Update app.py

app.py

@@ -12,49 +12,63 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 # --- YENİ ALFRED AJANI ---
 token = os.getenv("HF_TOKEN")
-model = HfApiModel(model_id="meta-llama/Llama-3.1-70B-Instruct", token=token)
+model = HfApiModel(model_id="Qwen/Qwen2.5-72B-Instruct", token=token)
 
 class AlfredAgent:
     def __init__(self):
-        CUSTOM_SYSTEM_PROMPT = """You are a highly efficient GAIA solver.
-1. Use DuckDuckGo or Wikipedia to find facts.
-2. When you visit a page, look for tables. If it's too messy, try another source.
-3. Your FINAL ANSWER must be ONLY the requested value (name, date, or number).
-4. Be fast. If stuck, make your best logical guess based on info found.
-
-Here are the tools you can use:
+        CUSTOM_SYSTEM_PROMPT = """You are a helpful and agile assistant that has access to a set of tools. 
+For every step, you must provide your reasoning in a 'Thoughts:' section and then the tool call in a 'Code:' section.
+
+Format:
+Thoughts: I need to search for...
+Code:
+```py
+print(web_search("question"))
+```<end_action>
+
+Rules:
+1. Provide ONLY the final answer value. No sentences.
+2. For dates, use YYYY-MM-DD.
+3. If you find the info, stop and give the answer immediately.
+
+Tools:
 {{managed_agents_descriptions}}
-
-You can use Python for calculations:
 {{authorized_imports}}"""
 
         self.agent = CodeAgent(
-            # DuckDuckGo'yu listeden çıkardık çünkü add_base_tools=True onu zaten getiriyor
-            tools=[VisitWebpageTool()], 
+            tools=[VisitWebpageTool(), DuckDuckGoSearchTool()], 
             model=model,
-            max_steps=8, 
-            add_base_tools=True, # Arama aracı buradan otomatik geliyor
+            max_steps=10,
+            add_base_tools=False, 
             planning_interval=2,
             system_prompt=CUSTOM_SYSTEM_PROMPT
         )
-        print("AlfredAgent (Llama-Powered) kuruldu.")
 
     def __call__(self, question: str) -> str:
-        prompt = f"""Task: {question}
-        
-        RULES:
-        - Provide ONLY the final value.
-        - Date: YYYY-MM-DD
-        - No units, no extra words."""
-        
         try:
-            result = self.agent.run(prompt)
-            clean_result = str(result).strip()
-            # Gereksiz kalıpları temizleme
-            if "Final Answer:" in clean_result:
-                clean_result = clean_result.split("Final Answer:")[-1].strip()
-            return clean_result[:50] # Çok uzun cevapları engelle
-        except Exception as e:
+            # Soruya net kural ekliyoruz
+            full_prompt = f"Solve this GAIA task precisely: {question}. Return ONLY the value."
+            result = self.agent.run(full_prompt)
+            
+            # İlk temizlik
+            ans = str(result).strip()
+            
+            # Eğer model "Final Answer: 1928" dediyse sadece 1928'i al
+            if "Final Answer:" in ans:
+                ans = ans.split("Final Answer:")[-1].strip()
+            
+            # Kelime bazlı temizlik (gereksiz ekleri atar)
+            for word in ["is:", "answer:", "result:", "the answer is"]:
+                if word in ans.lower():
+                    ans = ans.lower().split(word)[-1].strip()
+            
+            # Hala çok uzunsa (muhtemelen paragraf yazdı), sadece en son satırı/kelimeyi al
+            if len(ans) > 60:
+                ans = ans.split('\n')[-1].strip(" .\"'")
+                
+            return ans
+        except Exception:
+            # Hata anında bile "None" yerine "Unknown" dönerek formatı koruyoruz
             return "Unknown"
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
@@ -107,37 +121,32 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         print(f"An unexpected error occurred fetching questions: {e}")
         return f"An unexpected error occurred fetching questions: {e}", None
 
-    # 3. Run your Agent
+    # 3. Run your Agent (Bu kısmı bul ve değiştir)
     results_log = []
     answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
     
     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
+        q_text = item.get("question")
+        if not task_id: continue
             
-        print(f"\n>>> ÇÖZÜLÜYOR: Task {task_id}")
-        
+        print(f"\n--- Görev: {task_id} ---")
         try:
-            # Alfred'i çağırıyoruz
-            raw_answer = agent(question_text)
+            answer = agent(q_text)
             
-            # Sınav taktiği: Cevabı iyice temizle
-            final_fix = str(raw_answer).lower().replace("the answer is", "").replace("final answer:", "").strip(" .\"'")
-            
-            # Eğer cevap hala çok uzunsa sadece ilk kelimeyi al (genelde isim veya sayıdır)
-            if len(final_fix.split()) > 5:
-                final_fix = final_fix.split()[-1]
-
-            answers_payload.append({"task_id": task_id, "submitted_answer": final_fix})
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": final_fix})
-            print(f"BULDUM: {final_fix}")
+            # Sınav formatına zorla (lowercase ve temizlik)
+            final_ans = str(answer).replace('"', '').replace("'", "").strip()
             
-        except Exception as e:
-             answers_payload.append({"task_id": task_id, "submitted_answer": "Unknown"})
-             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": "ERROR"})
+            # Eğer model çok uzun bir şey döndürdüyse, GAIA bunu kabul etmez.
+            # İlk 2-3 kelimeyi veya sayıyı almaya çalışalım.
+            if len(final_ans) > 50:
+                final_ans = final_ans[:47] + "..."
+
+            answers_payload.append({"task_id": task_id, "submitted_answer": final_ans})
+            results_log.append({"Task ID": task_id, "Question": q_text, "Submitted Answer": final_ans})
+            print(f"Cevap Kaydedildi: {final_ans}")
+        except:
+            answers_payload.append({"task_id": task_id, "submitted_answer": "Unknown"})
 
     # 4. Prepare Submission 
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}