app.py

app.py

@@ -1,69 +1,115 @@
-from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
-import datetime
-import requests
-import pytz
-import yaml
-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 !
-@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 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:
-        # 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 f"Error fetching time for timezone '{timezone}': {str(e)}"
-
-
-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',# 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)
-
-with open("prompts.yaml", 'r') as stream:
-    prompt_templates = yaml.safe_load(stream)
-    
-agent = CodeAgent(
-    model=model,
-    tools=[final_answer], ## add your tools here (don't remove final answer)
-    max_steps=6,
-    verbosity_level=1,
-    grammar=None,
-    planning_interval=None,
-    name=None,
-    description=None,
-    prompt_templates=prompt_templates
-)
-
-
-GradioUI(agent).launch()
+# app.py
+
+# Import the necessary classes and functions from smolagents
+from smolagents import CodeAgent, HfApiModel, load_tool, tool
+
+# Standard library imports
+import datetime
+import pytz
+import yaml
+
+# External imports
+# TODO: uncomment the import statements
+#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")
+
+@tool
+def my_custom_tool(arg1: str, arg2: int) -> str:
+    """A tool that does nothing yet 
+    Args:
+        arg1: the first argument
+        arg2: the second argument
+    """
+    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.
+    """
+    
+    # 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"
+    
+    return f"Sentiment: {label} (confidence: {score:.4f})"
+
+@tool
+def simple_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.
+    """
+    text = text.lower()
+    
+    if "happy" in text:
+        return "Sentiment: Joyful (confidence: 1.00)"
+    elif "sad" in text:
+        return "Sentiment: Sorrowful (confidence: 1.00)"
+    
+    label = "positive"
+    score = "0.99"
+    
+    return f"Sentiment: {label} (confidence: {score:.4f})"
+    
+# Final answer tool
+final_answer = FinalAnswerTool()
+
+# Initialize the model
+model = HfApiModel(
+    max_tokens=2096,
+    temperature=0.5,
+    model_id='Qwen/Qwen2.5-Coder-32B-Instruct',
+    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)
+
+# 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],
+    max_steps=6,
+    verbosity_level=1,
+    grammar=None,
+    planning_interval=None,
+    name=None,
+    description=None,
+    prompt_templates=prompt_templates
+)
+
+# Launch the Gradio UI
+GradioUI(agent).launch()