Update agent.py

agent.py

@@ -1,114 +1,58 @@
 import os
-import re
+import requests
 from openai import OpenAI
 
 class GaiaAgent:
     def __init__(self):
         self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
         self.instructions = (
-            "You are a high-precision research assistant solving GAIA benchmark questions.\n"
-            "Answer with exact values only. Use scratchpad reasoning internally, but only return the final answer."
+            "You are a highly skilled and concise research assistant solving GAIA benchmark questions.\n"
+            "Analyze attached files, extract relevant information, reason step-by-step internally,\n"
+            "and return only the final factual answer in the correct format. Avoid explanations."
         )
-        self.task_templates = {
-            "8e867cd7-cff9-4e6c-867a-ff5ddc2550be": self.q_mercedes_sosa_albums,
-            "2d83110e-a098-4ebb-9987-066c06fa42d0": self.q_reversed_text,
-            "3cef3a44-215e-4aed-8e3b-b1e3f08063b7": self.q_botanical_vegetables,
-            "6f37996b-2ac7-44b0-8e68-6d28256631b4": self.q_commutative,
-            "305ac316-eef6-4446-960a-92d80d542f82": self.q_ray_polish,
-            "5a0c1adf-205e-4841-a666-7c3ef95def9d": self.q_malko_name
-        }
-
-    def __call__(self, question: str, task_id: str = None) -> str:
-        if task_id in self.task_templates:
-            result = self.task_templates[task_id](question)
-            cleaned = self.clean_output(result)
-            if self.validate(task_id, cleaned):
-                return cleaned
+        self.api_url = "https://agents-course-unit4-scoring.hf.space"
+
+    def fetch_file_content(self, task_id: str) -> str:
+        try:
+            url = f"{self.api_url}/files/{task_id}"
+            response = requests.get(url, timeout=10)
+            response.raise_for_status()
+            content_type = response.headers.get("Content-Type", "")
+            if "text" in content_type or "csv" in content_type or "json" in content_type:
+                return response.text[:3000]  # Truncate to 3000 chars
+            elif "application/pdf" in content_type:
+                return "[PDF detected. Summarize manually if needed.]"
+            elif "image" in content_type:
+                return "[Image detected. Describe the image if needed.]"
+            elif "audio" in content_type:
+                return "[Audio detected. Provide transcription if needed.]"
             else:
-                return "[SKIPPED: Invalid format]"
-        return "[SKIPPED: Task not implemented]"
-
-    def clean_output(self, text: str) -> str:
-        return text.strip().replace("\n", "").replace(".", "").strip()
+                return f"[Unsupported file type: {content_type}]"
+        except Exception as e:
+            return f"[Error fetching file: {e}]"
 
-    def validate(self, task_id, text) -> bool:
-        validators = {
-            "8e867cd7-cff9-4e6c-867a-ff5ddc2550be": lambda t: t.isdigit(),
-            "2d83110e-a098-4ebb-9987-066c06fa42d0": lambda t: t.lower() == "right",
-            "3cef3a44-215e-4aed-8e3b-b1e3f08063b7": lambda t: all(x.isalpha() for x in t.replace(",", "").split()),
-            "6f37996b-2ac7-44b0-8e68-6d28256631b4": lambda t: re.match(r"^[a-e](,[a-e])*$", t),
-            "305ac316-eef6-4446-960a-92d80d542f82": lambda t: t.isalpha(),
-            "5a0c1adf-205e-4841-a666-7c3ef95def9d": lambda t: t.isalpha()
-        }
-        return validators.get(task_id, lambda _: False)(text)
+    def __call__(self, question: str, task_id: str = None) -> str:
+        file_context = ""
+        if task_id:
+            file_context = self.fetch_file_content(task_id)
+            if file_context:
+                file_context = f"Attached File Context:\n{file_context}\n"
+
+        # Add scratchpad-like structure
+        prompt = (
+            f"{self.instructions}\n\n"
+            f"{file_context}"
+            f"Question: {question}\n"
+            f"Think step-by-step to extract relevant facts and solve the task.\n"
+            f"Final Answer (no explanation, just the answer):"
+        )
 
-    def query(self, prompt: str) -> str:
         response = self.client.chat.completions.create(
             model="gpt-4-turbo",
             messages=[
                 {"role": "system", "content": self.instructions},
                 {"role": "user", "content": prompt}
             ],
-            temperature=0.0
+            temperature=0.0,
         )
-        return response.choices[0].message.content
-
-    def q_mercedes_sosa_albums(self, _: str) -> str:
-        return self.query("""
-QUESTION: How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)?
-Scratchpad:
-Step 1: Identify studio albums.
-Step 2: Filter 2000–2009.
-Step 3: Count them.
-Answer as number only.
-""")
-
-    def q_reversed_text(self, _: str) -> str:
-        return self.query("""
-QUESTION: .rewsna eht sa \"tfel\" drow eht fo etisoppo eht etirw ,ecnetnes siht dnatsrednu uoy fI
-Scratchpad:
-Step 1: Reverse the sentence.
-Step 2: Understand it.
-Step 3: The opposite of "left" is "right".
-Final Answer:
-""")
-
-    def q_botanical_vegetables(self, _: str) -> str:
-        return self.query("""
-QUESTION: Classify these items botanically and return only vegetables: milk, eggs, flour, whole bean coffee, Oreos, sweet potatoes, fresh basil, plums, green beans, rice, corn, bell pepper, whole allspice, acorns, broccoli, celery, zucchini, lettuce, peanuts.
-Scratchpad:
-Step 1: Identify botanical vegetables.
-Step 2: Exclude fruits/seeds.
-Step 3: Alphabetize.
-Final Answer: comma-separated list.
-""")
-
-    def q_commutative(self, _: str) -> str:
-        return self.query("""
-QUESTION: Given the operation table over S = {a,b,c,d,e}, return elements involved in counterexamples that prove * is not commutative. Alphabetical order, comma-separated.
-Scratchpad:
-Step 1: Test all a*b vs b*a.
-Step 2: Record unequal pairs.
-Step 3: Collect elements.
-Final Answer:
-""")
-
-    def q_ray_polish(self, _: str) -> str:
-        return self.query("""
-QUESTION: Who did the actor who played Ray in the Polish version of Everybody Loves Raymond play in Magda M.? Give only the first name.
-Scratchpad:
-Step 1: Find actor.
-Step 2: Cross-reference roles.
-Step 3: Return first name only.
-Final Answer:
-""")
-
-    def q_malko_name(self, _: str) -> str:
-        return self.query("""
-QUESTION: First name of the only Malko Competition recipient from the 20th century (after 1977) whose nationality was from a country that no longer exists?
-Scratchpad:
-Step 1: Get winners list.
-Step 2: Check nationality.
-Step 3: Return first name.
-Final Answer:
-""")
+        return response.choices[0].message.content.strip()