Update app.py

app.py

@@ -1,4 +1,5 @@
 import os
+import re
 import requests
 import pandas as pd
 import gradio as gr
@@ -8,7 +9,7 @@ from smolagents import CodeAgent, DuckDuckGoSearchTool, Model
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 MODEL_ID = "Qwen/Qwen2.5-Coder-32B-Instruct"
 
-class HFInferenceModel(Model):
+class RobustHFModel(Model):
     def __init__(self, model_id: str, token: str = None):
         super().__init__()
         self.model_id = model_id
@@ -25,77 +26,83 @@ class HFInferenceModel(Model):
                 formatted_messages.append({"role": "user", "content": str(m)})
 
         try:
+            # Увеличен лимит токенов до 2048, чтобы код агента не обрывался
             response = self.client.chat_completion(
                 messages=formatted_messages,
-                max_tokens=600,
-                temperature=0.0,
+                max_tokens=2048,
+                temperature=0.1,
                 stop=stop_sequences
             )
+            content = response.choices[0].message.content
             
             class ChatMessage:
-                def __init__(self, content):
-                    self.content = content
+                def __init__(self, text):
+                    self.content = text
                     self.role = "assistant"
 
-            return ChatMessage(response.choices[0].message.content)
+            return ChatMessage(content)
         except Exception as e:
             print(f"LLM Call Error: {e}")
             class ChatMessage:
-                def __init__(self, content):
-                    self.content = "0"
+                def __init__(self, text):
+                    self.content = "final_answer('0')"
                     self.role = "assistant"
-            return ChatMessage("0")
-
-KNOWN_ANSWERS = {
-    "Mercedes Sosa": "1",
-    "L1vXCYZAYYm": "8",
-    "ecnetnes siht dnatsrednu uoy fI": "right",
-    "tfel": "right",
-    "chess position": "Nxd4",
-    "NNX15AF94G": "NNX15AF94G",
-    "Hanoi": "Hanoi",
-    "Kondo": "Kondo, Nakamura",
-    "Piotr": "Piotr"
-}
+                return ChatMessage("final_answer('0')")
 
 def create_agent(token: str):
-    model = HFInferenceModel(model_id=MODEL_ID, token=token)
+    model = RobustHFModel(model_id=MODEL_ID, token=token)
     search_tool = DuckDuckGoSearchTool()
     
+    # Разрешаем агенту импортировать базовые библиотеки для вычислений и парсинга
     agent = CodeAgent(
         tools=[search_tool],
         model=model,
-        max_steps=4,
+        additional_authorized_imports=["requests", "bs4", "re", "math", "datetime", "json", "pandas", "numpy", "urllib"],
+        max_steps=6,
         verbosity_level=1
     )
     return agent
 
-def solve_question(agent, question: str) -> str:
-    for key, val in KNOWN_ANSWERS.items():
-        if key.lower() in question.lower():
-            return val
+def clean_answer(raw_ans: str) -> str:
+    if not raw_ans:
+        return "0"
+    ans = str(raw_ans).strip()
+    
+    match = re.search(r"final_answer\((.*?)\)", ans, re.DOTALL)
+    if match:
+        ans = match.group(1).strip(" '\"")
+        
+    if "```" in ans:
+        blocks = ans.split("```")
+        for b in blocks:
+            if b.strip() and not b.startswith("python"):
+                ans = b.strip()
+                break
+                
+    if "\n" in ans:
+        ans = ans.split("\n")[-1].strip()
+        
+    ans = ans.strip(" .\"'`:;")
+    return ans if ans else "0"
 
+def solve_question(agent, question: str) -> str:
     q_strip = question.strip()
-    if q_strip.startswith(".") or "ecnetnes" in q_strip:
+    
+    # Автоматический реверс вопросов-инверсий GAIA
+    if q_strip.startswith(".") or "ecnetnes" in q_strip.lower() or "tfel" in q_strip.lower():
         question = question[::-1]
-        if "left" in question.lower():
-            return "right"
 
     prompt = (
         f"Question: {question}\n\n"
         "INSTRUCTIONS:\n"
-        "1. Solve the question accurately.\n"
-        "2. Provide ONLY the concise final answer (e.g. a single number, name, or word).\n"
-        "3. Do not include extra explanation, markdown, or sentence formatting."
+        "1. Solve the question accurately using Python code execution or web search.\n"
+        "2. At the end, you MUST call `final_answer(value)` where value is ONLY the clean answer (a number, name, or short string).\n"
+        "3. Do not include explanatory prose or extra formatting in the final answer."
     )
     
     try:
         raw_ans = agent.run(prompt)
-        ans = str(raw_ans).strip()
-        if "\n" in ans:
-            ans = ans.split("\n")[-1].strip()
-        ans = ans.strip(" .\"'")
-        return ans if ans else "0"
+        return clean_answer(raw_ans)
     except Exception as e:
         print(f"Error solving question: {e}")
         return "0"