Update app.py

app.py

@@ -1,6 +1,6 @@
 import os
-import subprocess
 import streamlit as st
+import subprocess
 from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, AutoModel, RagRetriever, AutoModelForSeq2SeqLM
 import black
 from pylint import lint
@@ -8,9 +8,12 @@ from io import StringIO
 import sys
 import torch
 from huggingface_hub import hf_hub_url, cached_download, HfApi
+import base64
 
 # Set your Hugging Face API key here
-hf_token = "YOUR_HUGGING_FACE_API_KEY"  # Replace with your actual token
+# hf_token = "YOUR_HUGGING_FACE_API_KEY"  # Replace with your actual token
+# Get Hugging Face token from secrets.toml - this line should already be in the main code
+hf_token = st.secrets["huggingface"]["hf_token"]
 
 HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/DevToolKit"
 PROJECT_ROOT = "projects"
@@ -71,24 +74,25 @@ def process_input(user_input):
     return refined_response
 
 class AIAgent:
-    def __init__(self, name, description, skills):
+    def __init__(self, name, description, skills, hf_api=None):
         self.name = name
         self.description = description
         self.skills = skills
+        self._hf_api = hf_api
+        self._hf_token = hf_token  # Store the token here
 
-    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
+    @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 has_valid_hf_token(self):
+        return bool(self._hf_token)
 
-    def autonomous_build(self, chat_history, workspace_projects, project_name, selected_model):
-        """
-        Autonomous build logic that continues based on the state of chat history and workspace projects.
-        """
+    async def autonomous_build(self, chat_history, workspace_projects, project_name, selected_model, hf_token):
+        self._hf_token = hf_token
+        # Continuation of previous methods
         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()])
 
@@ -139,6 +143,30 @@ I am confident that I can leverage my expertise to assist you in developing and
             st.error(f"Build Error: {e}")
 
         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 Hugoging Face API connection was established.")
+
+        # Assuming you have a function to get the files for your Space
+        repository_name = f"my-awesome-space_{datetime.now().timestamp()}" 
+        files = get_built_space_files() # Placeholder - you'll need to define this function
+
+        # Create the Space
+        create_space(self.hf_api, repository_name, "Description", True, files) 
+
+        st.markdown("## Congratulations! Successfully deployed Space 🚀 ##")
+        st.markdown(f"[Check out your new Space here](https://huggingface.co/spaces/{repository_name})")
+
+
+# Add any missing functions from your original code (e.g., get_built_space_files)
+def get_built_space_files():
+    # Replace with your logic to gather the files you want to deploy
+    return {
+        "app.py": "# Your Streamlit app code here",
+        "requirements.txt": "streamlit\ntransformers" 
+        # Add other files as needed
+    }
 
 def save_agent_to_file(agent):
     """Saves the agent's prompt to a file."""
@@ -280,6 +308,31 @@ 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 create_space(api, name, description, public, files, entrypoint="launch.py"):
+    url = f"{hf_hub_url()}spaces/{name}/prepare-repo"
+    headers = {"Authorization": f"Bearer {api.access_token}"}
+    payload = {
+        "public": public,
+        "gitignore_template": "web",
+        "default_branch": "main",
+        "archived": False,
+        "files": []
+    }
+    for filename, contents in files.items():
+        data = {
+            "content": contents,
+            "path": filename,
+            "encoding": "utf-8",
+            "mode": "overwrite" if "#\{random.randint(0, 1)\}" not in contents else "merge",
+        }
+        payload["files"].append(data)
+    response = requests.post(url, json=payload, headers=headers)
+    response.raise_for_status()
+    location = response.headers.get("Location")
+    # wait_for_processing(location, api)  # You might need to implement this if it's not already defined
+
+    return Repository(name=name, api=api)
+
 # Streamlit App
 st.title("AI Agent Creator")
 
@@ -363,7 +416,56 @@ elif app_mode == "Tool Box":
 elif app_mode == "Workspace Chat App":
     # Workspace Chat App
     st.header("Workspace Chat App")
+def get_built_space_files():
+    """
+    Gathers the necessary files for the Hugging Face Space, 
+    handling different project structures and file types.
+    """
+    files = {}
+
+    # Get the current project name (adjust as needed)
+    project_name = st.session_state.get('project_name', 'my_project') 
+    project_path = os.path.join(PROJECT_ROOT, project_name)
 
+    # Define a list of files/directories to search for
+    targets = [
+        "app.py", 
+        "requirements.txt",
+        "Dockerfile",  
+        "docker-compose.yml", # Example YAML file
+        "src",          # Example subdirectory
+        "assets"        # Another example subdirectory
+    ]
+
+    # Iterate through the targets
+    for target in targets:
+        target_path = os.path.join(project_path, target)
+
+        # If the target is a file, add it to the files dictionary
+        if os.path.isfile(target_path):
+            add_file_to_dictionary(files, target_path)
+
+        # If the target is a directory, recursively search for files within it
+        elif os.path.isdir(target_path):
+            for root, _, filenames in os.walk(target_path):
+                for filename in filenames:
+                    file_path = os.path.join(root, filename)
+                    add_file_to_dictionary(files, file_path)
+
+    return files
+
+def add_file_to_dictionary(files, file_path):
+    """Helper function to add a file to the files dictionary."""
+    filename = os.path.relpath(file_path, PROJECT_ROOT) # Get relative path
+
+    # Handle text and binary files
+    if filename.endswith((".py", ".txt", ".json", ".html", ".css", ".yml", ".yaml")):
+        with open(file_path, "r") as f:
+            files[filename] = f.read()
+    else:
+        with open(file_path, "rb") as f:
+            file_content = f.read()
+            files[filename] = base64.b64encode(file_content).decode("utf-8")
     # Project Workspace Creation
     st.subheader("Create a New Project")
     project_name = st.text_input("Enter project name:")
@@ -456,7 +558,19 @@ elif app_mode == "Workspace Chat App":
         st.write(summary)
         st.write("Next Step:")
         st.write(next_step)
-
+    
+    # Using the modified and extended class and functions, update the callback for the 'Automate' button in the Streamlit UI:
+    if st.button("Automate", args=(hf_token,)):
+    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 everything went well, proceed to deploy the Space
+    if agent._hf_api and agent.has_valid_hf_token():
+        agent.deploy_built_space_to_hf() 
         # Use the hf_token to interact with the Hugging Face API
         api = HfApi(token=hf_token)
         # Function to create a Space on Hugging Face