Drop file

chat.py

@@ -1,91 +0,0 @@
-#!/usr/bin/env python
-import json
-from typing import Any, Optional
-
-from pydantic import BaseModel, Field
-
-from crewai.flow import Flow, start
-from crewai.flow.persistence.decorators import persist
-from crewai.flow.persistence.sqlite import SQLiteFlowPersistence
-from crewai.llm import LLM
-
-
-class Message(BaseModel):
-    role: str = Field(
-        description="The role of the message sender (e.g., 'user', 'assistant')"
-    )
-    content: str = Field(description="The actual content/text of the message")
-
-
-class ChatState(BaseModel):
-    message: Optional[Message] = None
-    history: list[Message] = Field(default_factory=list)
-
-
-@persist(SQLiteFlowPersistence(), verbose=True)
-class PersonalAssistantFlow(Flow[ChatState]):
-    @start()
-    def chat(self):
-        user_message_pydantic = self.state.message
-
-        # Safety check for None message
-        if not user_message_pydantic:
-            return "No message provided"
-
-        # Format history for prompt
-        history_formatted = "\n".join(
-            [f"{msg.role}: {msg.content}" for msg in self.state.history]
-        )
-
-        prompt = f"""
-        You are a helpful assistant.
-        Answer the user's question: {user_message_pydantic.content}
-
-        Just for the sake of being context-aware, this is the entire conversation history:
-        {history_formatted}
-
-        Be friendly and helpful, yet to the point.
-        """
-
-        response = LLM(model="gemini/gemini-2.0-flash", response_format=Message).call(
-            prompt
-        )
-
-        # Parse the response
-        if isinstance(response, str):
-            try:
-                llm_response_json = json.loads(response)
-                llm_response_pydantic = Message(**llm_response_json)
-            except json.JSONDecodeError:
-                # Fallback if response isn't valid JSON
-                llm_response_pydantic = Message(
-                    role="assistant",
-                    content="I'm sorry, I encountered an error processing your request.",
-                )
-        else:
-            # If response is already a Message object
-            llm_response_pydantic = response
-
-        # Update history - with type safety
-        if user_message_pydantic:  # Ensure message is not None before adding to history
-            self.state.history.append(user_message_pydantic)
-        self.state.history.append(llm_response_pydantic)
-
-        print("History", self.state.history)
-        return llm_response_pydantic.content
-
-
-if __name__ == "__main__":
-    # Example usage
-    import sys
-
-    if len(sys.argv) > 1:
-        user_input = " ".join(sys.argv[1:])
-    else:
-        user_input = input("> ")
-
-    flow = PersonalAssistantFlow()
-    flow.state.message = Message(role="user", content=user_input)
-
-    response = flow.kickoff()
-    print(response)