Update app.py

app.py

@@ -2,7 +2,7 @@ import os
 import gradio as gr
 import requests
 import pandas as pd
-# from langchain_community.llms import HuggingFaceHub
+from langchain_community.llms import HuggingFaceHub # Uncommented for HuggingFaceHub
 # from dotenv import load_dotenv # Uncomment for local testing with a .env file
 
 # For local testing, you might want to load environment variables from a .env file
@@ -11,82 +11,183 @@ import pandas as pd
 #     load_dotenv()
 
 # --- Constants ---
-
-# TO:
-import google.generativeai as genai # For Gemini
+# import google.generativeai as genai # For Gemini - Commented out
 
 # ... (rest of your existing imports and constants)
 
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" # This remains the same
 
-# --- Basic Agent Definition --
+# --- Basic Agent Definition -- (Gemini Agent Commented Out) ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
-    def __init__(self, google_api_key: str | None = None): # Changed parameter name for clarity
-        print("BasicAgent initializing with Google Gemini...")
+# class BasicAgent:
+#     def __init__(self, google_api_key: str | None = None): # Changed parameter name for clarity
+#         print("BasicAgent initializing with Google Gemini...")
         
-        # Determine the Google API token
-        token_to_use = google_api_key
-        if not token_to_use:
-            token_to_use = os.getenv("GOOGLE_API_KEY") # Standard environment variable for Google API keys
+#         # Determine the Google API token
+#         token_to_use = google_api_key
+#         if not token_to_use:
+#             token_to_use = os.getenv("GOOGLE_API_KEY") # Standard environment variable for Google API keys
+
+#         if not token_to_use:
+#             raise ValueError(
+#                 "Google API key not found. Please set GOOGLE_API_KEY "
+#                 "as a secret in your Hugging Face Space. This token is required for Gemini."
+#             )
+        
+#         try:
+#             # Configure the Gemini client
+#             genai.configure(api_key=token_to_use)
+            
+#             self.model_name = "gemini-1.5-pro-latest" # Or "gemini-pro"
+            
+#             self.llm = genai.GenerativeModel(self.model_name)
+            
+#             self.generation_config = genai.types.GenerationConfig(
+#                 temperature=0.1, 
+#             )
+#             self.safety_settings = [
+#                 {
+#                     "category": "HARM_CATEGORY_HARASSMENT",
+#                     "threshold": "BLOCK_MEDIUM_AND_ABOVE" 
+#                 },
+#                 {
+#                     "category": "HARM_CATEGORY_HATE_SPEECH",
+#                     "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+#                 },
+#                 {
+#                     "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
+#                     "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+#                 },
+#                 {
+#                     "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
+#                     "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+#                 }
+#             ]
+
+#             print(f"BasicAgent initialized with Google Gemini model: {self.model_name}")
+#         except Exception as e:
+#             print(f"Error initializing Google Gemini client: {e}")
+#             raise ValueError(f"Failed to initialize Gemini: {e}. Check API key and model name.")
+
+#     def __call__(self, question: str, task_id: str | None = None) -> str:
+#         print(f"Agent (Gemini) received question (Task ID: {task_id}, first 80 chars): {question[:80]}...")
+
+#         current_prompt = f"""You are a diligent and highly intelligent AI assistant. Your goal is to answer the given `Question` accurately and concisely.
+# If the question requires multiple steps or information from tools, think step-by-step.
+# **Available Tools (Conceptual - for your reasoning process, actual tool calls are not implemented in this version):**
+# 1.  **`GAIAFileLookup(filename: str) -> str`**: Retrieves file content.
+# 2.  **`Calculator(expression: str) -> str`**: Performs calculations.
+# 3.  **`LLM_Query(sub_question: str) -> str`**: For general knowledge.
+# **Output Format Expectation:**
