Update app.py

app.py

@@ -1,4 +1,4 @@
-""" Basic Agent Evaluation Runner"""
+""" Multi-LLM Agent Evaluation Runner"""
 import os
 import inspect
 import gradio as gr
@@ -10,14 +10,14 @@ from veryfinal import build_graph
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
-class BasicAgent:
-    """A langgraph agent."""
+# --- Enhanced Agent Definition ---
+class EnhancedMultiLLMAgent:
+    """A multi-provider LangGraph agent supporting Groq, DeepSeek, and Baidu."""
     def __init__(self):
-        print("BasicAgent initialized.")
+        print("Enhanced Multi-LLM Agent initialized.")
         try:
             self.graph = build_graph(provider="groq")  # Using Groq as default
-            print("Graph built successfully.")
+            print("Multi-LLM Graph built successfully.")
         except Exception as e:
             print(f"Error building graph: {e}")
             self.graph = None
@@ -66,8 +66,8 @@ class BasicAgent:
 
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
+    Fetches all questions, runs the Enhanced Multi-LLM Agent on them, 
+    submits all answers, and displays the results.
     """
     # --- Determine HF Space Runtime URL and Repo URL ---
     space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
@@ -85,7 +85,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
 
     # 1. Instantiate Agent
     try:
-        agent = BasicAgent()
+        agent = EnhancedMultiLLMAgent()
         if agent.graph is None:
             return "Error: Failed to initialize agent properly", None
     except Exception as e:
@@ -115,7 +115,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     # 3. Run your Agent
     results_log = []
     answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
+    print(f"Running Enhanced Multi-LLM agent on {len(questions_data)} questions...")
     
     for i, item in enumerate(questions_data):
         task_id = item.get("task_id")
@@ -151,7 +151,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
 
     # 4. Prepare Submission 
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
+    status_update = f"Enhanced Multi-LLM Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
     print(status_update)
 
     # 5. Submit
@@ -178,25 +178,31 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
 
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# LangGraph Agent Evaluation Runner")
+    gr.Markdown("# Enhanced Multi-LLM Agent Evaluation Runner")
     gr.Markdown(
         """
         **Instructions:**
         1. Log in to your Hugging Face account using the button below.
         2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
         
-        **Agent Features:**
-        - Uses FAISS vector database for similar question retrieval
-        - Includes mathematical calculation tools
-        - Web search capabilities (Tavily, Wikipedia, ArXiv)
-        - Rate limiting for free tier models
-        - Best free models: Groq Llama 3.3 70B, Gemini 2.0 Flash, NVIDIA Llama 3.1 70B
+        **Enhanced Agent Features:**
+        - **Multi-LLM Support**: Groq, DeepSeek, and Baidu ERNIE
+        - **Intelligent Routing**: Automatically selects best provider based on query
+        - **Mathematical Tools**: Add, subtract, multiply, divide, modulus operations
+        - **Web Search**: Tavily and Wikipedia integration
+        - **Error Handling**: Robust fallback mechanisms
+        - **Rate Limiting**: Optimized for free tier usage
+        
+        **Supported Models:**
+        - **Groq**: Llama 3.1 70B Versatile (fast inference)
+        - **DeepSeek**: DeepSeek Chat (reasoning-focused)
+        - **Baidu**: ERNIE (Chinese language optimized)
         """
     )
 
     gr.LoginButton()
 
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
+    run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
 
     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
@@ -207,5 +213,5 @@ with gr.Blocks() as demo:
     )
 
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
+    print("\n" + "-"*30 + " Enhanced Multi-LLM Agent Starting " + "-"*30)
     demo.launch(debug=True, share=False)