remove unavailable libs

Adds a tool that uses available imports

app.py

@@ -3,10 +3,6 @@ import datetime
 import requests
 import pytz
 import yaml
-import yfinance as yf
-from ta.momentum import RSIIndicator, StochasticOscillator
-from ta.trend import MACD
-from ta.volume import volume_weighted_average_price
 from tools.final_answer import FinalAnswerTool
 
 
@@ -24,66 +20,54 @@ from Gradio_UI import GradioUI
 #     return "What magic will you build ?"
 
 @tool
-def get_stock_price(ticker: str) -> Union[Dict, str]:
-    """
-    A tool that fetches the historical stock price data and technical indicators for a given ticker.
+def trending_topic_analyzer(topic: str, hours_back: int) -> str:
+    """A tool that analyzes trending topics and provides timestamped insights with web search data
+    
     Args:
-        ticker: A string representing a stocke ticker name (e.g AAPL)
+        topic: The topic or keyword to analyze 
+        hours_back: How many hours back to consider for the analysis
     """
-
+    # Initialize search tool
+    search_tool = DuckDuckGoSearchTool()
+    
+    # Get current time in UTC
+    utc_now = datetime.datetime.now(pytz.UTC)
+    time_threshold = utc_now - datetime.timedelta(hours=hours_back)
+    
     try:
-        data = yf.download(
-            ticker,
-            start=dt.datetime.now() - dt.timedelta(weeks=24 * 3),
-            end=dt.datetime.now(),
-            interval="1wk",
-        )
-        df = data.copy()
-        data.reset_index(inplace=True)
-        data.Date = data.Date.astype(str)
-
-        indicators = {}
-
-        rsi_series = RSIIndicator(df["Close"], window=14).rsi().iloc[-12:]
-        indicators["RSI"] = {
-            date.strftime("%Y-%m-%d"): int(value)
-            for date, value in rsi_series.dropna().to_dict().items()
-        }
-
-        stochastic_series = (
-            StochasticOscillator(df["High"], df["Low"], df["Close"], window=14)
-            .stoch()
-            .iloc[-12:]
-        )
-        indicators["Stochastic Oscillator"] = {
-            date.strftime("%Y-%m-%d"): int(value)
-            for date, value in stochastic_series.dropna().to_dict().items()
-        }
-
-        macd = MACD(df["Close"])
-        macd_series = macd.macd().iloc[-12:]
-        indicators["MACD"] = {
-            date.strftime("%Y-%m-%d"): int(value)
-            for date, value in macd_series.to_dict().items()
-        }
-
-        macd_signal_series = macd.macd_signal().iloc[-12:]
-        indicators["MACD Signal"] = {
-            date.strftime("%Y-%m-%d"): int(value)
-            for date, value in macd_signal_series.to_dict().items()
-        }
-
-        vwap_series = volume_weighted_average_price(
-            df["High"], df["Low"], df["Close"], df["Volume"]
-        ).iloc[-12:]
-        indicators["vwap"] = {
-            date.strftime("%Y-%m-%d"): int(value)
-            for date, value in vwap_series.to_dict().items()
-        }
-
-        return {"stock_price": data.to_dict(orient="records"), "indicators": indicators}
+        # Perform search for recent content about the topic
+        search_query = f"{topic} after:{time_threshold.strftime('%Y-%m-%d')}"
+        search_results = search_tool.run(search_query)
+        
+        # Process search results
+        if not search_results:
+            return f"No recent data found for '{topic}' in the last {hours_back} hours"
+            
+        # Analyze basic statistics
+        result_count = len(search_results)
+        timestamp = utc_now.strftime("%Y-%m-%d %H:%M:%S UTC")
+        
+        # Extract key information from top results
+        summary = []
+        for i, result in enumerate(search_results[:3], 1):  # Top 3 results
+            title = result.get('title', 'No title')
+            snippet = result.get('snippet', 'No description')[:100] + "..."
+            summary.append(f"{i}. {title}\n   {snippet}")
+        
+        # Compile the analysis
+        analysis = f"Trending Topic Analysis for '{topic}'\n"
+        analysis += f"Generated at: {timestamp}\n"
+        analysis += f"Timeframe: Last {hours_back} hours\n"
+        analysis += f"Number of relevant results: {result_count}\n\n"
+        analysis += "Top Recent Mentions:\n"
+        analysis += "\n".join(summary)
+        analysis += "\n\nStatus: Analysis completed successfully"
+        
+        return analysis
+        
     except Exception as e:
-        return f"Error fetching price data: {str(e)}"
+        error_time = utc_now.strftime("%Y-%m-%d %H:%M:%S UTC")
+        return f"Error analyzing topic '{topic}' at {error_time}: {str(e)}"
 
 @tool
 def get_current_time_in_timezone(timezone: str) -> str: