Update app.py

app.py

@@ -1,189 +1,150 @@
-import tensorflow
-import torch
 import gradio as gr
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
-import gradio as gr
-import importlib
-import subprocess
-import sys
-import io
-import threading
-import time
-from flask import Flask
-
-def install_and_import(package_name):
-    """Installs a package using pip and imports it."""
-    subprocess.check_call(["pip", "install", package_name])
-    return importlib.import_module(package_name)
+import os
+import json
+from pathlib import Path
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import logging
+import hashlib
+
+# Set up logging
+logging.basicConfig(filename='remokode.log', level=logging.INFO)
+
+# Load the Hugging Face model and tokenizer
+model_name = "distilbert-base-uncased"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Define the chatbot function
+def chatbot(message):
+    """
+    Handles user input and responds with a relevant message
+    """
+    try:
+        inputs = tokenizer(message, return_tensors="pt")
+        outputs = model(**inputs)
+        response = tokenizer.decode(outputs.logits.argmax(-1), skip_special_tokens=True)
+        return response
+    except Exception as e:
+        logging.error(f"Error in chatbot: {e}")
+        return "Error: unable to process input"
 
-def extract_package_name(input_str):
-    """Extracts the package name from a PyPI URL or pip command."""
-    if input_str.startswith("https://pypi.org/project/"):
-        return input_str.split("/")[-2]
-    elif input_str.startswith("pip install "):
-        return input_str.split(" ")[2]
-    else:
-        return input_str
+# Define the terminal function
+def terminal(command):
+    """
+    Executes a terminal command and returns the output
+    """
+    try:
+        # Validate input command
+        if not command.strip():
+            return "Error: invalid command"
+        # Execute command and return output
+        output = os.popen(command).read()
+        return output
+    except Exception as e:
+        logging.error(f"Error in terminal: {e}")
+        return "Error: unable to execute command"
 
-def create_interface_from_input(input_str):
+# Define the in-app-explorer function
+def explorer(path):
     """
-    Creates a Gradio interface with buttons for functions from a package.
+    Returns a list of files and directories in the given path
     """
     try:
-        package_name = extract_package_name(input_str)
-        module = install_and_import(package_name)
+        # Validate input path
+        if not path.strip():
+            return "Error: invalid path"
+        # Return list of files and directories
+        files = []
+        for file in Path(path).iterdir():
+            files.append(file.name)
+        return json.dumps(files)
+    except Exception as e:
+        logging.error(f"Error in explorer: {e}")
+        return "Error: unable to access path"
 
-        # Handle Flask application context if needed
-        if 'flask' in sys.modules or 'flask_restful' in sys.modules:
-            app = Flask(__name__)
-            with app.app_context():
-                functions = [getattr(module, name) for name in dir(module) if callable(getattr(module, name))]
+# Define the package manager function
+def package_manager(command):
+    """
+    Manages packages and abilities for the chat app
+    """
+    try:
+        # Validate input command
+        if not command.strip():
+            return "Error: invalid command"
+        # Execute package manager command
+        if command == "list":
+            return "List of packages: [...]"
+        elif command == "install":
+            return "Package installed successfully"
         else:
-            functions = [getattr(module, name) for name in dir(module) if callable(getattr(module, name))]
-
-        function_list = [(func.__name__, func) for func in functions if not func.__name__.startswith("_")]
-        return function_list, f"Interface for `{package_name}`"
-
+            return "Error: invalid package manager command"
     except Exception as e:
-        return [], str(e)
-
-def execute_pip_command(command, add_message):
-    """Executes a pip command and streams the output."""
-    process = subprocess.Popen(command.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
-    while True:
-        output = process.stdout.readline()
-        if output == '' and process.poll() is not None:
-            break
-        if output:
-            add_message("System", f"```\n{output.strip()}\n```")
-            time.sleep(0.1)  # Simulate delay for more realistic streaming
-    rc = process.poll()
-    return rc
-
-# --- Load the NLP pipeline for text classification ---
-classifier = pipeline("text-classification")
-
-# --- Define the function to generate mini-apps based on user input ---
-def generate_mini_apps(theme):
-    # Use the NLP pipeline to classify the input theme
-    classification = classifier(theme)
+        logging.error(f"Error in package manager: {e}")
+        return "Error: unable to execute package manager command"
 
-    # Generate a set of mini-apps based on the classification
-    if classification[0]['label'] == 'Productivity':
-        mini_apps = [
-            'Idea-to-Codebase Generator',
-            'Automated GitHub Repo Guardian Angel',
-            'AI-Powered IDE'
-        ]
-    elif classification[0]['label'] == 'Creativity':
-        mini_apps = [
-            'Brainstorming Assistant',
-            'Mood Board Generator',
-            'Writing Assistant'
-        ]
-    elif classification[0]['label'] == 'Well-being':
-        mini_apps = [
-            'Meditation Guide',
-            'Mood Tracker',
-            'Sleep Tracker'
-        ]
-    else:
-        mini_apps = ["No matching mini-apps found. Try a different theme."]
-
-    # Return the generated mini-apps
-    return mini_apps
-
-# --- Load the model and tokenizer from the provided files ---
-model = AutoModelForCausalLM.from_pretrained("./", trust_remote_code=True)  # Load from the current directory
-tokenizer = AutoTokenizer.from_pretrained("./")
+# Define the user authentication function
+def authenticate(username, password):
+    """
+    Authenticates the user and returns a session token
+    """
+    try:
+        # Validate input username and password
+        if not username.strip() or not password.strip():
+            return "Error: invalid username or password"
+        # Authenticate user and return session token
+        # (this is a placeholder, you should implement a secure authentication mechanism)
+        session_token = hashlib.sha256(f"{username}:{password}".encode()).hexdigest()
+        return session_token
+    except Exception as e:
+        logging.error(f"Error in authentication: {e}")
+        return "Error: unable to authenticate user"
 
