Update app.py

app.py

@@ -6,9 +6,68 @@ from langchain.tools import Tool
 from langchain.chains import LLMChain
 from typing import List, Dict, Any, Optional
 
-# Base Tool and specific tools (CodeGenerationTool, DataRetrievalTool, TextGenerationTool) remain the same as in the previous version
+# Base Agent class
+class Agent:
+    def __init__(self, name, role, tools, knowledge_base=None):
+        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)
 
-# --- Specialized Agent Definitions ---
+    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)
@@ -27,6 +86,9 @@ class SpecializedAgent(Agent):
             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()])