added a tool to that uses a pre-trained transformer model to do sentiment analysis

.gitignore

@@ -0,0 +1,2 @@
+etc/
+myenv/

app.py

@@ -9,17 +9,15 @@ import pytz
 import yaml
 
 # External imports
-# TODO: uncomment the import statements
-#import torch
-#from transformers import pipeline
+import torch
+from transformers import pipeline
 
 # Import custom final answer tool and Gradio UI
 from tools.final_answer import FinalAnswerTool
 from Gradio_UI import GradioUI
 
 # Initialize the Transformer-based sentiment analysis pipeline
-#TODO: uncomment when testing using the real transformers
-#sentiment_pipeline = pipeline("sentiment-analysis")
+sentiment_pipeline = pipeline("sentiment-analysis")
 
 @tool
 def my_custom_tool(arg1: str, arg2: int) -> str:
@@ -30,34 +28,19 @@ def my_custom_tool(arg1: str, arg2: int) -> str:
     """
     return "What magic will you build?"
 
-@tool
-def get_current_time_in_timezone(timezone: str) -> str:
-    """A tool that fetches the current local time in a specified timezone.
-    Args:
-        timezone: A string representing a valid timezone (e.g., 'America/New_York').
-    """
-    try:
-        tz = pytz.timezone(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 f"Error fetching time for timezone '{timezone}': {str(e)}"
-
 @tool
 def advanced_sentiment_tool(text: str) -> str:
     """A tool that uses a pre-trained transformer model to do sentiment analysis.
     Args:
         text: The text to analyze for sentiment.
     """
+
+    analysis = sentiment_pipeline(text)
+    label = analysis[0]['label']
+    score = analysis[0]['score']
     
-    # TODO: uncomment later. for now test with hardcoded value first. later test using the real model
-    # TODO: also uncomment the import statements
-    #analysis = sentiment_pipeline(text)
-    #label = analysis[0]['label']
-    #score = analysis[0]['score']
-    
-    label = "positive"
-    score = "0.99"
+    #label = "positive"
+    #score = "0.99"
     
     return f"Sentiment: {label} (confidence: {score:.4f})"
 
@@ -79,6 +62,19 @@ def simple_sentiment_tool(text: str) -> str:
     
     return f"Sentiment: {label} (confidence: {score:.4f})"
     
+@tool
+def get_current_time_in_timezone(timezone: str) -> str:
+    """A tool that fetches the current local time in a specified timezone.
+    Args:
+        timezone: A string representing a valid timezone (e.g., 'America/New_York').
+    """
+    try:
+        tz = pytz.timezone(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 f"Error fetching time for timezone '{timezone}': {str(e)}"
+
 # Final answer tool
 final_answer = FinalAnswerTool()
 
@@ -90,9 +86,6 @@ model = HfApiModel(
     custom_role_conversions=None,
 )
 
-# Load an image generation tool (unrelated, just for demonstration)
-image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
-
 # Load prompt templates
 with open("prompts.yaml", 'r') as stream:
     prompt_templates = yaml.safe_load(stream)
@@ -100,8 +93,7 @@ with open("prompts.yaml", 'r') as stream:
 # Initialize the agent, including the sentiment analysis tool
 agent = CodeAgent(
     model=model,
-    # TODO: use advanced_sentiment_tool later after testing using the simpler tool is done
-    tools=[final_answer, simple_sentiment_tool],
+    tools=[final_answer, advanced_sentiment_tool],
     max_steps=6,
     verbosity_level=1,
     grammar=None,

requirements.txt

@@ -1,5 +1,6 @@
 markdownify
 smolagents
 requests
-duckduckgo_search
 pandas
+torch
+transformers