+# While you might reason using a "Thought:", "Action:", "Observation:" cycle internally, for this specific task, your final output should be ONLY the direct answer to the question.
+# Example: If asked "What is 2+2?", your output should be "4".
+# **Key Guidelines for GAIA Submission:**
+# 1.  **Conciseness:** The final answer must be precise and directly address the question.
+# 2.  **No "FINAL ANSWER" Prefix in Submission:** Do NOT include "FINAL ANSWER:" or "The answer is:" in your actual response. Just the answer value.
+# ---
+# Now, please answer the following question:
+# Question: {question}
+# Answer:"""
+
+#         try:
+#             print(f"Sending to Gemini (first 200 chars of prompt): {current_prompt[:200]}...")
+            
+#             response = self.llm.generate_content(
+#                 current_prompt,
+#                 generation_config=self.generation_config,
+#                 safety_settings=self.safety_settings 
+#             )
+
+#             if response.candidates:
+#                 if response.candidates[0].content.parts:
+#                     response_text = response.candidates[0].content.parts[0].text
+#                 else: 
+#                     response_text = "" 
+#                     print("Warning: Gemini response has no content parts.")
+#                     if response.prompt_feedback and response.prompt_feedback.block_reason:
+#                         print(f"Prompt blocked by Gemini. Reason: {response.prompt_feedback.block_reason_message or response.prompt_feedback.block_reason}")
+#                         return f"AGENT_ERROR: Prompt blocked by Gemini ({response.prompt_feedback.block_reason})."
+#             else: 
+#                 response_text = ""
+#                 print("Warning: Gemini response has no candidates.")
+#                 if response.prompt_feedback and response.prompt_feedback.block_reason:
+#                     print(f"Prompt blocked by Gemini. Reason: {response.prompt_feedback.block_reason_message or response.prompt_feedback.block_reason}")
+#                     return f"AGENT_ERROR: Prompt blocked by Gemini ({response.prompt_feedback.block_reason})."
+#                 return "AGENT_ERROR: Gemini returned no candidates in response."
+
+
+#             answer = response_text.strip()
+            
+#             if "Answer:" in answer:
+#                 answer = answer.split("Answer:")[-1].strip()
+
+#             common_prefixes_to_remove = [
+#                 "The answer is", "My answer is", "Based on the information", "The final answer is",
+#                 "Here is the answer", "I found that", "It seems that"
+#             ]
+#             for prefix in common_prefixes_to_remove:
+#                 if answer.lower().startswith(prefix.lower()):
+#                     answer = answer[len(prefix):].strip()
+#                     if answer.startswith(":") or answer.startswith("."):
+#                         answer = answer[1:].strip()
+#                     break 
+#             if "Final Answer:" in answer:
+#                 answer = answer.split("Final Answer:")[-1].strip()
+            
+#             print(f"Agent (Gemini) LLM raw response (first 80 chars): {response_text[:80]}...")
+#             print(f"Agent (Gemini) cleaned answer (first 80 chars): {answer[:80]}...")
+            
+#             if not answer:
+#                 print("Warning: Agent (Gemini) produced an empty answer after cleaning.")
+#                 return "Unable to generate a valid answer from Gemini."
+
+#             return answer
+#         except Exception as e:
+#             if hasattr(e, 'message'): 
+#                  error_message = e.message
+#             else:
+#                  error_message = str(e)
+#             print(f"Error during Gemini LLM call for question '{question[:50]}...': {error_message}")
+#             return f"AGENT_ERROR: Gemini LLM call failed. ({type(e).__name__}: {error_message})"
+
+# --- Basic Agent Definition -- (HuggingFaceHub Agent Activated) ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+class BasicAgent:
+    def __init__(self, hf_api_token: str | None = None):
+        print("BasicAgent initializing with HuggingFaceHub...")
+        token_to_use = hf_api_token or os.getenv("HUGGINGFACEHUB_API_TOKEN") or os.getenv("HF_TOKEN")
 
         if not token_to_use:
             raise ValueError(
-                "Google API key not found. Please set GOOGLE_API_KEY "
-                "as a secret in your Hugging Face Space. This token is required for Gemini."
+                "Hugging Face API token not found. Please set HUGGINGFACEHUB_API_TOKEN or HF_TOKEN "
+                "as a secret in your Hugging Face Space. This token is required for the LLM."
             )
         
+        self.llm_repo_id = "mistralai/Mistral-7B-Instruct-v0.1" # Or your preferred model
+        # self.llm_repo_id = "HuggingFaceH4/zephyr-7b-beta" # Another option
+        # self.llm_repo_id = "google/gemma-7b-it" # Another option, ensure you have access/agreed to terms
+        
         try:
-            # Configure the Gemini client
-            genai.configure(api_key=token_to_use)
-            
-            # Specify the Gemini model you want to use.
-            # As of my last update, "gemini-1.5-pro-latest" is a good choice for high capability.
-            # "gemini-pro" is also a valid and generally available option.
-            # "gemini-2.5-pro" is not a known generally available model name as of now.
-            # Please use a valid model name from: https://ai.google.dev/models/gemini
-            self.model_name = "gemini-1.5-pro-latest" # Or "gemini-pro"
-            
-            # Create the GenerativeModel instance
-            self.llm = genai.GenerativeModel(self.model_name)
-            
-            # Define generation configuration (optional, but good for consistency)
-            self.generation_config = genai.types.GenerationConfig(
-                temperature=0.1, # For more deterministic output, good for agents
-                # max_output_tokens=512 # Equivalent to max_new_tokens
-                # You can set other parameters here like top_p, top_k
+            self.llm = HuggingFaceHub(
+                repo_id=self.llm_repo_id,
+                # Increased max_new_tokens as the ReAct prompt is long and might generate a longer thought process
+                # Temperature 0.0 for more deterministic ReAct output, 0.1 is also fine.
+                model_kwargs={"temperature": 0.1, "max_new_tokens": 512}, 
+                huggingfacehub_api_token=token_to_use
             )
-            # Define safety settings (important for Gemini to avoid overly restrictive blocking)
-            # Adjust these based on your needs and if you encounter content blocking.
-            self.safety_settings = [
-                {
-                    "category": "HARM_CATEGORY_HARASSMENT",
-                    "threshold": "BLOCK_MEDIUM_AND_ABOVE" # Or BLOCK_ONLY_HIGH, BLOCK_NONE
-                },
-                {
-                    "category": "HARM_CATEGORY_HATE_SPEECH",
-                    "threshold": "BLOCK_MEDIUM_AND_ABOVE"
-                },
-                {
-                    "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
-                    "threshold": "BLOCK_MEDIUM_AND_ABOVE"
-                },
-                {
-                    "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
-                    "threshold": "BLOCK_MEDIUM_AND_ABOVE"
-                }
-            ]
-
-            print(f"BasicAgent initialized with Google Gemini model: {self.model_name}")
+            print(f"BasicAgent initialized with LLM: {self.llm_repo_id}")
         except Exception as e:
-            print(f"Error initializing Google Gemini client: {e}")
-            raise ValueError(f"Failed to initialize Gemini: {e}. Check API key and model name.")
+            print(f"Error initializing HuggingFaceHub: {e}")
+            raise ValueError(f"Failed to initialize LLM: {e}. Check token and model repo_id.")
 
     # Modified signature to accept task_id (though not used in this simple version yet)
     def __call__(self, question: str, task_id: str | None = None) -> str:
-        print(f"Agent (Gemini) received question (Task ID: {task_id}, first 80 chars): {question[:80]}...")
+        print(f"Agent (HF) received question (Task ID: {task_id}, first 80 chars): {question[:80]}...")
 
+        # Prompt engineering is crucial.
+        # The `question` variable (method argument) is now correctly inserted here.
+        # This is a single-shot prompt. A true ReAct agent would have a loop.
         current_prompt = f"""You are a diligent and highly intelligent AI assistant. Your goal is to answer the given `Question` accurately and concisely.
 If the question requires multiple steps or information from tools, think step-by-step.
 
@@ -109,186 +210,51 @@ Example: If asked "What is 2+2?", your output should be "4".
 Now, please answer the following question:
 Question: {question}
 
-Answer:"""
+Answer:""" # Modified to guide the LLM towards a direct answer for this simplified agent
 
         try:
-            print(f"Sending to Gemini (first 200 chars of prompt): {current_prompt[:200]}...")
-            
-            # FROM: response_text = self.llm.invoke(current_prompt)
-            # TO: Use Gemini's generate_content method
-            response = self.llm.generate_content(
-                current_prompt,
-                generation_config=self.generation_config,
-                safety_settings=self.safety_settings 
-            )
-
-            # Extract the text from the Gemini response object
-            # Need to handle potential errors or blocked content
-            if response.candidates:
-                if response.candidates[0].content.parts:
-                    response_text = response.candidates[0].content.parts[0].text
-                else: # Content might be empty if blocked or other issues
-                    response_text = "" 
-                    print("Warning: Gemini response has no content parts.")
-                    if response.prompt_feedback and response.prompt_feedback.block_reason:
-                        print(f"Prompt blocked by Gemini. Reason: {response.prompt_feedback.block_reason_message or response.prompt_feedback.block_reason}")
-                        return f"AGENT_ERROR: Prompt blocked by Gemini ({response.prompt_feedback.block_reason})."
-            else: # No candidates means something went wrong, possibly blocking
-                response_text = ""
-                print("Warning: Gemini response has no candidates.")
-                if response.prompt_feedback and response.prompt_feedback.block_reason:
-                    print(f"Prompt blocked by Gemini. Reason: {response.prompt_feedback.block_reason_message or response.prompt_feedback.block_reason}")
-                    return f"AGENT_ERROR: Prompt blocked by Gemini ({response.prompt_feedback.block_reason})."
-                return "AGENT_ERROR: Gemini returned no candidates in response."
-
-
+            print(f"Sending to LLM (HF Hub) (first 200 chars of prompt): {current_prompt[:200]}...")
+            # Langchain's HuggingFaceHub.invoke expects a string and returns a string
+            response_text = self.llm.invoke(current_prompt) 
             answer = response_text.strip()
             
-            # (Your existing cleaning logic can largely remain the same)
+            # Further cleaning if the model still adds prefixes or explanations
+            # This is important because we are not doing a full ReAct loop to extract "Final Answer:"
+            
+            # Try to find "Answer:" if the LLM adds it despite instructions
             if "Answer:" in answer:
+                # Take text after the last occurrence of "Answer:"
                 answer = answer.split("Answer:")[-1].strip()
 
+            # Remove common conversational prefixes that might slip through
             common_prefixes_to_remove = [
                 "The answer is", "My answer is", "Based on the information", "The final answer is",
                 "Here is the answer", "I found that", "It seems that"
-            ]
+            ] # Case-insensitive removal
             for prefix in common_prefixes_to_remove:
                 if answer.lower().startswith(prefix.lower()):
                     answer = answer[len(prefix):].strip()
+                    # If the first character is now a colon or period, remove it
                     if answer.startswith(":") or answer.startswith("."):
                         answer = answer[1:].strip()
-                    break 
-            if "Final Answer:" in answer:
-                answer = answer.split("Final Answer:")[-1].strip()
+                    break # Only remove one such prefix
+
+            # If the LLM generated a ReAct-style "Final Answer:", extract from it.
+            if "Final Answer:" in answer: # Check if "Final Answer:" exists in the string
+                answer = answer.split("Final Answer:")[-1].strip() # Get content after "Final Answer:"
             
-            print(f"Agent (Gemini) LLM raw response (first 80 chars): {response_text[:80]}...")
-            print(f"Agent (Gemini) cleaned answer (first 80 chars): {answer[:80]}...")
+            print(f"Agent (HF) LLM raw response (first 80 chars): {response_text[:80]}...")
+            print(f"Agent (HF) cleaned answer (first 80 chars): {answer[:80]}...")
             
             if not answer:
-                print("Warning: Agent (Gemini) produced an empty answer after cleaning.")
-                return "Unable to generate a valid answer from Gemini."
+                print("Warning: Agent (HF) produced an empty answer after cleaning.")
+                return "AGENT_ERROR: LLM produced an empty answer." # More specific error
 
             return answer
         except Exception as e:
-            # Check if it's a specific Google API error for more details
-            if hasattr(e, 'message'): # google.api_core.exceptions often have a message attribute
-                 error_message = e.message
-            else:
-                 error_message = str(e)
-            print(f"Error during Gemini LLM call for question '{question[:50]}...': {error_message}")
-            return f"AGENT_ERROR: Gemini LLM call failed. ({type(e).__name__}: {error_message})"
-
-# ... (The rest of your `run_and_submit_all` function and Gradio UI code remains unchanged) ...
-
-# At the end of the file, in the __main__ block, update the warning message:
-# if __name__ == "__main__":
-    # ... (existing startup prints) ...
-    
-    # TO: Check for GOOGLE_API_KEY
-    if not os.getenv("GOOGLE_API_KEY"):
-        print("⚠️  WARNING: GOOGLE_API_KEY environment variable not found.")
-        print("   The Gemini agent will likely fail to initialize. Please set this token in your Space secrets.")
-
-    # ... (rest of existing startup prints and demo.launch()) ...
-# --- Basic Agent Definition --
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-# class BasicAgent:
-#     def __init__(self, hf_api_token: str | None = None):
-#         print("BasicAgent initializing...")
-#         token_to_use = hf_api_token or os.getenv("HUGGINGFACEHUB_API_TOKEN") or os.getenv("HF_TOKEN")
-
-#         if not token_to_use:
-#             raise ValueError(
-#                 "Hugging Face API token not found. Please set HUGGINGFACEHUB_API_TOKEN or HF_TOKEN "
-#                 "as a secret in your Hugging Face Space. This token is required for the LLM."
-#             )
-        
-#         self.llm_repo_id = "mistralai/Mistral-7B-Instruct-v0.1" # Or your preferred model
-#         try:
-#             self.llm = HuggingFaceHub(
-#                 repo_id=self.llm_repo_id,
-#                 # Increased max_new_tokens as the ReAct prompt is long and might generate a longer thought process
-#                 # Temperature 0.0 for more deterministic ReAct output, 0.1 is also fine.
-#                 model_kwargs={"temperature": 0.1, "max_new_tokens": 512}, 
-#                 huggingfacehub_api_token=token_to_use
-#             )
-#             print(f"BasicAgent initialized with LLM: {self.llm_repo_id}")
-#         except Exception as e:
-#             print(f"Error initializing HuggingFaceHub: {e}")
-#             raise ValueError(f"Failed to initialize LLM: {e}. Check token and model repo_id.")
-
-#     # Modified signature to accept task_id (though not used in this simple version yet)
-#     def __call__(self, question: str, task_id: str | None = None) -> str:
-#         print(f"Agent received question (Task ID: {task_id}, first 80 chars): {question[:80]}...")
-
-#         # Prompt engineering is crucial.
-#         # The `question` variable (method argument) is now correctly inserted here.
-#         # This is a single-shot prompt. A true ReAct agent would have a loop.
-#         current_prompt = f"""You are a diligent and highly intelligent AI assistant. Your goal is to answer the given `Question` accurately and concisely.
-# If the question requires multiple steps or information from tools, think step-by-step.
-
-# **Available Tools (Conceptual - for your reasoning process, actual tool calls are not implemented in this version):**
-
-# 1.  **`GAIAFileLookup(filename: str) -> str`**: Retrieves file content.
-# 2.  **`Calculator(expression: str) -> str`**: Performs calculations.
-# 3.  **`LLM_Query(sub_question: str) -> str`**: For general knowledge.
-
-# **Output Format Expectation:**
-# While you might reason using a "Thought:", "Action:", "Observation:" cycle internally, for this specific task, your final output should be ONLY the direct answer to the question.
-# Example: If asked "What is 2+2?", your output should be "4".
-
-# **Key Guidelines for GAIA Submission:**
-# 1.  **Conciseness:** The final answer must be precise and directly address the question.
-# 2.  **No "FINAL ANSWER" Prefix in Submission:** Do NOT include "FINAL ANSWER:" or "The answer is:" in your actual response. Just the answer value.
-
-# ---
-
-# Now, please answer the following question:
-# Question: {question}
-
-# Answer:""" # Modified to guide the LLM towards a direct answer for this simplified agent
-
-#         try:
-#             print(f"Sending to LLM (first 200 chars of prompt): {current_prompt[:200]}...")
-#             response_text = self.llm.invoke(current_prompt)
-#             answer = response_text.strip()
-            
-#             # Further cleaning if the model still adds prefixes or explanations
-#             # This is important because we are not doing a full ReAct loop to extract "Final Answer:"
-            
-#             # Try to find "Answer:" if the LLM adds it despite instructions
-#             if "Answer:" in answer:
-#                 # Take text after the last occurrence of "Answer:"
-#                 answer = answer.split("Answer:")[-1].strip()
-
-#             # Remove common conversational prefixes that might slip through
-#             common_prefixes_to_remove = [
-#                 "The answer is", "My answer is", "Based on the information", "The final answer is",
-#                 "Here is the answer", "I found that", "It seems that"
-#             ] # Case-insensitive removal
-#             for prefix in common_prefixes_to_remove:
-#                 if answer.lower().startswith(prefix.lower()):
-#                     answer = answer[len(prefix):].strip()
-#                     # If the first character is now a colon or period, remove it
-#                     if answer.startswith(":") or answer.startswith("."):
-#                         answer = answer[1:].strip()
-#                     break # Only remove one such prefix
+            print(f"Error during LLM call for question '{question[:50]}...': {e}")
+            return f"AGENT_ERROR: LLM call failed. ({type(e).__name__}: {str(e)})"
 
-#             # If the LLM generated a ReAct-style "Final Answer:", extract from it.
-#             if "Final Answer:" in answer:
-#                 answer = answer.split("Final Answer:")[-1].strip()
-            
-#             print(f"Agent LLM raw response (first 80 chars): {response_text[:80]}...")
-#             print(f"Agent cleaned answer (first 80 chars): {answer[:80]}...")
-            
-#             if not answer:
-#                 print("Warning: Agent produced an empty answer after cleaning.")
-#                 return "Unable to generate a valid answer."
-
-        #     return answer
-        # except Exception as e:
-        #     print(f"Error during LLM call for question '{question[:50]}...': {e}")
-        #     return f"AGENT_ERROR: LLM call failed. ({type(e).__name__})"
 
