Create agent.py

agent.py

@@ -0,0 +1,57 @@
+import os
+import google.generativeai as genai
+
+class GeminiAgent:
+    """
+    An agent that uses the Gemini-1.5-Pro model to answer questions.
+    """
+    def __init__(self):
+        """
+        Initializes the agent, configures the Gemini API key, and sets up the model.
+        Raises a ValueError if the GEMINI_API_KEY is not found in the environment secrets.
+        """
+        print("Initializing GeminiAgent...")
+        
+        # 1. Get API Key from Hugging Face Secrets
+        api_key = os.getenv("GEMINI_API_KEY")
+        if not api_key:
+            raise ValueError("GEMINI_API_KEY secret not found! Please set it in your Space's settings.")
+        
+        # 2. Configure the Generative AI client
+        genai.configure(api_key=api_key)
+        
+        # 3. Initialize the Gemini 1.5 Pro model
+        self.model = genai.GenerativeModel('gemini-1.5-pro-latest')
+        print("GeminiAgent initialized successfully.")
+
+    def __call__(self, question: str) -> str:
+        """
+        Processes a question by sending it to the Gemini model and returns the stripped text answer.
+        
+        The prompt is engineered to request a direct, exact-match answer as required by the competition.
+        """
+        print(f"Agent received question (first 80 chars): {question[:80]}...")
+        
+        # Prompt engineered for the "EXACT MATCH" requirement.
+        # It instructs the model to provide only the answer and nothing else.
+        prompt = f"""You are an expert problem-solving agent. Your goal is to answer the following question as accurately as possible.
+The evaluation system requires an EXACT MATCH. Therefore, you must provide only the final answer and nothing else.
+Do not include any introductory text, explanations, or the phrase "FINAL ANSWER".
+For example, if the question asks for a specific year, your response should be just "2023". If it's a name, just "John Doe". If it is a number, just "42".
+
+Question: {question}
+
+Final Answer:"""
+
+        try:
+            # 4. Call the model
+            response = self.model.generate_content(prompt)
+            
+            # 5. Extract and clean the answer
+            final_answer = response.text.strip()
+            print(f"Agent returning answer: {final_answer}")
+            return final_answer
+        
+        except Exception as e:
+            print(f"An error occurred while calling the Gemini API: {e}")
+            return f"Error processing question: {e}"