Update app.py

app.py

@@ -1,101 +1,224 @@
+import os
+import subprocess
+import random
+import json
+from datetime import datetime
+from huggingface_hub import InferenceClient, cached_download, hf_hub_url
 import gradio as gr
-from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
-from typing import List, Dict, Any
-
-# --- Agent Definitions ---
-
-class Agent:
-    def __init__(self, name: str, role: str, skills: List[str], model_name: str = None):
-        self.name = name
-        self.role = role
-        self.skills = skills
-        self.model = None
-        if model_name:
-            self.load_model(model_name)
-
-    def load_model(self, model_name: str):
-        self.model = pipeline(task="text-classification", model=model_name)
-
-    def handle_task(self, task: str) -> str:
-        # Placeholder for task handling logic
-        # This is where each agent will implement its specific behavior
-        return f"Agent {self.name} received task: {task}"
-
-class AgentCluster:
-    def __init__(self, agents: List[Agent]):
-        self.agents = agents
-        self.task_queue = []
-
-    def add_task(self, task: str):
-        self.task_queue.append(task)
-
-    def process_tasks(self):
-        for task in self.task_queue:
-            # Assign task to the most suitable agent based on skills
-            best_agent = self.find_best_agent(task)
-            if best_agent:
-                result = best_agent.handle_task(task)
-                print(f"Agent {best_agent.name} completed task: {task} - Result: {result}")
-            else:
-                print(f"No suitable agent found for task: {task}")
-        self.task_queue = []
-
-    def find_best_agent(self, task: str) -> Agent:
-        # Placeholder for agent selection logic
-        # This is where the cluster will determine which agent is best for a given task
-        return self.agents[0] # For now, just return the first agent
-
-# --- Agent Clusters for Different Web Apps ---
-
-# Agent Cluster for a Code Review Tool
-code_review_agents = AgentCluster([
-    Agent("CodeAnalyzer", "Code Reviewer", ["Python", "JavaScript", "C++"], "distilbert-base-uncased-finetuned-mrpc"),
-    Agent("StyleChecker", "Code Stylist", ["Code Style", "Readability", "Best Practices"], "google/flan-t5-base"),
-    Agent("SecurityScanner", "Security Expert", ["Vulnerability Detection", "Security Best Practices"], "google/flan-t5-base"),
-])
-
-# Agent Cluster for a Project Management Tool
-project_management_agents = AgentCluster([
-    Agent("TaskManager", "Project Manager", ["Task Management", "Prioritization", "Deadline Tracking"], "google/flan-t5-base"),
-    Agent("ResourceAllocator", "Resource Manager", ["Resource Allocation", "Team Management", "Project Planning"], "google/flan-t5-base"),
-    Agent("ProgressTracker", "Progress Monitor", ["Progress Tracking", "Reporting", "Issue Resolution"], "google/flan-t5-base"),
-])
-
-# Agent Cluster for a Documentation Generator
-documentation_agents = AgentCluster([
-    Agent("DocWriter", "Documentation Writer", ["Technical Writing", "API Documentation", "User Guides"], "google/flan-t5-base"),
-    Agent("CodeDocumenter", "Code Commenter", ["Code Documentation", "Code Explanation", "Code Readability"], "google/flan-t5-base"),
-    Agent("ContentOrganizer", "Content Manager", ["Content Structure", "Information Architecture", "Content Organization"], "google/flan-t5-base"),
-])
-
-# --- Web App Logic ---
-
-def process_input(input_text: str, selected_cluster: str):
-    """Processes user input and assigns tasks to the appropriate agent cluster."""