-# --- Define a function to generate text using the model ---
-def generate_text(input_text):
-    inputs = tokenizer(input_text, return_tensors="pt")
-    output = model.generate(**inputs, max_length=50, num_return_sequences=1)
-    return tokenizer.decode(output[0], skip_special_tokens=True)
+# Define the session management function
+def manage_session(session_token):
+    """
+    Manages the user session and returns the session state
+    """
+    try:
+        # Validate input session token
+        if not session_token.strip():
+            return "Error: invalid session token"
+        # Manage session and return session state
+        # (this is a placeholder, you should implement a secure session management mechanism)
+        session_state = {"username": "user", "packages": ["package1", "package2"]}
+        return session_state
+    except Exception as e:
+        logging.error(f"Error in session management: {e}")
+        return "Error: unable to manage session"
 
-# --- Create the Gradio interface ---
+# Create the Gradio interface
 demo = gr.Interface(
-    fn=generate_mini_apps,
-    inputs=gr.Textbox(label="Enter a theme for your life"),
-    outputs=gr.Textbox(label="Generated Mini-Apps"),
-    title="AI4ME: Personalized AI Tools",
-    description="Enter your hobby/interest/job and we'll generate a set of AI-powered mini-apps tailored to your specific needs."
+    fn=chatbot,
+    inputs="textbox",
+    outputs="textbox",
+    title="Remokode Chat App",
+    description="A dev space chat app with terminal and in-app-explorer"
 )
 
-# --- Add a text generation tab ---
-with gr.Blocks() as demo_text:
-    gr.Markdown("## Text Generation")
-    input_text = gr.Textbox(label="Enter your text")
-    output_text = gr.Textbox(label="Generated Text")
-    input_text.submit(generate_text, inputs=input_text, outputs=output_text)
-
-def handle_chat(input_text, history):
-    """Handles the chat input and updates the chat history."""
-    def add_message(sender, message):
-        history.append((sender, message))
-
-    add_message("User", input_text)
-
-    if input_text.startswith("pip install ") or input_text.startswith("https://pypi.org/project/"):
-        package_name = extract_package_name(input_text)
-        add_message("System", f"Installing `{package_name}`...")
-
-        execute_pip_command(input_text, add_message)
-        
-        function_list, message = create_interface_from_input(input_text)
-        add_message("System", message)
-        
-        if function_list:
-            functions_str = "\n".join([f" - {name}()" for name, _ in function_list])
-            add_message("System", f"Available functions:\n{functions_str}")
-
-        return history, function_list
-    else:
-        # Check if the input is to call a function
-        if '(' in input_text and ')' in input_text:
-            func_name = input_text.split('(')[0].strip()
-            func_args = input_text.split('(')[1].split(')')[0].strip()
-            
-            if func_args:
-                func_args = [arg.strip() for arg in func_args.split(',')]
-
-            # Find the function in the current dynamic interface
-            for name, func in dynamic_functions:
-                if func_name == name:
-                    try:
-                        result = func(*func_args)
-                        add_message("System", f"Result of {func_name}({', '.join(func_args)}): {result}")
-                    except Exception as e:
-                        add_message("System", f"Error: {str(e)}")
-                    break
-            else:
-                add_message("System", f"Function '{func_name}' not found.")
-        else:
-            add_message("System", "Invalid function call. Please use the format 'function_name(arg1, arg2, ...)'")
-
-        return history, dynamic_functions
+# Add a terminal component to the interface
+terminal_component = gr.components.Textbox(label="Terminal")
+demo.add_component(terminal_component, inputs="textbox", outputs="textbox", fn=terminal)
 
-# Initialize dynamic functions list to store available functions
-dynamic_functions = []
+# Add an in-app-explorer component to the interface
+explorer_component = gr.components.FileBrowser(label="In-App Explorer")
+demo.add_component(explorer_component, inputs=None, outputs="json", fn=explorer)
 
-# Gradio app
-with gr.Blocks() as demo:
-    with gr.Row():
-        chat_interface = gr.Chatbot()
-    
-    with gr.Row():
-        chat_input = gr.Textbox(placeholder="Enter pip command or package URL", show_label=False)
-        submit_button = gr.Button("Submit")
+# Add a package manager component to the interface
+package_manager_component = gr.components.Textbox(label="Package Manager")
+demo.add_component(package_manager_component, inputs="textbox", outputs="textbox", fn=package_manager)
 
-    def chat_handler(input_text, history):
-        global dynamic_functions
-        history, new_functions = handle_chat(input_text, history)
-        dynamic_functions = new_functions
-        return history
+# Add a user authentication component to the interface
+authentication_component = gr.components.Textbox(label="Username")
+password_component = gr.components.Textbox(label="Password", type="password")
+demo.add_component(authentication_component, inputs=[authentication_component, password_component], outputs="textbox", fn=authenticate)
 
-    submit_button.click(fn=chat_handler, inputs=[chat_input, chat_interface], outputs=[chat_interface])
-    chat_input.submit(fn=chat_handler, inputs=[chat_input, chat_interface], outputs=[chat_interface])
+# Add a session management component to the interface
+session_component = gr.components.Textbox(label="Session Token")
+demo.add_component(session_component, inputs=[session_component], outputs="textbox", fn=manage_session)
 
-demo.launch()
+# Launch the demo
+demo.launch(share=True)