Create app.py

app.py

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+import os
+import datetime
+import pytz
+import math
+import requests
+import gradio as gr
+from deep_translator import GoogleTranslator
+
+# --- LLAMAINDEX IMPORTS ---
+from llama_index.core.agent import ReActAgent
+from llama_index.core.tools import FunctionTool
+from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
+
+# --- SMOLAGENTS IMPORTS ---
+from smolagents import CodeAgent, DuckDuckGoSearchTool, tool, InferenceClientModel
+
+# 1. SHARED SETUP & TOKENS
+# Ensure you set these in your Hugging Face Space Settings -> Secrets
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+# ==========================================
+# PART 1: LLAMAINDEX AGENT SETUP
+# ==========================================
+li_llm = HuggingFaceInferenceAPI(
+    model_name="Qwen/Qwen2.5-7B-Instruct", 
+    token=HF_TOKEN,
+    task="conversational", 
+    provider="together"
+)
+
+def get_tokyo_time_li() -> str:
+    """Returns the current time in Tokyo, Japan."""
+    tz = pytz.timezone('Asia/Tokyo')
+    return f"The current time in Tokyo is {datetime.datetime.now(tz).strftime('%H:%M:%S')}"
+
+def multiply_li(a: float, b: float) -> float:
+    """Multiplies two numbers (a and b) and returns the result."""
+    return a * b
+
+li_tools = [
+    FunctionTool.from_defaults(fn=get_tokyo_time_li),
+    FunctionTool.from_defaults(fn=multiply_li)
+]
+
+RE_ACT_PROMPT = """You are a helpful assistant.
+For every query, you MUST follow this sequence:
+Thought: <your reasoning>
+Action: <tool_name>
+Action Input: {"arg1": value}
+Observation: <result from tool>
+... (repeat if needed)
+Thought: I have the final answer.
+Answer: <your final response to the user>
+"""
+
+li_agent = ReActAgent.from_tools(
+    li_tools, 
+    llm=li_llm, 
+    verbose=True,
+    context=RE_ACT_PROMPT
+)
+
+def li_chat_fn(message, history):
+    try:
+        return str(li_agent.chat(message))
+    except Exception as e:
+        return f"LlamaIndex Error: {str(e)}"
+
+li_interface = gr.ChatInterface(li_chat_fn, title="LlamaIndex Agent")
+
+# ==========================================
+# PART 2: SMOLAGENTS SETUP
+# ==========================================
+smol_model = InferenceClientModel(
+    model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
+    token=HF_TOKEN 
+)
+
+@tool
+def weather_tool(location: str) -> str:
+    """Get current weather for any location.
+    Args:
+        location: The name of the city.
+    """
+    geo_url = f"https://geocoding-api.open-meteo.com/v1/search?name={location}&count=1"
+    geo_res = requests.get(geo_url).json()
+    if not geo_res.get('results'): return "Location not found."
+    data = geo_res['results'][0]
+    w_url = f"https://api.open-meteo.com/v1/forecast?latitude={data['latitude']}&longitude={data['longitude']}&current=temperature_2m"
+    w_res = requests.get(w_url).json()
+    return f"Temp in {location}: {w_res['current']['temperature_2m']}°C"
+
+smol_agent = CodeAgent(
+    model=smol_model,
+    tools=[weather_tool, DuckDuckGoSearchTool()],
+    additional_authorized_imports=['requests', 'math', 'pytz']
+)
+
+def smol_chat_fn(message, history):
+    try:
+        return str(smol_agent.run(message))
+    except Exception as e:
+        return f"Smolagents Error: {str(e)}"
+
+smol_interface = gr.ChatInterface(smol_chat_fn, title="Smolagents Assistant")
+
+# ==========================================
+# PART 3: COMBINE INTO TABS
+# ==========================================
+demo = gr.TabbedInterface(
+    [li_interface, smol_interface], 
+    ["LlamaIndex Agent", "Smolagents Agent"],
+    title="Multi-Framework Agent Comparison"
+)
+
+if __name__ == "__main__":
+    demo.launch()