Update app.py

app.py

@@ -1,5 +1,3 @@
-
-
 import os
 import gradio as gr
 import requests
@@ -8,143 +6,263 @@ import pandas as pd
 from smolagents import LiteLLMModel, CodeAgent, DuckDuckGoSearchTool
 from gaia_tools import ReverseTextTool, RunPythonFileTool, download_server
 
-# System prompt for the agent
+
+# ==============================
+# System Prompt
+# ==============================
+
 SYSTEM_PROMPT = """You are a general AI assistant. I will ask you a question.
-Report your thoughts, and finish your answer with just the answer — no prefixes like "FINAL ANSWER:".
-Your answer should be a number OR as few words as possible OR a comma-separated list of numbers and/or strings.
-If you're asked for a number, don’t use commas or units like $ or %, unless specified.
-If you're asked for a string, don’t use articles or abbreviations (e.g. for cities), and write digits in plain text unless told otherwise.
-
-Tool Use Guidelines:
-1. Do **not** use any tools outside of the provided tools list.
-2. Always use **only one tool at a time** in each step of your execution.
-3. If the question refers to a `.py` file or uploaded Python script, use **RunPythonFileTool** to execute it and base your answer on its output.
-4. If the question looks reversed (starts with a period or reads backward), first use **ReverseTextTool** to reverse it, then process the question.
-5. For logic or word puzzles, solve them directly unless they are reversed — in which case, decode first using **ReverseTextTool**.
-6. When dealing with Excel files, prioritize using the **excel** tool over writing code in **terminal-controller**.
-7. If you need to download a file, always use the **download_server** tool and save it to the correct path.
-8. Even for complex tasks, assume a solution exists. If one method fails, try another approach using different tools.
-9. Due to context length limits, keep browser-based tasks (e.g., searches) as short and efficient as possible.
+Report your thoughts, and finish your answer with just the answer — no prefixes.
+
+Your answer should be:
+• A number
+OR
+• Few words
+OR
+• Comma-separated list
+
+Rules:
+
+If number:
+- No commas
+- No units
+
+If string:
+- No articles
+- No abbreviations
+- Write digits as words
+
+Tool Rules:
+
+1. Use only provided tools.
+2. Use one tool at a time.
+3. If reversed question → use ReverseTextTool.
+4. If .py file → use RunPythonFileTool.
+5. For downloads → use download_server.
 """
 
+
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# Agent wrapper using LiteLLMModel
+
+# ==============================
+# Agent Class (NVIDIA Version)
+# ==============================
+
 class MyAgent:
+
     def __init__(self):
-        gemini_api_key = os.getenv("GEMINI_API_KEY")
-        if not gemini_api_key:
-            raise ValueError("GEMINI_API_KEY not set in environment variables.")
-        
+
+        nvidia_api_key = os.getenv("NVIDIA_API_KEY")
+
+        if not nvidia_api_key:
+            raise ValueError(
+                "NVIDIA_API_KEY not set in environment variables."
+            )
+
+        # NVIDIA MiniMax model via LiteLLM
         self.model = LiteLLMModel(
-            model_id="gemini/gemini-2.0-flash-lite",
-            api_key=gemini_api_key,
+
+            model_id="openai/minimaxai/minimax-m2.5",
+
+            api_key=nvidia_api_key,
+
+            api_base="https://integrate.api.nvidia.com/v1",
+
             system_prompt=SYSTEM_PROMPT
+
         )
-        
+
         self.agent = CodeAgent(
+
             tools=[
                 DuckDuckGoSearchTool(),
                 ReverseTextTool,
                 RunPythonFileTool,
                 download_server
             ],
+
             model=self.model,
+
             add_base_tools=True,
+
         )
 
     def __call__(self, question: str) -> str:
         return self.agent.run(question)
 
-# Main evaluation function
+
+# ==============================
+# Main Evaluation Function
+# ==============================
+
 def run_and_submit_all(profile: gr.OAuthProfile | None):
+
     space_id = os.getenv("SPACE_ID")
 
     if profile:
         username = profile.username
         print(f"User logged in: {username}")
     else:
-        print("User not logged in.")
         return "Please login to Hugging Face.", None
 