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
@@ -310,7 +276,8 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
 
     # 1. Instantiate Agent
     try:
-        agent = BasicAgent()
+        # This will now instantiate the HuggingFaceHub BasicAgent
+        agent = BasicAgent() 
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {str(e)}", None
@@ -422,14 +389,14 @@ with gr.Blocks() as demo:
     gr.Markdown(
         """
         **Instructions:**
-        1.  This Space uses a `BasicAgent` with an LLM. Ensure you have set your `HUGGINGFACEHUB_API_TOKEN` or `HF_TOKEN` in the Space secrets for the LLM to work.
+        1.  This Space uses a `BasicAgent` with an LLM from HuggingFace Hub. Ensure you have set your `HUGGINGFACEHUB_API_TOKEN` or `HF_TOKEN` in the Space secrets for the LLM to work.
         2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
         3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
         ---
         **Disclaimers:**
         Once clicking on the "submit" button, it can take quite some time (this is the time for the agent to go through all the questions using an LLM).
         This space provides a basic setup. For better GAIA scores, you might need to:
-            - Choose a more powerful LLM.
+            - Choose a more powerful LLM (e.g., from the `llm_repo_id` options in `BasicAgent` or others).
             - Implement a proper ReAct loop with tool parsing and execution.
             - Implement actual tool usage (e.g., `/files/{task_id}`, calculator).
         """
@@ -437,18 +404,33 @@ with gr.Blocks() as demo:
 
     hf_profile_state = gr.State(None) 
     
-    def login_handler(profile: gr.OAuthProfile | None):
-        if profile:
-            print(f"Profile captured: {profile.username}")
-        return profile
-
-    gr.LoginButton()
+    # This handler is not strictly necessary for the profile data itself if just using gr.LoginButton()
+    # but can be useful if you need to react to login events beyond what the button click does.
+    # For this app, `profile` argument to `run_and_submit_all` is handled directly by Gradio if login is used.
+    # def login_handler(profile: gr.OAuthProfile | None):
+    #     if profile:
+    #         print(f"Profile captured: {profile.username}")
+    #     return profile
+
+    # The LoginButton itself enables OAuth.
+    # When `run_and_submit_all` is called, if the user is logged in, 
+    # Gradio automatically passes the gr.OAuthProfile object as the first argument
+    # if the function signature expects it (like `profile: gr.OAuthProfile | None`).
+    login_button = gr.LoginButton() 
+    
     run_button = gr.Button("Run Evaluation & Submit All Answers")
     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
+    # The `login_button` itself doesn't need to be an input to `run_and_submit_all`
+    # if `run_and_submit_all` is typed with `gr.OAuthProfile | None` as its first argument.
+    # Gradio handles passing the profile automatically on click if the user is logged in.
+    # If the user is not logged in, `profile` will be `None`.
     run_button.click(
         fn=run_and_submit_all,
+        # No explicit inputs needed here if the first arg of fn is type-hinted with gr.OAuthProfile
+        # and you are using gr.LoginButton(). Gradio handles this.
+        # inputs=[hf_profile_state], # Not needed if using gr.OAuthProfile type hint
         outputs=[status_output, results_table]
     )
 
@@ -470,9 +452,18 @@ if __name__ == "__main__":
     else:
         print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
     
+    # Updated token check for HuggingFace Hub
     if not (os.getenv("HUGGINGFACEHUB_API_TOKEN") or os.getenv("HF_TOKEN")):
         print("⚠️  WARNING: HUGGINGFACEHUB_API_TOKEN or HF_TOKEN environment variable not found.")
         print("   The LLM agent will likely fail to initialize. Please set this token in your Space secrets.")
+    # else: # Optional: confirm if token is found
+    #     print("✅ HUGGINGFACEHUB_API_TOKEN or HF_TOKEN found (or assumed to be set).")
+
+
+    # Commented out the GOOGLE_API_KEY check as it's no longer relevant for this agent
+    # if not os.getenv("GOOGLE_API_KEY"):
+    #     print("⚠️  WARNING: GOOGLE_API_KEY environment variable not found.")
+    #     print("   The Gemini agent will likely fail to initialize. Please set this token in your Space secrets.")
 
     print("-"*(60 + len(" App Starting ")) + "\n")
     print("Launching Gradio Interface for Basic Agent Evaluation...")