Update app.py

app.py

@@ -1,349 +1,886 @@
 import os
+
+import sys
+
 import subprocess
+
 import streamlit as st
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, AutoConfig, AutoModel
 
-HF_TOKEN = os.getenv("HF_TOKEN")
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+
+import black
+
+from pylint import lint
+
+from io import StringIO
+
+import openai
+
+
+
+
+# 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"
 
+
+
+
 # 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
 
+
+
+
     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()])
 
-        # 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
 
-def load_hf_token():
-    return 'YOUR_HF_TOKEN'
+
+
 
 def save_agent_to_file(agent):
-    """Saves the agent's prompt to a file."""
+
+    """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()
 
+
+
+
+# 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."
 
-    model_name = "Bin12345/AutoCoder_S_6.7B"
+
+
+
+    # Load the GPT-2 model which is compatible with AutoModelForCausalLM
+
+    model_name = "gpt2"
+
     try:
-        model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=load_hf_token())
-        tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=load_hf_token())
+
+        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}"
 
+
+
+
+    # Combine the agent prompt with user input
+
     combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:"
+
     
-    input_ids = tokenizer.encode(combined_input, return_tensors="pt")
+
+    # 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]
 
+
+
+
+    # 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
+
+        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 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 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."
+
+    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, shell=True, capture_output=True, text=True, cwd=project_path)
+
+        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)
-    return result.stdout
 
-# Code editor interface
+    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
 
-# Text summarization tool
+
+
+
 def summarize_text(text):
-    summarizer = pipeline("summarization", model="t5-base", use_auth_token=load_hf_token())
-    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
+
+    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']
 
-# Sentiment analysis tool
+
+
+
 def sentiment_analysis(text):
-    analyzer = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment", use_auth_token=load_hf_token())
-    result = analyzer(text)
-    return result[0]['label']
-
-# Text translation tool (code translation)
-def translate_code(code, source_language, target_language):
-    # Use a Hugging Face translation model instead of OpenAI
-    translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es", use_auth_token=load_hf_token())  # Example: English to Spanish
-    translated_code = translator(code, target_lang=target_language)[0]['translation_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):
-    # Use a Hugging Face code generation model instead of OpenAI
-    generator = pipeline('text-generation', model='bigcode/starcoder', use_auth_token=load_hf_token())
-    generated_code = generator(code_idea, max_length=1000, num_return_sequences=1)[0]['generated_text']
+
+    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 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", use_auth_token=load_hf_token())
-    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}'."
+
+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"):
-        chat_response = chat_interface(chat_input)
+
+        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'):")
+
+    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'):")
+
+    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.write(next_step)
+
+
+
+
+# Display current state for debugging
+
+st.sidebar.subheader("Current State")
+
+st.sidebar.json(st.session_state.current_state)