+
     questions_url = f"{DEFAULT_API_URL}/questions"
     submit_url = f"{DEFAULT_API_URL}/submit"
 
+
     try:
         agent = MyAgent()
     except Exception as e:
         return f"Error initializing agent: {e}", None
 
+
+    # Fetch questions
+
     try:
-        response = requests.get(questions_url, timeout=15)
+
+        response = requests.get(
+            questions_url,
+            timeout=15
+        )
+
         response.raise_for_status()
+
         questions_data = response.json()
+
     except Exception as e:
+
         return f"Error fetching questions: {e}", None
 
+
     results_log = []
     answers_payload = []
 
+
+    # Run agent
+
     for item in questions_data:
+
         task_id = item.get("task_id")
         question_text = item.get("question")
+
         if not task_id or question_text is None:
             continue
+
         try:
+
             submitted_answer = agent(question_text)
-            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+
+            answers_payload.append({
+                "task_id": task_id,
+                "submitted_answer": submitted_answer
+            })
+
+            results_log.append({
+                "Task ID": task_id,
+                "Question": question_text,
+                "Submitted Answer": submitted_answer
+            })
+
         except Exception as e:
+
             results_log.append({
                 "Task ID": task_id,
                 "Question": question_text,
                 "Submitted Answer": f"AGENT ERROR: {e}"
             })
 
+
     if not answers_payload:
-        return "Agent did not return any answers.", pd.DataFrame(results_log)
+        return "Agent did not return answers.", pd.DataFrame(results_log)
+
 
     submission_data = {
+
         "username": profile.username.strip(),
-        "agent_code": f"https://huggingface.co/spaces/{space_id}/tree/main",
+
+        "agent_code":
+        f"https://huggingface.co/spaces/{space_id}/tree/main",
+
         "answers": answers_payload
+
     }
 
+
+    # Submit answers
+
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
+
+        response = requests.post(
+            submit_url,
+            json=submission_data,
+            timeout=60
+        )
+
         response.raise_for_status()
+
         result_data = response.json()
+
         final_status = (
             f"Submission Successful!\n"
             f"User: {result_data.get('username')}\n"
             f"Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
+            f"({result_data.get('correct_count', '?')}/"
+            f"{result_data.get('total_attempted', '?')} correct)\n"
             f"Message: {result_data.get('message', 'No message received.')}"
         )
+
         return final_status, pd.DataFrame(results_log)
+
     except Exception as e:
+
         return f"Submission failed: {e}", pd.DataFrame(results_log)
 
-# Gradio UI setup
+
+# ==============================
+# Gradio UI
+# ==============================
+
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
+
+    gr.Markdown("# NVIDIA MiniMax Agent Runner 🚀")
+
     gr.Markdown("""
-    **Instructions:**
-    1. Clone this space and configure your Gemini API key.
-    2. Log in to Hugging Face.
-    3. Run your agent on evaluation tasks and submit answers.
-    """)
+**Instructions**
+
+1. Add NVIDIA API key in Secrets  
+2. Login to HuggingFace  
+3. Click Run  
+""")
 
     gr.LoginButton()
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
-    status_output = gr.Textbox(label="Submission Result", lines=5, interactive=False)
-    results_table = gr.DataFrame(label="Results", wrap=True)
 
-    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
+    run_button = gr.Button(
+        "Run Evaluation & Submit All Answers"
+    )
 
-if __name__ == "__main__":
-    print("🔧 App starting...")
-    demo.launch(debug=True, share=False)
+    status_output = gr.Textbox(
+        label="Submission Result",
+        lines=5,
+        interactive=False
+    )
 
+    results_table = gr.DataFrame(
+        label="Results",
+        wrap=True
+    )
 
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
+
+
+if __name__ == "__main__":
+
+    print("🔧 App starting...")
 
+    demo.launch(
+        debug=True,
+        share=False
+    )