Create app.py

app.py

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+
+import os
+import groq
+import phi
+import phi.api
+
+from dotenv import load_dotenv
+from phi.agent import Agent
+from phi.model.groq import Groq
+from phi.tools.yfinance import YFinanceTools
+from phi.tools.youtube_tools import YouTubeTools
+from phi.tools.googlesearch import GoogleSearch
+from phi.playground import Playground
+
+# 1. Load environment variables
+load_dotenv()
+
+phi.api = os.getenv("PHI_API_KEY")
+groq_api_key = os.getenv("GROQ_API_KEY")
+
+# 2. Create a groq client
+groq_client = groq.Client(api_key=groq_api_key)
+
+# 3. Define Agents
+
+# Websearch Agent
+websearch_agent = Agent(
+    name='websearch_agent',
+    role="Search the web for financial information including all the latest news too.",
+    model=Groq(id="llama-3.3-70b-versatile"),
+    tools=[GoogleSearch()],
+    instructions=["Always include the sources for the information in APA format."],
+    markdown=True,
+    # debug_mode=True,
+)
+
+# Youtube Agent
+youtube_agent = Agent(
+    name="YouTube Agent", 
+    role="You are a YouTube agent that has the special skill of understanding YouTube videos and answering questions about them. You can only answer if the duration is less than 22 minutes.",
+    model=Groq(id="llama-3.3-70b-versatile"),
+    tools=[YouTubeTools()],
+    instructions=[
+        "1. When the user asks about a video, confirm that they have provided a valid YouTube URL. If not, ask them for it.",
+        "2. Using a video URL, get the video data using the get_youtube_video_data tool. Using the video data, get the video captions using the get_youtube_video_captions tool.",
+        "3. Using the data and captions, answer the user questions in an engaging and thoughtful manner and only focus on the important information.",
+        "4. If you cannot find the information, let the user know by asking for more details, and don't hallucinate.",
+        "5. Keep your answers concise and engaging."
+    ],
+    markdown=True,
+    read_chat_history=True,
+    # debug_mode=True,
+)
+
+# Financial Agent
+finance_agent = Agent(
+    name="Finance AI Agent",
+    model=Groq(id="llama-3.3-70b-versatile"),
+    tools=[
+        YFinanceTools(
+            stock_price=True,
+            analyst_recommendations=True,
+            stock_fundamentals=True,
+            company_news=True
+        ),
+    ],
+    description="You are an investment analyst that researches stock prices, analyst recommendations, and stock fundamentals.",
+    instructions=["Format your response using markdown and use tables to display data where possible."],
+    markdown=True,
+    # debug_mode=True,
+)
+
+# Multi-Model Agent
+MultiModelAgent = Agent(
+    team=[websearch_agent, finance_agent, youtube_agent],
+    model=Groq(id="llama-3.3-70b-versatile"),
+    name="Multi-Model Agent",
+    instructions=["Always include sources and use tables to display data where possible."],
+    show_tool_calls=True,
+    markdown=True,
+    # debug_mode=True,
+)
+
+# 4. Create the Playground app with all agents
+playground = Playground(agents=[websearch_agent, finance_agent, MultiModelAgent, youtube_agent])
+
+# 5. The Hugging Face Space will look for 'app' to serve
+app = playground.get_app()