Update app.py

app.py

@@ -1,88 +1,88 @@
 
-# Import the necessary parts of our AI framework.
-# CodeAgent helps us create our helper agent.
-# HfApiModel is used to set up the model (the brain) that processes prompts.
-# The @tool decorator marks functions as "tools" the agent can use.
+# Import necessary parts of our AI framework.
+# CodeAgent helps us build our helper agent.
+# HfApiModel sets up the model (the "brain") that processes text.
+# The @tool decorator marks functions as "tools" that our agent can use.
 from smolagents import CodeAgent, HfApiModel, tool
 
 # ------------------------------------------------------------------------------
-# Import YAML so we can load additional instructions from a YAML file.
+# Import YAML so we can load additional instructions from a file.
 import yaml
 
 # ------------------------------------------------------------------------------
-# Import our helper tool for preparing the final output and the user interface.
-# FinalAnswerTool packages the final answer.
+# Import our helper tool for preparing the final answer and the user interface.
+# FinalAnswerTool packages the final answer to show to the user.
 # GradioUI creates a simple webpage for user interaction.
 from tools.final_answer import FinalAnswerTool
 from Gradio_UI import GradioUI
 
 # ------------------------------------------------------------------------------
-# Set up our AI model (the "brain" of the agent).
-# The model processes text prompts and returns answers.
-# We use an alternative endpoint here to help when the primary model is overloaded.
+# Set up our AI model (the "brain" of our agent).
+# This model processes text prompts and generates responses.
 model = HfApiModel(
-    max_tokens=2096,  # This sets the maximum length of the answer.
-    temperature=0.5,  # This controls how creative the answer is.
+    max_tokens=2096,  # Maximum length of the generated answer.
+    temperature=0.5,  # Controls how creative or focused the response is.
+    # We use an alternative model endpoint to help if the primary model is busy.
     model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud',
-    # (To switch back to the original model, use: model_id='Qwen/Qwen2.5-Coder-32B-Instruct')
+    # (To revert to the original model, you could use: model_id='Qwen/Qwen2.5-Coder-32B-Instruct')
     custom_role_conversions=None
 )
 
 # ------------------------------------------------------------------------------
 # Define the simplify_text tool.
-# This tool takes technical text and asks the LLM to convert it into plain language.
-# It also instructs the model to explain any technical jargon.
+# This tool takes technical text and instructs the model to explain it in plain, everyday language.
+# It tells the model to break complex ideas into simple sentences, and if any technical terms are necessary,
+# to provide a brief definition in parentheses.
 @tool
 def simplify_text(text: str) -> str:
     """
     Converts technical text into plain, everyday language.
-    It explains any technical terms in simple language for someone with little tech knowledge.
+    It explains any technical terms (by providing a simple definition in parentheses)
+    and breaks down complex ideas into short, clear sentences.
 
     Args:
         text: A technical sentence or paragraph.
 
     Returns:
-        A simplified version of the text.
+        A simplified explanation of the text.
     """
-    # Build a clear prompt that instructs the model on what to do:
-    # 1. Convert the text into plain language.
-    # 2. Avoid technical jargon.
-    # 3. If technical terms are necessary, explain them in simple language.
+    # Build an improved prompt with detailed instructions:
     prompt = (
-        "Please convert the following technical text into plain, everyday language. "
-        "Avoid using technical jargon, and if you must use any, explain them in simple words. "
-        "Break down complex ideas into simple, short sentences:\n\n"
+        "Please read the following technical text and explain it in plain, everyday language as if you were talking to someone with little technical knowledge. "
+        "Break down complex ideas into simple, short sentences. If you need to use any technical terms, provide a brief definition in parentheses right after the term. "
+        "Do not use confusing jargon; instead, use simple words. \n\n"
         f"Technical text:\n{text}\n\n"
         "Simplified explanation:"
     )
-    # Call the model with our prompt. The model's response should be the simplified text.
+    # Call the model with the prompt. The model should generate a simplified explanation.
     response = model(prompt)
     return response
 
 # ------------------------------------------------------------------------------
-# Load extra instructions from the 'prompts.yaml' file.
-# These instructions help guide the model's behavior.
+# Load additional instructions from the 'prompts.yaml' file.
+# These prompt templates help guide the model's behavior.
 with open("prompts.yaml", 'r') as stream:
     prompt_templates = yaml.safe_load(stream)
 
 # ------------------------------------------------------------------------------
 # Create our AI agent.
-# The agent uses the model (its brain) and the tools we've defined to generate answers.
-# We include the FinalAnswerTool to package the answer and our simplify_text tool.
+# The agent is built by combining the model with our tools.
+# Here we include:
+#   - FinalAnswerTool: Packages the final answer.
+#   - simplify_text: Converts technical text into plain language.
 agent = CodeAgent(
     model=model,
     tools=[
         FinalAnswerTool(),
         simplify_text
     ],
-    max_steps=6,             # Limit the number of reasoning steps.
-    verbosity_level=1,       # Lower verbosity for cleaner output.
+    max_steps=6,             # Limit the number of reasoning steps the agent can use.
+    verbosity_level=1,       # A lower number keeps internal details minimal.
     prompt_templates=prompt_templates
 )
 
 # ------------------------------------------------------------------------------
 # Launch the interactive user interface.
-# GradioUI creates a webpage where users can enter text and see the agent's answer.
-# It listens on all network interfaces (0.0.0.0) on port 7860.
-if __name__ == "__main__":
-    GradioUI(agent).launch(server_name="0.0.0.0", server_port=7860)
+# GradioUI creates a webpage where users can type in technical text and see the simplified version.
+# The interface listens on all network addresses (0.0.0.0) on port 7860.
+if __n