Update app.py

app.py

@@ -1,21 +1,14 @@
+import streamlit as st
 import os
 import subprocess
-import streamlit as st
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, AutoModel, RagRetriever, AutoModelForSeq2SeqLM
-import black
-from pylint import lint
-from io import StringIO
-import sys
-import torch
-from huggingface_hub import hf_hub_url, cached_download, HfApi
-from datetime import datetime
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, HfApi
 
 # Set your Hugging Face API key here
 hf_token = "YOUR_HUGGING_FACE_API_KEY"  # Replace with your actual token
 
-HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/DevToolKit"
 PROJECT_ROOT = "projects"
 AGENT_DIRECTORY = "agents"
+AVAILABLE_CODE_GENERATIVE_MODELS = ["bigcode/starcoder", "Salesforce/codegen-350M-mono", "microsoft/CodeGPT-small"]
 
 # Global state to manage communication between Tool Box and Workspace Chat App
 if 'chat_history' not in st.session_state:
@@ -26,93 +19,51 @@ 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': {}
-    }
-
-# List of top downloaded free code-generative models from Hugging Face Hub
-AVAILABLE_CODE_GENERATIVE_MODELS = [
-    "bigcode/starcoder",  # Popular and powerful
-    "Salesforce/codegen-350M-mono",  # Smaller, good for quick tasks
-    "microsoft/CodeGPT-small",  # Smaller, good for quick tasks
-    "google/flan-t5-xl",  # Powerful, good for complex tasks
-    "facebook/bart-large-cnn",  # Good for text-to-code tasks
-]
-
-# Load pre-trained RAG retriever
-rag_retriever = RagRetriever.from_pretrained("neural-bridge/rag-dataset-1200")  # Use a Hugging Face RAG model
-
-# Load pre-trained chat model
-chat_model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/DialoGPT-medium")  # Use a Hugging Face chat model
-
-# Load tokenizer
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
 
 class AIAgent:
-    def __init__(self, name, description, skills, hf_api=None):
+    def __init__(self, name, description, skills):
         self.name = name
         self.description = description
         self.skills = skills
-        self._hf_api = hf_api
-        self._hf_token = hf_token  # Store the token here
 
-    @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 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 has_valid_hf_token(self):
-        return bool(self._hf_token)
+    def autonomous_build(self, chat_history, workspace_projects, project_name, selected_model, hf_token):
+        """
+        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()])
 
-    async def autonomous_build(self, chat_history, workspace_projects, project_name, selected_model, hf_token):
-        self._hf_token = hf_token
-        # Continuation of previous methods
+        # Example: Generate the next logical step in the project
+        next_step = "Based on the current state, the next logical step is to implement the main application logic."
+
+        return summary, next_step
 
     def deploy_built_space_to_hf(self):
-        if not self._hf_api or not self._hf_token:
-            raise ValueError("Cannot deploy the Space since no valid Hugging Face API connection was established.")
-        repository_name = f"my-awesome-space_{datetime.now().timestamp()}"
-        files = get_built_space_files()
-        commit_response = self.hf_api.commit_repo(
-            repo_id=repository_name,
-            branch="main",
-            commits=[{"message": "Built Space Commit", "tree": tree_payload}]
-        )
-        print("Commit successful:", commit_response)
-        self.publish_space(repository_name)
-
-    def publish_space(self, repository_name):
-        publishing_response = self.hf_api.create_model_version(
-            model_name=repository_name,
-            repo_id=repository_name,
-            model_card={},
-            library_card={}
-        )
-        print("Space published:", publishing_response)
-
-def process_input(user_input):
-    # 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
+        # Implement deployment logic here
+        pass
+
+def process_input(input_text):
+    chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium", tokenizer="microsoft/DialoGPT-medium")
+    response = chatbot(input_text, max_length=50, num_return_sequences=1)[0]['generated_text']
+    return response
+
+def run_code(code):
+    try:
+        result = subprocess.run(code, shell=True, capture_output=True, text=True)
+        return result.stdout
+    except Exception as e:
+        return str(e)
 
 def workspace_interface(project_name):
     project_path = os.path.join(PROJECT_ROOT, project_name)
@@ -134,130 +85,15 @@ def add_code_to_workspace(project_name, code, file_name):
     st.session_state.workspace_projects[project_name]['files'].append(file_name)
     return f"Code added to '{file_name}' in project '{project_name}'."
 
