Update agent.py

agent.py

@@ -3,32 +3,25 @@ import re
 import google.generativeai as genai
 from tools import web_search, read_file_from_api, python_interpreter
 
-# --- NEW, MORE ADVANCED REACT PROMPT ---
+# --- UPGRADED REACT PROMPT ---
 REACT_PROMPT = """
 You are a state-of-the-art, helpful AI agent designed to solve complex, multi-step problems.
 
 **Your Task:**
-Your goal is to answer the user's question with 100% accuracy. To do this, you will operate in a loop of Thought, Action, and Observation. You must break down the problem into a series of smaller steps.
+Your goal is to answer the user's question with 100% accuracy. You must operate in a loop of Thought, Action, and Observation. Break the problem down into a series of smaller steps.
 
 **Your Tools:**
 You have access to the following tools. Choose ONE tool per turn.
 1.  `web_search[query]`: Use this to find current information, facts, or to research topics.
-2.  `read_file_from_api[task_id]`: Use this ONLY when the question or an agent note explicitly mentions an attached file. It reads the raw content of that file.
-3.  `python_interpreter[code]`: Use this for all calculations, data processing, and complex logic.
-    - **This tool is powerful.** It has the `pandas` and `openpyxl` libraries installed.
-    - You can use it to analyze data from files. For example, after using `read_file_from_api`, you can pass the raw content into a Python script for processing.
-    - For Excel files, it's often better to use `pandas.read_excel(file_url)` directly within the python tool, where `file_url` can be constructed from the task_id.
+2.  `read_file_from_api[task_id]`: Use this ONLY when the question explicitly mentions an attached file.
+3.  `python_interpreter[code]`: Use this for all calculations, data processing (with pandas), and complex logic.
 
-**Reasoning Process:**
-1.  **Thought:** Carefully analyze the question. Identify the required information and devise a step-by-step plan.
-2.  **Action:** Choose the appropriate tool and input to execute the current step of your plan. Your action MUST be in the format `Action: tool_name[input]`.
-3.  **Observation:** You will receive the result of your action.
-4.  **Repeat:** Analyze the observation and continue with the next step in your plan until you have the final answer.
-
-**CRITICAL SUBMISSION RULE:**
-When you have the final, definitive answer, you MUST format your response as:
+**CRITICAL INSTRUCTIONS:**
+1.  Your reasoning process is: Thought -> Action -> Observation.
+2.  You MUST continue this loop until you are certain of the answer.
+3.  When you have the final, definitive answer, your ABSOLUTELY LAST output line MUST be in the format:
 `Final Answer: [The single, exact answer]`
-The system will automatically extract only the text after this prefix for submission. Do not add any other text or explanation.
+4.  Do not output any other text or explanation after the `Final Answer:` line.
 
 ---
 Here is the problem:
@@ -38,10 +31,9 @@ Question: {question}
 class GeminiAgent:
     def __init__(self):
         print("Initializing GeminiAgent (Advanced ReAct)...")
-        # ... (init logic remains the same: api_key, model, tools)
         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.")
+            raise ValueError("GEMINI_API_KEY secret not found!")
         genai.configure(api_key=api_key)
         self.model = genai.GenerativeModel('gemini-2.5-pro')
         self.tools = {
@@ -51,32 +43,35 @@ class GeminiAgent:
         }
         print("GeminiAgent initialized successfully with model 'gemini-2.5-pro'.")
 
-
-    # MODIFIED to accept task_id
     def __call__(self, question: str, task_id: str) -> str:
         prompt = REACT_PROMPT.format(question=question)
         
-        # ReAct loop
         for turn in range(10): # Max 10 turns
             print(f"\n--- Turn {turn + 1} for Task ID: {task_id} ---\n")
             
             response = self.model.generate_content(prompt)
             if not response.parts:
-                print("Warning: Model returned an empty response.")
                 prompt += "\nObservation: The model returned an empty response. Please try again."
                 continue
             
             response_text = response.text
             print(f"LLM Response:\n{response_text}\n")
             
-            # Check for Final Answer
-            final_answer_match = re.search(r"Final Answer: (.*)", response_text, re.DOTALL)
-            if final_answer_match:
-                answer = final_answer_match.group(1).strip()
-                print(f"Final Answer extracted: '{answer}'")
-                return answer
+            # Use re.findall to get ALL occurrences of Final Answer
+            final_answer_matches = re.findall(r"Final Answer: (.*)", response_text, re.DOTALL)
+            if final_answer_matches:
+                # The model sometimes outputs multiple 'Final Answer' lines. The last one is the most correct.
+                final_answer = final_answer_matches[-1].strip()
+                
+                # --- NEW: Robust cleaning of the final answer ---
+                # Remove common trailing punctuation that isn't part of the answer itself.
+                # This handles cases like 'Claus.' but preserves '1759.70'.
+                if not final_answer.isnumeric():
+                     final_answer = final_answer.rstrip('.?!,')
+
+                print(f"Final Answer extracted and cleaned: '{final_answer}'")
+                return final_answer
 
-            # Look for an Action
             action_match = re.search(r"Action: (\w+)\[(.*)\]", response_text, re.DOTALL)
             if not action_match:
                 observation = "No valid 'Action:' or 'Final Answer:' found. Please think step-by-step and select a tool or provide the final answer."
@@ -88,14 +83,14 @@ class GeminiAgent:
                     observation = f"Error: Unknown tool '{tool_name}'."
                 else:
                     try:
-                        # Pass the task_id to the tool function, which can then use it if needed
                         observation = self.tools[tool_name](tool_input if tool_name != 'read_file_from_api' else task_id)
                     except Exception as e:
                         observation = f"Error executing tool {tool_name}: {e}"
             
             print(f"Observation:\n{observation}\n")
-
-            # Append the full turn to the prompt
             prompt += f"{response_text}\nObservation: {observation}\n"
 
-        return "Agent failed to find an answer within 10 turns."
+        # Fallback if the agent gets stuck
+        last_guess = response_text.split("Final Answer:")[-1].strip()
+        print(f"Agent failed to find a 'Final Answer:' signal. Returning last guess: {last_guess}")
+        return last_guess