Update app.py

app.py

@@ -1,242 +1,101 @@
-import asyncio
-import logging
-from typing import Dict, Any
-from functools import partial
-import warnings
+import gradio as gr
+from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
+from typing import List, Dict, Any
 
-from flask import Flask, request, jsonify, render_template
-from transformers import pipeline
+# --- Agent Definitions ---
 
-# Suppress the FutureWarning
-warnings.filterwarnings("ignore", category=FutureWarning)
+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
+    else:
+        return "Please select a valid agent cluster."
 
-logging.basicConfig(level=logging.INFO)
+    cluster.add_task(input_text)
+    cluster.process_tasks()
+    return "Task processed successfully!"
 
-# Define core component classes
-class Task:
-    def __init__(self, task_name: str, input_data: Any, agent_name: str):
-        self.task_name = task_name
-        self.input_data = input_data
-        self.agent_name = agent_name
+# --- Gradio Interface ---
 
-class ModelManager:
-    def __init__(self):
-        self.model = None
+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")
 
-    async def start(self):
-        logging.info("Starting model.")
-        await asyncio.sleep(1)  # Simulate loading time
-
-    async def stop(self):
-        logging.info("Unloading model.")
-
-class CodeArchitect:
-    def __init__(self, model_manager: ModelManager, model=None):
-        self.model_manager = model_manager
-        self.generator = model if model else pipeline("text-generation", model="gpt2")
-
-    async def start(self):
-        await self.model_manager.start()
-
-    async def stop(self):
-        await self.model_manager.stop()
-
-    async def generate_code(self, text_input: str) -> str:
-        response = self.generator(text_input, max_length=5000, num_return_sequences=1)[0]['generated_text']
-        return response
-
-class UIUXWizard:
-    def __init__(self, model_manager: ModelManager, vector_store=None):
-        self.model_manager = model_manager
-        self.vector_store = vector_store
-        self.conversation_chain = pipeline("text-generation", model="gpt2")
-
-    async def start(self):
-        await self.model_manager.start()
-
-    async def stop(self):
-        await self.model_manager.stop()
-
-    def get_memory_response(self, query):
-        if self.vector_store is None:
-            return "No memory available."
-        else:
-            results = self.vector_store.similarity_search(query, k=3)
-            return "\n".join(results)
-
-    def get_conversation_response(self, query):
-        response = self.conversation_chain(query, max_length=5000, num_return_sequences=1)[0]['generated_text']
-        return response
-
-# Define VersionControl class
-class VersionControl:
-    def __init__(self, system_name: str):
-        self.system_name = system_name
-
-    async def start(self):
-        logging.info(f"Starting version control system: {self.system_name}")
-        await asyncio.sleep(1)  # Simulate initialization time
-
-    async def stop(self):
-        logging.info(f"Stopping version control system: {self.system_name}")
-
-# Define Documentation class
-class Documentation:
-    def __init__(self, system_name: str):
-        self.system_name = system_name
-
-    async def start(self):
-        logging.info(f"Starting documentation system: {self.system_name}")
-        await asyncio.sleep(1)  # Simulate initialization time
-
-    async def stop(self):
-        logging.info(f"Stopping documentation system: {self.system_name}")
-
-class BuildAutomation:
-    def __init__(self, system_name: str):
-        self.system_name = system_name
-
-    async def start(self):
-        logging.info(f"Starting build automation system: {self.system_name}")
-        await asyncio.sleep(1)  # Simulate initialization time
-
-    async def stop(self):
-        logging.info(f"Stopping build automation system: {self.system_name}")
-
-# Define EliteDeveloperCluster class
-class EliteDeveloperCluster:
-    def __init__(self, config: Dict[str, Any], model):
-        self.config = config
-        self.model_manager = ModelManager()
-        self.code_architect = CodeArchitect(self.model_manager, model)
-        self.uiux_wizard = UIUXWizard(self.model_manager)
-        self.version_control = VersionControl(config["version_control_system"])
-        self.documentation = Documentation(config["documentation_system"])