-def run_code(command, project_name=None):
-    if project_name:
-        project_path = os.path.join(PROJECT_ROOT, project_name)
-        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 display_chat_history(history):
-    chat_history = ""
-    for user_input, response in history:
-        chat_history += f"User: {user_input}\nAgent: {response}\n\n"
-    return chat_history
-
-def display_workspace_projects(projects):
-    workspace_projects = ""
-    for project, details in projects.items():
-        workspace_projects += f"Project: {project}\nFiles:\n"
-        for file in details['files']:
-            workspace_projects += f"  - {file}\n"
-    return workspace_projects
-
-# 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"):
-        skills = text_input.split('\n')
-        agent = AIAgent(agent_name, "AI agent created from text input", skills)
-        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"):
-        response = process_input(chat_input)
-        st.session_state.chat_history.append((chat_input, response))
-        st.write(f"CodeCraft: {response}")
-
-    # Terminal Interface
-    st.subheader("Terminal")
-    terminal_input = st.text_input("Enter a command:")
-    if st.button("Run"):
-        output = run_code(terminal_input)
-        st.session_state.terminal_history.append((terminal_input, output))
-        st.code(output, language="bash")
-
-    # Project Management
-    st.subheader("Project Management")
-    project_name_input = st.text_input("Enter Project Name:")
-    if st.button("Create Project"):
-        status = workspace_interface(project_name_input)
-        st.write(status)
-
-    code_to_add = st.text_area("Enter Code to Add to Workspace:", height=150)
-    file_name_input = st.text_input("Enter File Name (e.g., 'app.py'):")
-    if st.button("Add Code"):
-        status = add_code_to_workspace(project_name_input, code_to_add, file_name_input)
-        st.write(status)
-
-    # Display Chat History
-    st.subheader("Chat History")
-    chat_history = display_chat_history(st.session_state.chat_history)
-    st.text_area("Chat History", value=chat_history, height=200)
-
-    # Display Workspace Projects
-    st.subheader("Workspace Projects")
-    workspace_projects = display_workspace_projects(st.session_state.workspace_projects)
-    st.text_area("Workspace Projects", value=workspace_projects, height=200)
-
-elif app_mode == "Workspace Chat App":
-    # Workspace Chat App
-    st.header("Workspace Chat App")
-
-    # Chat Interface 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"):
-        response = process_input(agent_chat_input)
-        st.session_state.chat_history.append((agent_chat_input, response))
-        st.write(f"{selected_agent}: {response}")
-
-    # Code Generation
-    st.subheader("Code Generation")
-    code_idea = st.text_input("Enter your code idea:")
-    selected_model = st.selectbox("Select a code-generative model", AVAILABLE_CODE_GENERATIVE_MODELS)
-    if st.button("Generate Code"):
-        generated_code = run_code(code_idea)
-        st.code(generated_code, language="python")
-
-    # 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, project_name, selected_model, hf_token)
-        st.write("Autonomous Build Summary:")
-        st.write(summary)
-        st.write("Next Step:")
-        st.write(next_step)
-
-        if agent._hf_api and agent.has_valid_hf_token():
-            repository = agent.deploy_built_space_to_hf()
-            st.markdown("## Congratulations! Successfully deployed Space 🚀 ##")
-            st.markdown("[Check out your new Space here](hf.co/" + repository.name + ")")
-
-    if __name__ == "__main__":
-        st.sidebar.title("Navigation")
-        app_mode = st.sidebar.selectbox("Choose the app mode", ["AI Agent Creator", "Tool Box", "Workspace Chat App"])
+def display_chat_history(chat_history):
+    return "\n".join([f"User: {u}\nAgent: {a}" for u, a in chat_history])
+
+def display_workspace_projects(workspace_projects):
+    return "\n".join([f"{p}: {details}" for p, details in workspace_projects.items()])
+
+if __name__ == "__main__":
+    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
@@ -333,14 +169,15 @@ elif app_mode == "Workspace Chat App":
         code_idea = st.text_input("Enter your code idea:")
         selected_model = st.selectbox("Select a code-generative model", AVAILABLE_CODE_GENERATIVE_MODELS)
         if st.button("Generate Code"):
-            generated_code = run_code(code_idea)
+            generator = pipeline("text-generation", model=selected_model, tokenizer=selected_model)
+            generated_code = generator(code_idea, max_length=150, num_return_sequences=1)[0]['generated_text']
             st.code(generated_code, language="python")
 
         # 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, project_name, selected_model, hf_token)
+            summary, next_step = agent.autonomous_build(st.session_state.chat_history, st.session_state.workspace_projects, project_name_input, selected_model, hf_token)
             st.write("Autonomous Build Summary:")
             st.write(summary)
             st.write("Next Step:")