_mode == "Explorer":
+    st.header("Explorer")
+    st.write("Explore files and projects here.")
+    # Implement your explorer functionality here
+
+elif app_mode == "In-Chat Terminal":
+    st.header("In-Chat Terminal")
+    
+    def run_terminal_command(command):
+        try:
+            result = subprocess.run(command, shell=True, capture_output=True, text=True)
+            return result.stdout if result.returncode == 0 else result.stderr
+        except Exception as e:
+            return str(e)
+
+    def terminal_interface(command):
+        response = run_terminal_command(command)
+        return response
+
+    def nlp_code_interpreter(text):
+        response = code_generator(text, max_length=150)
+        code = response[0]['generated_text']
+        return code, run_terminal_command(code)
+
+    with gr.Blocks() as iface:
+        terminal_input = gr.Textbox(label="Enter Command or Code")
+        terminal_output = gr.Textbox(label="Terminal Output", lines=10)
+        terminal_button = gr.Button("Run")
+        
+        terminal_button.click(
+            nlp_code_interpreter,
+            inputs=terminal_input,
+            outputs=[terminal_output, terminal_output]
         )
 
-        return summary, next_step
-
-def save_agent_to_file(agent):
-    """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)
+        iface.launch()
 
-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
+    st.write("Use the terminal to execute commands or interpret natural language into code.")
 
-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()
+elif app_mode == "Tool Box":
+    st.header("Tool Box")
+    st.write("Access various AI development tools here.")
+    # Implement your tool box functionality here
 
-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."
+# Deploy to Hugging Face Spaces
+def deploy_to_huggingface(app_name):
+    code = f"""
+import gradio as gr
 
-    model_name = "Bin12345/AutoCoder_S_6.7B"
+def run_terminal_command(command):
     try:
-        model = AutoModelForCausalLM.from_pretrained(model_name)
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
-        generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
-    except EnvironmentError as e:
-        return f"Error loading model: {e}"
-
-    combined_input = (
-        f"{agent_prompt}\n\nUser: {input_text}\nAgent:"
+        result = subprocess.run(command, shell=True, capture_output=True, text=True)
+        return result.stdout if result.returncode == 0 else result.stderr
+    except Exception as e:
+        return str(e)
+
+def nlp_code_interpreter(text):
+    response = code_generator(text, max_length=150)
+        code = response[0]['generated_text']
+        return code, run_terminal_command(code)
+
+with gr.Blocks() as iface:
+    terminal_input = gr.Textbox(label="Enter Command or Code")
+    terminal_output = gr.Textbox(label="Terminal Output", lines=10)
+    terminal_button = gr.Button("Run")
+    
+    terminal_button.click(
+        nlp_code_interpreter,
+        inputs=terminal_input,
+        outputs=[terminal_output, terminal_output]
     )
 
-    input_ids = tokenizer.encode(combined_input, return_tensors="pt")
-    max_input_length = 900
-    if input_ids.shape[1] > max_input_length:
-        input_ids = input_ids[:, :max_input_length]
-
-    outputs = model.generate(
-        input_ids, max_new_tokens=50, num_return_sequences=1, do_sample=True,
-        pad_token_id=tokenizer.eos_token_id  # Set pad_token_id to eos_token_id
-    )
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return response
+iface.launch()
+"""
 
-# Terminal interface
-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, 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
+    with open("app.py", "w") as f:
+        f.write(code)
 
-# Code editor interface
-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()
-
-    return formatted_code, lint_message
-
-# Text summarization tool
-def summarize_text(text):
-    summarizer = pipeline("summarization", model="t5-base")
-    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
-    return summary[0]['summary_text']
-
-# Sentiment analysis tool
-def sentiment_analysis(text):
-    analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
-    result = analyzer(text)
-    return result[0]['label']
-
-# Text translation tool (code translation)
-def translate_code(code, source_language, target_language):
-    translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")  # Example: English to Spanish
-    translated_code = translator(code, target_lang=target_language)[0]['translation_text']
-    return translated_code
-
-def generate_code(code_idea):
-    generator = pipeline('text-generation', model='bigcode/starcoder')
-    generated_code = generator(code_idea, max_length=1000, num_return_sequences=1)[0]['generated_text']
-    return generated_code
-
-def chat_interface(input_text):
-    chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium")
-    response = chatbot(input_text, max_length=50, num_return_sequences=1)[0]['generated_text']
-    return response
-
-# Workspace interface
-def workspace_interface(project_name):
-    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."
-
-# Add code to workspace
-def add_code_to_workspace(project_name, code, file_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."
-
-    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}'."
-
-# 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}"))
-
-    # 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")
-
-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)
+        subprocess.run(["huggingface-cli", "repo", "create", "--type", "space", "--space_sdk", "gradio", app_name], check=True)
+        subprocess.run(["git", "init"], cwd=f"./{app_name}", check=True)
+        subprocess.run(["git", "add", "."], cwd=f"./{app_name}", check=True)
+        subprocess.run(['git', 'commit', '-m', '"Initial commit"'], cwd=f'./{app_name}', check=True)
+        subprocess.run(["git", "push", "https://huggingface.co/spaces/" + app_name, "main"], cwd=f'./{app_name}', check=True)
+        return f"Successfully deployed to Hugging Face Spaces: https://huggingface.co/spaces/{app_name}"
+    except Exception as e:
+        return f"Error deploying to Hugging Face Spaces: {e}"
+
+# Example usage
+if st.button("Deploy to Hugging Face"):
+    app_name = "ai-dev-toolkit"
+    deploy_status = deploy_to_huggingface(app_name)
+    st.write(deploy_status)