Update app.py

app.py

@@ -1,184 +1,54 @@
 import streamlit as st
-from langchain.agents import create_react_agent, AgentExecutor
-from langchain.prompts import PromptTemplate
-from langchain_community.llms import HuggingFaceHub
-from langchain_huggingface import HuggingFaceEndpoint
-from langchain.tools import Tool
-from langchain.chains import LLMChain
-from typing import List, Dict, Any, Optional
-
-# Base Agent class
-class Agent:
-    def __init__(self, name, role, tools, knowledge_base=None):
+from huggingface_hub import HfApi
+import os
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from typing import List, Dict
+from supplemental import Agent, Tool, CodeGenerationTool, DataRetrievalTool, TextGenerationTool, CodeExecutionTool, CodeDebuggingTool, CodeSummarizationTool, CodeTranslationTool, CodeOptimizationTool, CodeDocumentationTool, ImageGenerationTool, ImageEditingTool, ImageAnalysisTool, Workflow, EnhancedAIAgent
+
+st.title("CODEFUSSION ☄")
+
+# Access Hugging Face API key from secrets
+hf_token = st.secrets["hf_token"]
+if not hf_token:
+    st.error("Hugging Face API key not found. Please make sure it is set in the secrets.")
+
+# --- Agent Pool ---
+agent_pool = {
+    "IdeaIntake": EnhancedAIAgent("IdeaIntake", "Idea Intake", ["Data Retrieval", "Code Generation", "Text Generation"], "bigcode/starcoder"),
+    "CodeBuilder": EnhancedAIAgent("CodeBuilder", "Code Builder", ["Code Generation", "Code Debugging", "Code Optimization"], "bigcode/starcoder"),
+    "ImageCreator": EnhancedAIAgent("ImageCreator", "Image Creator", ["Image Generation", "Image Editing"], "bigcode/starcoder"),
+}
+
+# --- Workflow Definitions ---
+class Workflow:
+    def __init__(self, name, agents, task, description):
         self.name = name
-        self.role = role
-        self.tools = tools
-        self.knowledge_base = knowledge_base
-        self.llm = HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature": 0.5})
-
-    def act(self, task, context):
-        # This method should be implemented in subclasses
-        raise NotImplementedError
-
-# Base Tool class
-class BaseTool(Tool):
-    def __init__(self, name, description):
-        super().__init__(name=name, description=description, func=self.run)
-        self.llm = HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature": 0.5})
-
-    def run(self, arguments: str) -> str:
-        raise NotImplementedError
-        
-# Specific tool implementations
-class CodeGenerationTool(BaseTool):
-    def __init__(self):
-        super().__init__("Code Generation", "Generates code snippets in various languages.")
-        self.prompt_template = PromptTemplate(
-            input_variables=["language", "task"],
-            template="Generate {language} code for: {task}"
-        )
-        self.chain = LLMChain(llm=self.llm, prompt=self.prompt_template)
-
-    def run(self, arguments: str) -> str:
-        language, task = arguments.split(", ", 1)
-        return self.chain.run(language=language, task=task)
-
-class DataRetrievalTool(BaseTool):
-    def __init__(self):
-        super().__init__("Data Retrieval", "Accesses data from APIs, databases, or files.")
-        self.prompt_template = PromptTemplate(
-            input_variables=["source", "query"],
-            template="Retrieve data from {source} based on: {query}"
-        )
-        self.chain = LLMChain(llm=self.llm, prompt=self.prompt_template)
-
-    def run(self, arguments: str) -> str:
-        source, query = arguments.split(", ", 1)
-        return self.chain.run(source=source, query=query)
-
-class TextGenerationTool(BaseTool):
-    def __init__(self):
-        super().__init__("Text Generation", "Generates human-like text based on a given prompt.")
-        self.prompt_template = PromptTemplate(
-            input_variables=["prompt"],
-            template="Generate text based on: {prompt}"
-        )
-        self.chain = LLMChain(llm=self.llm, prompt=self.prompt_template)
-
-    def run(self, arguments: str) -> str:
-        return self.chain.run(prompt=arguments)
-        
-# Specialized Agent Definitions
-class SpecializedAgent(Agent):
-    def __init__(self, name, role, tools, knowledge_base=None):
-        super().__init__(name, role, tools, knowledge_base)
-        self.prompt = PromptTemplate.from_template(
-            "You are a specialized AI assistant named {name} with the role of {role}. "
-            "Use the following tools to complete your task: {tools}\n\n"
-            "Task: {input}\n"
-            "Thought: Let's approach this step-by-step:\n"
-            "{agent_scratchpad}"
-        )
-        self.react_agent = create_react_agent(self.llm, self.tools, self.prompt)
-        self.agent_executor = AgentExecutor(
-            agent=self.react_agent, 
-            tools=self.tools, 
-            verbose=True,
-            max_iterations=5
-        )
-
-    def act(self, task, context):
-        return self.agent_executor.run(input=task)
-
-class RequirementsAgent(SpecializedAgent):
-    def __init__(self):
-        super().__init__("RequirementsAnalyst", "Analyzing and refining project requirements", [TextGenerationTool()])
-
-class ArchitectureAgent(SpecializedAgent):
-    def __init__(self):
-        super().__init__("SystemArchitect", "Designing system architecture", [TextGenerationTool()])
+        self.agents = agents
+        self.task = task
+        self.description = description
 
