Update app.py

app.py

@@ -1,23 +1,34 @@
-import streamlit as st
 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 openai
 import sys
 
+# Set your OpenAI API key here
+openai.api_key = "YOUR_OPENAI_API_KEY"
+
+HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/DevToolKit"
 PROJECT_ROOT = "projects"
 AGENT_DIRECTORY = "agents"
 
-if "chat_history" not in st.session_state:
+# 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:
+if 'terminal_history' not in st.session_state:
     st.session_state.terminal_history = []
-if "workspace_projects" not in st.session_state:
+if 'workspace_projects' not in st.session_state:
     st.session_state.workspace_projects = {}
-if "available_agents" not in st.session_state:
+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):
@@ -28,20 +39,31 @@ class AIAgent:
     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}
+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. """
+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
 
     def autonomous_build(self, chat_history, workspace_projects):
         """
         Autonomous build logic that continues based on the state of chat history and workspace projects.
         """
-        # Example logic: Generate a summary of chat history and workspace state
         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()])
 
-        # Example: Generate the next logical step in the project
+        # 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
+
+        # 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."
 
         return summary, next_step
@@ -76,7 +98,7 @@ def chat_interface_with_agent(input_text, agent_name):
     if agent_prompt is None:
         return f"Agent {agent_name} not found."
 
-    model_name = "gpt2"
+    model_name = "Bin12345/AutoCoder_S_6.7B"
     try:
         model = AutoModelForCausalLM.from_pretrained(model_name)
         tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -84,101 +106,249 @@ def chat_interface_with_agent(input_text, agent_name):
     except EnvironmentError as e:
         return f"Error loading model: {e}"
 
-    # 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")
+    max_input_length = 900
     if input_ids.shape[1] > max_input_length:
         input_ids = input_ids[:, :max_input_length]
 
-    # Generate chatbot response
     outputs = model.generate(
-        input_ids, max_new_tokens=50, num_return_sequences=1, do_sample=True
+        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
 
+# 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
 
+# Code editor interface
 def code_editor_interface(code):
-    formatted_code = black.format_str(code, mode=black.FileMode())
-    pylint_output = lint.Run([formatted_code], do_exit=False)
-    pylint_output_str = StringIO()
-    pylint_output.linter.reporter.write_messages(pylint_output_str)
-    return formatted_code, pylint_output_str.getvalue()
+    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")
     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")
     result = analyzer(text)
     return result[0]['label']
 
+# Text translation tool (code translation)
 def translate_code(code, source_language, target_language):
-    # Placeholder for translation logic
-    return f"Translated {source_language} code to {target_language}."
+    # Use a Hugging Face translation model instead of OpenAI
+    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(idea):
-    # Placeholder for code generation logic
-    return f"Generated code based on the idea: {idea}."
+def generate_code(code_idea):
+    # Use a Hugging Face code generation model instead of OpenAI
+    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):
+    """Handles general chat interactions with the user."""
+    # Use a Hugging Face chatbot model or your own logic
+    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."
+        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}'."
 
-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."
+# Streamlit App
+st.title("AI Agent Creator")
 
-    model_name = "gpt2"
-    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}"
+# Sidebar navigation
+st.sidebar.title("Navigation")
+app_mode = st.sidebar.selectbox("Choose the app mode", ["AI Agent Creator", "Tool Box", "Workspace Chat App"])
 
-    # Combine the agent prompt with user input
-    combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:"
+if app_mode == "AI Agent Creator":
+    # AI Agent Creator
+    st.header("Create an AI Agent from Text")
 
-    # 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]
+    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)
 
-    # Adjust max_new_tokens if needed
-    max_new_tokens = 50  # Reduce if necessary
+elif app_mode == "Tool Box":
+    # Tool Box
+    st.header("AI-Powered Tools")
 
-    # Generate chatbot response
-    outputs = model.generate(
-        input_ids, max_new_tokens=max_new_tokens, num_return_sequences=1, do_sample=True
-    )
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return response
+    # 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)