-        self.build_automation = BuildAutomation(config["build_automation_system"])
-        self.task_queue = asyncio.Queue()
-
-    async def start(self):
-        await self.code_architect.start()
-        await self.uiux_wizard.start()
-        await self.version_control.start()
-        await self.documentation.start()
-        await self.build_automation.start()
-
-    async def stop(self):
-        await self.code_architect.stop()
-        await self.uiux_wizard.stop()
-        await self.version_control.stop()
-        await self.documentation.stop()
-        await self.build_automation.stop()
-
-    async def process_task(self, task: Task):
-        if task.task_name == "generate_code":
-            response = await self.code_architect.generate_code(task.input_data)
-            return response
-        elif task.task_name == "get_memory_response":
-            response = self.uiux_wizard.get_memory_response(task.input_data)
-            return response
-        elif task.task_name == "get_conversation_response":
-            response = self.uiux_wizard.get_conversation_response(task.input_data)
-            return response
-        else:
-            return f"Unknown task: {task.task_name}"
-
-    async def process_tasks(self):
-        while True:
-            task = await self.task_queue.get()
-            response = await self.process_task(task)
-            logging.info(f"Processed task: {task.task_name} for agent: {task.agent_name}")
-            self.task_queue.task_done()
-            yield response
-
-    def route_request(self, query: str) -> str:
-        # TODO: Implement logic to determine the appropriate agent based on query
-        # For now, assume all requests are for the UIUXWizard
-        return self.uiux_wizard.get_conversation_response(query)
-
-# Flask App for handling agent requests
-app = Flask(__name__)
-
-@app.route('/')
-def index():
-    return render_template('index.html')
-
-@app.route('/agent', methods=['POST'])
-async def agent_request():
-    data = request.get_json()
-    if data.get('input_value'):
-        # Process request from any agent (Agent 2, Agent 3, etc.)
-        task = Task(f"Process request from {data.get('agent_name', 'unknown agent')}", data.get('input_value'), data.get('agent_name', 'unknown agent'))
-        await cluster.task_queue.put(task)
-        return jsonify({'response': 'Received input: from an agent, task added to queue.'})
-    else:
-        return jsonify({'response': 'Invalid input'})
-
-@app.route('/chat', methods=['POST'])
-async def chat():
-    data = request.get_json()
-    query = data.get('query')
-    if query:
-        response = await get_response(query)
-        return jsonify({'response': response})
-    else:
-        return jsonify({'response': 'Invalid input'})
-
-# Chat Interface
-async def get_response(query: str) -> str:
-    return await cluster.route_request(query)
-
-def response_streaming(text: str):
-    try:
-        for char in text:
-            yield char
-    except Exception as e:
-        logging.error(f"Error in response streaming: {e}")
-        yield "Error occurred while streaming the response."
-
-class ChatApp:
-    def __init__(self, cluster: EliteDeveloperCluster):
-        self.cluster = cluster
-
-    async def start(self):
-        await self.cluster.start()
-
-    async def stop(self):
-        await self.cluster.stop()
-
-    async def handle_request(self, query: str) -> str:
-        response = await anext(self.cluster.process_tasks())
-        return response
-
-# Configuration
-config = {
-    "version_control_system": "Git",
-    "testing_framework": "PyTest",
-    "documentation_system": "Sphinx",
-    "build_automation_system": "Jenkins",
-    "redis_host": "localhost",
-    "redis_port": 6379,
-    "max_workers": 4,
-}
-
-async def main():
-    global cluster
-    # Initialize the cluster
-    cluster = EliteDeveloperCluster(config, model=None)
-
-    # Start the cluster
-    await cluster.start()
-
-    # Create a task for processing tasks
-    asyncio.create_task(anext(cluster.process_tasks()))
-
-    # Run Flask app
-    from hypercorn.asyncio import serve
-    from hypercorn.config import Config as HypercornConfig
-    hypercorn_config = HypercornConfig()
-    hypercorn_config.bind = ["localhost:5000"]
-    await serve(app, hypercorn_config)
-
-if __name__ == "__main__":
-    asyncio.run(main())
+    submit_button.click(process_input, inputs=[input_text, selected_cluster], outputs=output_text)
+
+demo.launch()