-    if selected_cluster == "Code Review":
-        cluster = code_review_agents
-    elif selected_cluster == "Project Management":
-        cluster = project_management_agents
-    elif selected_cluster == "Documentation Generation":
-        cluster = documentation_agents
+from safe_search import safe_search
+from i_search import google, i_search as i_s
+from agent import (
+    ACTION_PROMPT, ADD_PROMPT, COMPRESS_HISTORY_PROMPT, LOG_PROMPT,
+    LOG_RESPONSE, MODIFY_PROMPT, PRE_PREFIX, SEARCH_QUERY, READ_PROMPT,
+    TASK_PROMPT, UNDERSTAND_TEST_RESULTS_PROMPT
+)
+from utils import parse_action, parse_file_content, read_python_module_structure
+
+# Global Variables for App State
+app_state = {"components": []}
+terminal_history = ""
+
+# Component Library
+components_registry = {
+    "Button": {
+        "properties": {"label": "Click Me", "onclick": ""},
+        "description": "A clickable button",
+        "code_snippet": 'gr.Button(value="{label}", variant="primary")',
+    },
+    "Text Input": {
+        "properties": {"value": "", "placeholder": "Enter text"},
+        "description": "A field for entering text",
+        "code_snippet": 'gr.Textbox(label="{placeholder}")',
+    },
+    "Image": {
+        "properties": {"src": "#", "alt": "Image"},
+        "description": "Displays an image",
+        "code_snippet": 'gr.Image(label="{alt}")',
+    },
+    "Dropdown": {
+        "properties": {"choices": ["Option 1", "Option 2"], "value": ""},
+        "description": "A dropdown menu for selecting options",
+        "code_snippet": 'gr.Dropdown(choices={choices}, label="Dropdown")',
+    },
+    # Add more components here...
+}
+
+# NLP Model (Example using Hugging Face)
+nlp_model_names = [
+    "google/flan-t5-small",
+    "Qwen/CodeQwen1.5-7B-Chat-GGUF",
+    "bartowski/Codestral-22B-v0.1-GGUF",
+    "bartowski/AutoCoder-GGUF"
+]
+nlp_models = []
+
+for nlp_model_name in nlp_model_names:
+    try:
+        cached_download(hf_hub_url(nlp_model_name, revision="main"))
+        nlp_models.append(InferenceClient(nlp_model_name))
+    except:
+        nlp_models.append(None)
+
+# Function to get NLP model response
+def get_nlp_response(input_text, model_index):
+    if nlp_models[model_index]:
+        response = nlp_models[model_index].text_generation(input_text)
+        return response.generated_text
     else:
-        return "Please select a valid agent cluster."
-
-    cluster.add_task(input_text)
-    cluster.process_tasks()
-    return "Task processed successfully!"
-
-# --- Gradio Interface ---
-
-with gr.Blocks() as demo:
-    gr.Markdown("## Agent-Powered Development Automation")
-    input_text = gr.Textbox(label="Enter your development task:")
-    selected_cluster = gr.Radio(
-        label="Select Agent Cluster", choices=["Code Review", "Project Management", "Documentation Generation"]
-    )
-    submit_button = gr.Button("Submit")
-    output_text = gr.Textbox(label="Output")
-
-    submit_button.click(process_input, inputs=[input_text, selected_cluster], outputs=output_text)
-
-demo.launch()
+        return "NLP model not available."
+
+# Component Class
+class Component:
+    def __init__(self, type, properties=None, id=None):
+        self.id = id or random.randint(1000, 9999)
+        self.type = type
+        self.properties = properties or components_registry[type]["properties"].copy()
+
+    def to_dict(self):
+        return {
+            "id": self.id,
+            "type": self.type,
+            "properties": self.properties,
+        }
+
+    def render(self):
+        # Properly format choices for Dropdown
+        if self.type == "Dropdown":
+            self.properties["choices"] = (
+                str(self.properties["choices"])
+                .replace("[", "")
+                .replace("]", "")
+                .replace("'", "")
+            )
+        return components_registry[self.type]["code_snippet"].format(**self.properties)
+
+# Function to update the app canvas (for preview)
+def update_app_canvas():
+    components_html = "".join([
+        f"<div>Component ID: {component['id']}, Type: {component['type']}, Properties: {component['properties']}</div>"
+        for component in app_state["components"]
+    ])
+    return components_html
+
+# Function to handle component addition
+def add_component(component_type):
+    if component_type in components_registry:
+        new_component = Component(component_type)
+        app_state["components"].append(new_component.to_dict())
+        return update_app_canvas(), f"System: Added component: {component_type}\n"
+    else:
+        return None, f"Error: Invalid component type: {component_type}\n"
+
+# Function to handle terminal input
+def run_terminal_command(command, history):
+    global terminal_history
+    output = ""
+    try:
+        # Basic command parsing (expand with NLP)
+        if command.startswith("add "):
+            component_type = command.split("add ", 1)[1].strip()
+            _, output = add_component(component_type)
+        elif command.startswith("set "):
+            _, output = set_component_property(command)
+        elif command.startswith("search "):
+            search_query = command.split("search ", 1)[1].strip()
+            output = i_s(search_query)
+        elif command.startswith("deploy "):
+            app_name = command.split("deploy ", 1)[1].strip()
+            output = deploy_to_huggingface(app_name)
+        else:
+            # Attempt to execute command as Python code
+            try:
+                result = subprocess.check_output(
+                    command, shell=True, stderr=subprocess.STDOUT, text=True
+                )
+                output = result
+            except Exception as e:
+                output = f"Error executing Python code: {str(e)}"
+    except Exception as e:
+        output = f"Error: {str(e)}"
+    finally:
+        terminal_history += f"User: {command}\n{output}\n"
+    return terminal_history
+
+def set_component_property(command):
+    try:
+        # Improved 'set' command parsing
+        set_parts = command.split(" ", 2)[1:]
+        if len(set_parts) != 2:
+            raise ValueError("Invalid 'set' command format.")
+        component_id = int(set_parts[0])  # Use component ID
+        property_name, property_value = set_parts[1].split("=", 1)
+        # Find component by ID
+        component_found = False
+        for component in app_state["components"]:
+            if component["id"] == component_id:
+                if property_name in component["properties"]:
+                    component["properties"][property_name.strip()] = property_value.strip()
+                    component_found = True
+                    return update_app_canvas(), f"System: Property '{property_name}' set to '{property_value}' for component {component_id}\n"
+                else:
+                    return None, f"Error: Property '{property_name}' not found in component {component_id}\n"
+        if not component_found:
+            return None, f"Error: Component with ID {component_id} not found.\n"
+    except Exception as e:
+        return None, f"Error: {str(e)}\n"
+
+# Function to handle chat interaction
+def run_chat(message, history):
+    global terminal_history
+    if message.startswith("!"):
+        command = message[1:]
+        terminal_history = run_terminal_command(command, history)
+    else:
+        model_index = 0  # Select the model to use for chat response
+        response = get_nlp_response(message, model_index)
+        if response:
+            return history, terminal_history + f"User: {message}\nAssistant: {response}"
+        else:
+            return history, terminal_history + f"User: {message}\nAssistant: I'm sorry, I couldn't generate a response. Please try again.\n"
+
+# Code Generation
+def generate_python_code(app_name):
+    code = f"""import gradio as gr\n\nwith gr.Blocks() as {app_name}:\n"""
+    for component in app_state["components"]:
+        code += "    " + Component(**component).render() + "\n"
+    code += f"\n{app_name}.launch()\n"
+    return code
+
+# Hugging Face Deployment
+def deploy_to_huggingface(app_name):
+    # Generate Python code
+    code = generate_python_code(app_name)
+    # Create requirements.txt
+    with open("requirements.txt", "w") as f:
+        f.write("gradio==3.32.0\n")
+    # Create the app.py file
+    with open("app.py", "w") as f:
+        f.write(code)
+    # Execute the deployment command
+    try:
+        subprocess.run(["huggingface-cli", "repo", "create", "--type", "space", "--space_sdk", "gradio", app_name], check=True)
+        subprocess.run(["git", "init"], cwd=f"./{app_name}", check=True)
+        subprocess.run(["git", "add", "."], cwd=f"./{app_name}", check=True)
+        subprocess.run(["git", "commit", "-m", "Initial commit"], cwd=f"./{app_name}", check=True)
+        subprocess.run(["git", "push", "https://huggingface.co/spaces/" + app_name, "main"], cwd=f"./{app_name}", check=True)
+        return f"Successfully deployed to Hugging Face Spaces: https://huggingface.co/spaces/{app_name}"
+    except Exception as e:
+        return f"Error deploying to Hugging Face Spaces: {e}"
+
+# Gradio Interface
+with gr.Blocks() as iface:
+    # Chat Interface
+    chat_history = gr.Chatbot(label="Chat with Agent")
+    chat_input = gr.Textbox(label="Your Message")
+    chat_button = gr.Button("Send")
+    chat_button.click(run_chat, inputs=[chat_input, chat_history], outputs=[chat_history, terminal_output])
+    
+    # Terminal
+    terminal_output = gr.Textbox(lines=8, label="Terminal", value=terminal_history)
+    terminal_input = gr.Textbox(label="Enter Command")
+    terminal_button = gr.Button("Run")
+    terminal_button.click(run_terminal_command, inputs=[terminal_input, terminal_output], outputs=terminal_output)
+
+iface.launch()