Update app.py

app.py

@@ -24,15 +24,14 @@ model = HfApiModel(
     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 revert to the original model, you could use: model_id='Qwen/Qwen2.5-Coder-32B-Instruct')
+    # (To revert to the original model, use: model_id='Qwen/Qwen2.5-Coder-32B-Instruct')
     custom_role_conversions=None
 )
 
 # ------------------------------------------------------------------------------
 # Define the simplify_text tool.
-# 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.
+# This tool takes technical text and instructs the LLM to explain it in plain language.
+# It tells the model to break down complex ideas into simple sentences and explain technical terms in simple words.
 @tool
 def simplify_text(text: str) -> str:
     """
@@ -50,39 +49,4 @@ def simplify_text(text: str) -> str:
     prompt = (
         "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 the prompt. The model should generate a simplified explanation.
-    response = model(prompt)
-    return response
-
-# ------------------------------------------------------------------------------
-# 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 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 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 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
+        "Do not use c