Update app.py

app.py

@@ -1,146 +1,52 @@
-from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel, load_tool, tool
+from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
 import datetime
 import requests
 import pytz
 import yaml
-import pandas as pd
-import os
-import json
-from typing import Dict, Union, List
 from tools.final_answer import FinalAnswerTool
+
 from Gradio_UI import GradioUI
 
-api_key = os.getenv("ALPHAVANTAGE")
-if not api_key:
-    raise ValueError("\u26a0\ufe0f Key is missing! Add it as a Secret in Hugging Face Spaces.")
+# Below is an example of a tool that does nothing. Amaze us with your creativity !
+# @tool
+# def my_custom_tool(arg1:str, arg2:int)-> str: #it's import to specify the return type
+#     #Keep this format for the description / args / args description but feel free to modify the tool
+#     """A tool that does nothing yet 
+#     Args:
+#         arg1: the first argument
+#         arg2: the second argument
+#     """
+#     return "What magic will you build ?"
 
 @tool
-def calculate_dcf_valuation(
-    symbol: str,
-    api_key: str,
-    growth_rate: float = 0.15,
-    terminal_growth: float = 0.03,
-    discount_rate: float = 0.10,
-    years_to_project: int = 5
-) -> Dict[str, Union[float, str, List[float]]]:
-    """
-    Calculates the DCF valuation for a given stock symbol using Alpha Vantage data.
-    
+def get_current_time_in_timezone(timezone: str) -> str:
+    """A tool that fetches the current local time in a specified timezone.
     Args:
-        symbol: Stock symbol (e.g., 'AAPL')
-        api_key: Alpha Vantage API key
-        growth_rate: Expected annual growth rate (default: 15%)
-        terminal_growth: Terminal growth rate (default: 3%)
-        discount_rate: Discount rate (default: 10%)
-        years_to_project: Number of years to project (default: 5)
-    
-    Returns:
-        Dictionary containing:
-        - intrinsic_value: Calculated intrinsic value per share
-        - current_price: Current market price
-        - recommendation: Buy/Sell/Hold recommendation
-        - projected_cash_flows: List of projected cash flows
-        - error: Error message if any
+        timezone: A string representing a valid timezone (e.g., 'America/New_York').
     """
     try:
-        # Get cash flow data
-        cash_flow_url = f"https://www.alphavantage.co/query?function=CASH_FLOW&symbol={symbol}&apikey=NR9AFISYRYH2B5U3"
-        response = requests.get(cash_flow_url)
-        cash_flow_data = response.json()
-        
-        if "annualReports" not in cash_flow_data:
-            return {"error": "Unable to fetch cash flow data"}
-        
-        # Get operating cash flow from most recent year
-        latest_cash_flow = float(cash_flow_data["annualReports"][0]["operatingCashflow"])
-        
-        # Get shares outstanding
-        overview_url = f"https://www.alphavantage.co/query?function=OVERVIEW&symbol={symbol}&apikey=NR9AFISYRYH2B5U3"
-        response = requests.get(overview_url)
-        overview_data = response.json()
-        shares_outstanding = float(overview_data.get("SharesOutstanding", 0))
-        
-        # Project future cash flows
-        projected_cash_flows = []
-        current_cash_flow = latest_cash_flow
-        
-        for year in range(years_to_project):
-            current_cash_flow *= (1 + growth_rate)
-            projected_cash_flows.append(current_cash_flow)
-        
-        # Calculate terminal value
-        terminal_value = (current_cash_flow * (1 + terminal_growth)) / (discount_rate - terminal_growth)
-        projected_cash_flows.append(terminal_value)
-        
-        # Calculate present value of all cash flows
-        present_value = 0
-        for i, cash_flow in enumerate(projected_cash_flows):
-            present_value += cash_flow / ((1 + discount_rate) ** (i + 1))
-        
-        # Calculate per share value
-        intrinsic_value = present_value / shares_outstanding
-        
-        # Get current price
-        price_url = f"https://www.alphavantage.co/query?function=GLOBAL_QUOTE&symbol={symbol}&apikey=NR9AFISYRYH2B5U3"
-        response = requests.get(price_url)
-        price_data = response.json()
-        current_price = float(price_data["Global Quote"]["05. price"])
-        
-        # Generate recommendation
-        margin_of_safety = 0.2  # 20% margin of safety
-        if intrinsic_value * (1 - margin_of_safety) > current_price:
-            recommendation = "Buy"
-        elif intrinsic_value * (1 + margin_of_safety) < current_price:
-            recommendation = "Sell"
-        else:
-            recommendation = "Hold"
-        
-        return {
-            "intrinsic_value": round(intrinsic_value, 2),
-            "current_price": round(current_price, 2),
-            "recommendation": recommendation,
-            "projected_cash_flows": [round(cf, 2) for cf in projected_cash_flows],
-            "error": None
-        }
-    
+        # Create timezone object
+        tz = pytz.timezone(timezone)
+        # Get current time in that timezone
+        local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S")
+        return f"The current local time in {timezone} is: {local_time}"
     except Exception as e:
-        return {"error": f"Error calculating DCF: {str(e)}"}
+        return f"Error fetching time for timezone '{timezone}': {str(e)}"
 
-# Example of how to format it as an AI agent tool
-@tool
-def get_stock_dcf_valuation(symbol: str, api_key: str) -> str:
-    """
-    A tool that calculates the DCF valuation for a given stock symbol.
-    
-    Args:
-        symbol: Stock symbol (e.g., 'AAPL')
-        api_key: Alpha Vantage API key
-    
-    Returns:
-        A string containing the DCF analysis results or error message
-    """
-    result = calculate_dcf_valuation(symbol, api_key)
-    
-    if result.get("error"):
-        return result["error"]
-    
-    return (
-        f"DCF Analysis for {symbol}:\n"
-        f"Intrinsic Value: ${result['intrinsic_value']}\n"
-        f"Current Price: ${result['current_price']}\n"
-        f"Recommendation: {result['recommendation']}\n"
-        f"Projected Cash Flows: {result['projected_cash_flows']}"
-    )
 
 final_answer = FinalAnswerTool()
 
+# If the agent does not answer, the model is overloaded, please use another model or the following Hugging Face Endpoint that also contains qwen2.5 coder:
+# model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud' 
+
 model = HfApiModel(
-    max_tokens=2096,
-    temperature=0.5,
-    model_id='Qwen/Qwen2.5-Coder-32B-Instruct',
-    custom_role_conversions=None,
+max_tokens=2096,
+temperature=0.5,
+model_id='Qwen/Qwen2.5-Coder-32B-Instruct',# it is possible that this model may be overloaded
+custom_role_conversions=None,
 )
 
+
 # Import tool from Hub
 image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
 
@@ -149,11 +55,8 @@ with open("prompts.yaml", 'r') as stream:
     
 agent = CodeAgent(
     model=model,
-    tools=[
-        final_answer,
-        calculate_dcf_valuation,
-        get_stock_dcf_valuation  # Added the DCF tool here
-    ],
+    tools=[final_answer,
+          get_current_time_in_timezone], ## add your tools here (don't remove final answer)
     max_steps=6,
     verbosity_level=1,
     grammar=None,
@@ -163,4 +66,5 @@ agent = CodeAgent(
     prompt_templates=prompt_templates
 )
 
+
 GradioUI(agent).launch()