Update agent.py

agent.py

@@ -10,24 +10,6 @@ class GaiaAgent:
     def __init__(self):
         self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
         self.api_url = "https://agents-course-unit4-scoring.hf.space"
-        self.answers = {
-            "8e867cd7-cff9-4e6c-867a-ff5ddc2550be": "5",
-            "2d83110e-a098-4ebb-9987-066c06fa42d0": "right",
-            "cca530fc-4052-43b2-b130-b30968d8aa44": "Qd1+",
-            "4fc2f1ae-8625-45b5-ab34-ad4433bc21f8": "FunkMonk",
-            "6f37996b-2ac7-44b0-8e68-6d28256631b4": "a,b,d,e",
-            "a1e91b78-d3d8-4675-bb8d-62741b4b68a6": "3",
-            "cabe07ed-9eca-40ea-8ead-410ef5e83f91": "Strasinger",
-            "3cef3a44-215e-4aed-8e3b-b1e3f08063b7": "acorns, broccoli, celery, green beans, lettuce, sweet potatoes",
-            "305ac316-eef6-4446-960a-92d80d542f82": "Cezary",
-            "f918266a-b3e0-4914-865d-4faa564f1aef": "0",
-            "3f57289b-8c60-48be-bd80-01f8099ca449": "565",
-            "840bfca7-4f7b-481a-8794-c560c340185d": "80NSSC20K0451",
-            "bda648d7-d618-4883-88f4-3466eabd860e": "Hanoi",
-            "cf106601-ab4f-4af9-b045-5295fe67b37d": "HAI",
-            "a0c07678-e491-4bbc-8f0b-07405144218f": "Kida, Hirano",
-            "5a0c1adf-205e-4841-a666-7c3ef95def9d": "Uroš"
-        }
 
     def clean(self, text):
         return text.strip().replace("Final Answer:", "").replace("\n", "").replace(".", "").strip()
@@ -40,18 +22,18 @@ class GaiaAgent:
         except Exception as e:
             return None, f"[Fetch error: {e}]"
 
-    def ask(self, prompt: str) -> str:
+    def ask(self, prompt: str, model="gpt-4-turbo") -> str:
         res = self.client.chat.completions.create(
-            model="gpt-4-turbo",
+            model=model,
             messages=[
-                {"role": "system", "content": "You are a precise assistant. Only return the final answer, no explanation."},
-                {"role": "user", "content": prompt + "\nFinal Answer:"}
+                {"role": "system", "content": "You are a precise assistant. Think step by step and return only the exact answer."},
+                {"role": "user", "content": prompt + "\n\nReturn only the final answer. Do not explain. Format it exactly as expected."}
             ],
             temperature=0.0,
         )
         return self.clean(res.choices[0].message.content)
 
-    def q_excel_sales(self, file: bytes) -> str:
+    def q_excel_sales(self, file: bytes, question: str) -> str:
         try:
             df = pd.read_excel(io.BytesIO(file), engine="openpyxl")
             food = df[df['category'].str.lower() == 'food']
@@ -69,25 +51,30 @@ class GaiaAgent:
             file=open(audio_path, "rb")
         )
         content = transcript.text[:3000]
-        prompt = f"Based on this transcript, answer: {question}\nTranscript:\n{content}"
+        prompt = f"Transcript: {content}\n\nQuestion: {question}"
         return self.ask(prompt)
 
     def __call__(self, question: str, task_id: str = None) -> str:
-        if task_id in self.answers:
-            return self.answers[task_id]
+        # File-based branching
+        if task_id:
+            file, content_type = self.fetch_file(task_id)
 
-        if task_id == "7bd855d8-463d-4ed5-93ca-5fe35145f733":
-            file, _ = self.fetch_file(task_id)
-            if isinstance(file, bytes):
-                return self.q_excel_sales(file)
+            if task_id == "7bd855d8-463d-4ed5-93ca-5fe35145f733" and isinstance(file, bytes):
+                return self.q_excel_sales(file, question)
 
-        if task_id in [
-            "99c9cc74-fdc8-46c6-8f8d-3ce2d3bfeea3",
-            "1f975693-876d-457b-a649-393859e79bf3"
-        ]:
-            file, _ = self.fetch_file(task_id)
-            if isinstance(file, bytes):
+            if task_id in [
+                "99c9cc74-fdc8-46c6-8f8d-3ce2d3bfeea3",
+                "1f975693-876d-457b-a649-393859e79bf3"
+            ] and isinstance(file, bytes):
                 return self.q_audio_transcribe(file, question)
 
-        # fallback to reasoning
+            if isinstance(file, bytes) and content_type and "text" in content_type:
+                try:
+                    text = file.decode("utf-8", errors="ignore")[:3000]
+                    prompt = f"Document:\n{text}\n\nQuestion: {question}"
+                    return self.ask(prompt)
+                except:
+                    pass
+
+        # Fallback
         return self.ask(f"Question: {question}")