Delete deepseek_example.py

deepseek_example.py

@@ -1,70 +0,0 @@
-import os
-
-from dotenv import load_dotenv
-from openai import OpenAI
-
-from swarms import Agent
-from swarms.prompts.finance_agent_sys_prompt import (
-    FINANCIAL_AGENT_SYS_PROMPT,
-)
-
-load_dotenv()
-
-
-class DeepSeekChat:
-    def __init__(
-        self,
-        api_key: str = os.getenv("DEEPSEEK_API_KEY"),
-        system_prompt: str = None,
-    ):
-        self.api_key = api_key
-
-        self.client = OpenAI(
-            api_key=api_key, base_url="https://api.deepseek.com"
-        )
-
-    def run(self, task: str):
-        response = self.client.chat.completions.create(
-            model="deepseek-chat",
-            messages=[
-                {
-                    "role": "system",
-                    "content": "You are a helpful assistant",
-                },
-                {"role": "user", "content": task},
-            ],
-            stream=False,
-        )
-
-        print(response)
-
-        out = response.choices[0].message.content
-        print(out)
-
-        return out
-
-
-model = DeepSeekChat()
-
-# Initialize the agent
-agent = Agent(
-    agent_name="Financial-Analysis-Agent",
-    agent_description="Personal finance advisor agent",
-    system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
-    max_loops=1,
-    llm=model,
-    dynamic_temperature_enabled=True,
-    user_name="swarms_corp",
-    retry_attempts=3,
-    context_length=8192,
-    return_step_meta=False,
-    output_type="str",  # "json", "dict", "csv" OR "string" "yaml" and
-    auto_generate_prompt=False,  # Auto generate prompt for the agent based on name, description, and system prompt, task
-    max_tokens=4000,  # max output tokens
-)
-
-print(
-    agent.run(
-        "Create a table of super high growth opportunities for AI. I have $40k to invest in ETFs, index funds, and more. Please create a table in markdown.",
-    )
-)