Update app.py

app.py

@@ -1,5 +1,5 @@
 
-# Import necessary parts of our AI framework to create our agent and define our tools.
+# Import necessary parts of our AI framework.
 from smolagents import CodeAgent, HfApiModel, tool
 
 # ------------------------------------------------------------------------------
@@ -13,69 +13,60 @@ from Gradio_UI import GradioUI
 
 # ------------------------------------------------------------------------------
 # Set up the AI model (the "brain" of our agent).
-# The model processes text based on prompts and produces answers.
-# We use an alternative endpoint if needed.
 model = HfApiModel(
     max_tokens=2096,  # Maximum length of the answer.
-    temperature=0.5,  # Controls how creative the answer is.
+    temperature=0.5,  # Controls creativity.
     model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud',
-    # (You can switch to another model by changing the model_id.)
     custom_role_conversions=None
 )
 
 # ------------------------------------------------------------------------------
 # Define the simplify_text tool.
-# This tool takes technical text and asks the LLM to convert it into plain, simple language.
-# It also instructs the model to define any technical jargon in easy-to-understand terms.
+# This tool now asks the model to convert technical text into plain language,
+# and to explain any technical terms in a way that a non-expert would understand.
 @tool
 def simplify_text(text: str) -> str:
     """
-    Converts technical text into plain language, avoiding jargon and explaining any technical terms.
+    Converts technical text into plain, everyday language.
+    Explains technical terms in simple language as if explaining to someone with little tech knowledge.
 
     Args:
         text: A technical sentence or paragraph.
 
     Returns:
-        A simplified version of the text in common language.
+        A simplified version of the text.
     """
-    # Create a prompt that instructs the model to rephrase the text.
-    # The prompt tells the model:
-    #   - "Convert this technical text into simple, common language."
-    #   - "Avoid using technical jargon, and if you use any, explain it in plain language."
     prompt = (
-        "Convert the following technical text into plain, common language. Avoid technical jargon. "
-        "If you must use technical terms, please define them in simple language:\n\n"
-        f"{text}\n\nSimplified version:"
+        "Please read the following technical text and convert it into plain, everyday language. "
+        "Break down any complex ideas and explain any technical terms as if you were talking to someone "
+        "who has little knowledge about technology. Use simple words and short sentences.\n\n"
+        f"Technical text:\n{text}\n\n"
+        "Simplified explanation:"
     )
-    # Call the model with our prompt and store its response.
     response = model(prompt)
-    # Return the response, which should be a simplified version of the input text.
     return response
 
 # ------------------------------------------------------------------------------
-# Load prompt templates from the 'prompts.yaml' file.
-# These templates provide extra instructions to help guide the model.
+# Load prompt templates from a YAML file.
 with open("prompts.yaml", 'r') as stream:
     prompt_templates = yaml.safe_load(stream)
 
 # ------------------------------------------------------------------------------
-# Create our AI agent by combining the model with our tools.
-# Our agent uses the FinalAnswerTool (to package the final answer)
-# and the simplify_text tool (to rephrase technical text into simple language).
+# Create our AI agent using our model and our two tools:
+# - FinalAnswerTool: Packages the final output.
+# - simplify_text: Converts technical text into plain language.
 agent = CodeAgent(
     model=model,
     tools=[
         FinalAnswerTool(),
         simplify_text
     ],
-    max_steps=6,             # Limit the number of steps the agent can use to produce an answer.
-    verbosity_level=1,       # Lower verbosity for a cleaner output.
+    max_steps=6,
+    verbosity_level=1,
     prompt_templates=prompt_templates
 )
 
 # ------------------------------------------------------------------------------
-# Launch the interactive user interface.
-# Gradio creates a simple webpage for users to type in their text and see the agent's answer.
-# The interface listens on all network addresses (0.0.0.0) on port 7860.
+# Launch the interactive UI so users can interact with the agent.
 if __name__ == "__main__":
     GradioUI(agent).launch(server_name="0.0.0.0", server_port=7860)