Update app.py

app.py

@@ -1,10 +1,9 @@
+
 from smolagents import CodeAgent, HfApiModel, load_tool, tool
 import requests
 from tools.final_answer import FinalAnswerTool
 from Gradio_UI import GradioUI
-import numpy as np
 
-# Tool to fetch Bitcoin price from CoinGecko
 @tool
 def get_btc_price() -> str:
     """Fetches the current Bitcoin price in USD using the CoinGecko API."""
@@ -18,33 +17,10 @@ def get_btc_price() -> str:
         return '{"error": "Bitcoin price not found"}'
     return f"The current Bitcoin price is ${price:.2f} USD."
 
-# Fetch Bitcoin price data from CoinGecko for calculating fear and greed
-def fetch_btc_price_data():
-    url = 'https://api.coingecko.com/api/v3/coins/bitcoin/market_chart?vs_currency=usd&days=30'
-    response = requests.get(url)
-    data = response.json()
-    prices = [item[1] for item in data['prices']]
-    volumes = [item[1] for item in data['total_volumes']]
-    return np.array(prices), np.array(volumes)
-
-# Function to calculate price volatility
-def calculate_volatility(prices):
-    returns = np.diff(prices) / prices[:-1]
-    volatility = np.std(returns) * np.sqrt(len(returns))
-    return volatility
-
-# Function to calculate trading volume average
-def calculate_volume_average(volumes):
-    return np.mean(volumes)
-
-# Function to normalize values to 0-100 range
-def normalize(value, min_value, max_value):
-    return (value - min_value) / (max_value - min_value) * 100
-
 # Initialize the FinalAnswerTool
 final_answer = FinalAnswerTool()
 
-# Initialize the model for the agent
+# Initialize the model
 model = HfApiModel(
     max_tokens=2096,
     temperature=0.5,
@@ -52,51 +28,22 @@ model = HfApiModel(
     custom_role_conversions=None,
 )
 
-# Creating the agent to run the calculations
-@tool
-def calculate_fear_and_greed() -> str:
-    # Fetch BTC price and volume data
-    prices, volumes = fetch_btc_price_data()
-
-    # Calculate metrics
-    volatility = calculate_volatility(prices)
-    volume_avg = calculate_volume_average(volumes)
-
-    # Normalize the values
-    volatility_normalized = normalize(volatility, 0, 0.1)
-    volume_normalized = normalize(volume_avg, 0, 1000000000)
-
-    # Calculate sub-indices
-    volatility_index = 100 - volatility_normalized
-    volume_index = volume_normalized
-
-    # Aggregate Fear and Greed Index
-    fear_greed_index = np.mean([volatility_index, volume_index])
-
-    # Interpretation of the Fear and Greed Index
-    if fear_greed_index <= 24:
-        sentiment = "Extreme Fear"
-    elif 25 <= fear_greed_index <= 49:
-        sentiment = "Fear"
-    elif 50 <= fear_greed_index <= 74:
-        sentiment = "Neutral"
-    elif 75 <= fear_greed_index <= 100:
-        sentiment = "Greed"
-
-    return f"Fear and Greed Index: {fear_greed_index:.2f} ({sentiment})"
+# Load additional tools if needed
+# For example, to load an image generation tool:
+# image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
 
 # Create the agent
 agent = CodeAgent(
     model=model,
-    tools=[get_btc_price, final_answer, calculate_fear_and_greed],
+    tools=[get_btc_price, final_answer],  # Add your tools here
     max_steps=6,
     verbosity_level=1,
     grammar=None,
     planning_interval=None,
-    name="Crypto Sentiment Agent",
-    description="An agent that fetches BTC data and calculates the Fear and Greed index.",
-    prompt_templates=None
+    name=None,
+    description=None,
+    prompt_templates=None  # Set to None if not using prompt templates
 )
 
-# Launch the Gradio interface for interaction
+# Launch the Gradio interface
 GradioUI(agent).launch()