Updated app.py

app.py

@@ -7,16 +7,81 @@ from tools.final_answer import FinalAnswerTool
 
 from Gradio_UI import GradioUI
 
-# Below is an example of a tool that does nothing. Amaze us with your creativity !
+# My first tool !
 @tool
-def my_custom_tool(arg1:str, arg2:int)-> str: #it's import to specify the return type
+def coin_predictor_tool()-> 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
+!pip install -q llama-index requests
+
+# Step 2: Define the Agent Tool
+from llama_index.core.tools import FunctionTool
+import requests
+from datetime import datetime
+
+def coin_predictor_tool() -> str:
     """
-    return "What magic will you build ?"
+    Predicts Bitcoin's price for today (March 10, 2025) based on available data or trends.
+    Returns a formatted string with the predicted price and reasoning.
+    """
+    # Simulate current date
+    today = datetime(2025, 3, 10)
+    current_date_str = today.strftime('%Y-%m-%d')
+
+    # Placeholder for real data (replace with API call in practice)
+    # Example: Fetch last 7 days of BTC prices from an API
+    try:
+        # Hypothetical API call (e.g., CoinGecko)
+        url = "https://api.coingecko.com/api/v3/coins/bitcoin/market_chart"
+        params = {
+            "vs_currency": "usd",
+            "days": "7",  # Last 7 days
+            "interval": "daily"
+        }
+        response = requests.get(url, params=params, timeout=10)
+        response.raise_for_status()
+        data = response.json()
+        
+        # Extract prices (simulated here; replace with real data)
+        prices = data.get("prices", [])  # [[timestamp, price], ...]
+        if not prices:
+            raise ValueError("No price data available")
+        
+        # Simple prediction: Average of last 7 days + trend adjustment
+        recent_prices = [price[1] for price in prices[-7:]]  # Last 7 days' closing prices
+        avg_price = sum(recent_prices) / len(recent_prices)
+        
+        # Assume a trend (e.g., based on last day's change)
+        last_change = recent_prices[-1] - recent_prices[-2]
+        trend_factor = 1 + (last_change / recent_prices[-2])  # % change applied
+        predicted_price = avg_price * trend_factor
+
+    except Exception as e:
+        # Fallback simulation if API fails or for demo purposes
+        print(f"API error: {e}. Using simulated data.")
+        # Simulated data based on recent trends (e.g., from your provided context)
+        avg_price = 91981  # From Web ID 5, March 10, 2025 price
+        trend_factor = 1.0418  # +4.18% from Web ID 7's 24h change
+        predicted_price = avg_price * trend_factor
+
+    # Format the output
+    output = (
+        f"Bitcoin Price Prediction for {current_date_str}:\n"
+        f"Predicted Price: ${predicted_price:,.2f} USD\n"
+        f"Reasoning: Based on a 7-day average of ${avg_price:,.2f} with a "
+        f"{(trend_factor-1)*100:.2f}% trend adjustment from recent data."
+    )
+    return output
+
+# Create the tool
+bitcoin_price_tool = FunctionTool.from_defaults(
+    fn=coin_predictor_tool,
+    name="bitcoin_price_predictor",
+    description="Predicts Bitcoin's price for today (March 10, 2025) using recent trends or API data."
+)
+
+# Step 3: Test the tool standalone
+print(bitcoin_price_tool())
+
 
 @tool
 def get_current_time_in_timezone(timezone: str) -> str: