Update app.py

app.py

@@ -1,34 +1,42 @@
 
-# Import necessary parts of our AI framework.
+# 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.
 from smolagents import CodeAgent, HfApiModel, tool
 
 # ------------------------------------------------------------------------------
-# Import YAML to load additional instructions from a file.
+# Import YAML so we can load additional instructions from a YAML file.
 import yaml
 
 # ------------------------------------------------------------------------------
-# Import our helper tool for producing the final answer and the user interface.
+# Import our helper tool for preparing the final output and the user interface.
+# FinalAnswerTool packages the final answer.
+# GradioUI creates a simple webpage for user interaction.
 from tools.final_answer import FinalAnswerTool
 from Gradio_UI import GradioUI
 
 # ------------------------------------------------------------------------------
-# Set up the AI model (the "brain" of our agent).
+# 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.
 model = HfApiModel(
-    max_tokens=2096,  # Maximum length of the answer.
-    temperature=0.5,  # Controls creativity.
+    max_tokens=2096,  # This sets the maximum length of the answer.
+    temperature=0.5,  # This controls how creative the answer is.
     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')
     custom_role_conversions=None
 )
 
 # ------------------------------------------------------------------------------
 # Define the simplify_text tool.
-# 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.
+# 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.
 @tool
 def simplify_text(text: str) -> str:
     """
     Converts technical text into plain, everyday language.
-    Explains technical terms in simple language as if explaining to someone with little tech knowledge.
+    It explains any technical terms in simple language for someone with little tech knowledge.
 
     Args:
         text: A technical sentence or paragraph.
@@ -36,37 +44,45 @@ def simplify_text(text: str) -> str:
     Returns:
         A simplified version 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.
     prompt = (
-        "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"
+        "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"
         f"Technical text:\n{text}\n\n"
         "Simplified explanation:"
     )
+    # Call the model with our prompt. The model's response should be the simplified text.
     response = model(prompt)
     return response
 
 # ------------------------------------------------------------------------------
-# Load prompt templates from a YAML file.
+# Load extra instructions from the 'prompts.yaml' file.
+# These instructions help guide the model's behavior.
 with open("prompts.yaml", 'r') as stream:
     prompt_templates = yaml.safe_load(stream)
 
 # ------------------------------------------------------------------------------
-# Create our AI agent using our model and our two tools:
-# - FinalAnswerTool: Packages the final output.
-# - simplify_text: Converts technical text into plain language.
+# 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.
 agent = CodeAgent(
     model=model,
     tools=[
         FinalAnswerTool(),
         simplify_text
     ],
-    max_steps=6,
-    verbosity_level=1,
+    max_steps=6,             # Limit the number of reasoning steps.
+    verbosity_level=1,       # Lower verbosity for cleaner output.
     prompt_templates=prompt_templates
 )
 
 # ------------------------------------------------------------------------------
-# Launch the interactive UI so users can interact with the agent.
+# 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)