-class FrontendAgent(SpecializedAgent):
-    def __init__(self):
-        super().__init__("FrontendDeveloper", "Developing the frontend", [CodeGenerationTool()])
-
-class BackendAgent(SpecializedAgent):
-    def __init__(self):
-        super().__init__("BackendDeveloper", "Developing the backend", [CodeGenerationTool(), DataRetrievalTool()])
-
-class DatabaseAgent(SpecializedAgent):
-    def __init__(self):
-        super().__init__("DatabaseEngineer", "Designing and implementing the database", [CodeGenerationTool(), DataRetrievalTool()])
-
-class TestingAgent(SpecializedAgent):
-    def __init__(self):
-        super().__init__("QAEngineer", "Creating and executing test plans", [CodeGenerationTool(), TextGenerationTool()])
-
-class DeploymentAgent(SpecializedAgent):
-    def __init__(self):
-        super().__init__("DevOpsEngineer", "Handling deployment and infrastructure", [CodeGenerationTool(), TextGenerationTool()])
-
-# --- Application Building Sequence ---
-class ApplicationBuilder:
-    def __init__(self):
-        self.agents = [
-            RequirementsAgent(),
-            ArchitectureAgent(),
-            FrontendAgent(),
-            BackendAgent(),
-            DatabaseAgent(),
-            TestingAgent(),
-            DeploymentAgent()
-        ]
-        self.project_state = {}
-
-    def build_application(self, project_description):
-        st.write("Starting application building process...")
+    def run(self, prompt, context):
+        # Workflow execution logic
         for agent in self.agents:
-            st.write(f"\n--- {agent.name}'s Turn ---")
-            task = self.get_task_for_agent(agent, project_description)
-            response = agent.act(task, self.project_state)
-            self.project_state[agent.role] = response
-            st.write(f"{agent.name}'s output:")
-            st.write(response)
-        st.write("\nApplication building process completed!")
-
-    def get_task_for_agent(self, agent, project_description):
-        tasks = (
-            (RequirementsAgent, f"Analyze and refine the requirements for this project: {project_description}"),
-            (ArchitectureAgent, f"Design the system architecture based on these requirements: {self.project_state.get('Analyzing and refining project requirements', '')}"),
-            (FrontendAgent, f"Develop the frontend based on this architecture: {self.project_state.get('Designing system architecture', '')}"),
-            (BackendAgent, f"Develop the backend based on this architecture: {self.project_state.get('Designing system architecture', '')}"),
-            (DatabaseAgent, f"Design and implement the database based on this architecture: {self.project_state.get('Designing system architecture', '')}"),
-            (TestingAgent, f"Create a test plan for this application: {project_description}"),
-            (DeploymentAgent, f"Create a deployment plan for this application: {project_description}")
-        )
-        
-        for agent_class, task in tasks:
-            if isinstance(agent, agent_class):
-                return task
-        
-        return f"Contribute to the project: {project_description}"
-
-# --- Streamlit App ---
-st.title("CODEFUSSION ☄ - Full-Stack Application Builder")
-
-project_description = st.text_area("Enter your project description:")
-
-if st.button("Build Application"):
-    if project_description:
-        app_builder = ApplicationBuilder()
-        app_builder.build_application(project_description)
-    else:
-        st.write("Please enter a project description.")
-
-# Display information about the agents
-st.sidebar.title("Agent Information")
-app_builder = ApplicationBuilder()
-for agent in app_builder.agents:
-    st.sidebar.write(f"--- {agent.name} ---")
-    st.sidebar.write(f"Role: {agent.role}")
-    st.sidebar.write("Tools:")
-    for tool in agent.tools:
-        st.sidebar.write(f"- {tool.name}")
-    st.sidebar.write("")
+            action = agent.act(prompt, context)
+            # Execute the tool
+            if action.get("tool"):
+                tool = next((t for t in agent.tools if t.name == action["tool"]), None)
+                if tool:
+                    output = tool.run(action["arguments"])
+                    # Update context
+                    context.update(output)
+
+# Example usage
+workflow = Workflow(
+    name="Example Workflow",
+    agents=[agent_pool["IdeaIntake"], agent_pool["CodeBuilder"]],
+    task="Generate and debug code",
+    description="A workflow to generate and debug code using AI agents."
+)
+
+context = {}
+prompt = "Create a Python function to add two numbers."
+workflow.run(prompt, context)
+st.write(context)