Update agent.py

agent.py

@@ -1,22 +1,48 @@
-from openai import OpenAI
-import requests
 import os
+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 fact-based research assistant solving GAIA benchmark questions. "
-            "For each question, reason step-by-step and output a single factual answer only."
+            "You are a highly skilled research assistant solving GAIA benchmark questions. "
+            "You can analyze documents, perform reasoning, and answer with a single factual answer only."
         )
+        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 fit token limit
+            elif "application/pdf" in content_type:
+                return "[PDF file content detected. Summarize manually or use tool.]"
+            else:
+                return f"[Unsupported file type: {content_type}]"
+        except Exception as e:
+            return f"[Error downloading or reading file: {e}]"
+
+    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"Here is the related file content:\n{file_context}\n"
+
+        prompt = f"{self.instructions}\n\n{file_context}Question: {question}\nAnswer:"
 
-    def __call__(self, question: str) -> str:
         response = self.client.chat.completions.create(
             model="gpt-4-turbo",
             messages=[
                 {"role": "system", "content": self.instructions},
-                {"role": "user", "content": question}
+                {"role": "user", "content": prompt}
             ],
             temperature=0.0,
         )
+
         return response.choices[0].message.content.strip()