Update agent.py

agent.py

@@ -1,37 +1,25 @@
-import os
-from openai import OpenAI
-from tools.file_loader import load_file_if_any
+# agent.py (Updated for Hugging Face inference with FLAN-T5)
 
-client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+from transformers import pipeline
 
-SYSTEM_PROMPT = """You are an advanced AI agent participating in the GAIA benchmark. 
-For each question, read the text and any attached file, then provide a concise and accurate final answer.
-Only return the answer itself — no explanations or formatting."""
+# Load the Hugging Face inference pipeline
+qa_pipeline = pipeline("text2text-generation", model="google/flan-t5-base")
 
-def answer_question(question_obj):
-    try:
-        file_content = ""
-        if question_obj.get("file_path"):
-            file_content = load_file_if_any(question_obj["file_path"])
-
-        user_prompt = f"""Question:
-{question_obj['question']}
-
-Attached file content (if any):
-{file_content}
+def answer_question(question: str, file_context: str = None, do_search: bool = False) -> str:
+    """
+    Answers the question using file context (if any) with a Hugging Face model.
 
-Final answer:"""
-
-        # ✅ Use GPT-3.5-turbo (free-tier)
-        response = client.chat.completions.create(
-            model="gpt-3.5-turbo",
-            messages=[
-                {"role": "system", "content": SYSTEM_PROMPT},
-                {"role": "user", "content": user_prompt}
-            ]
-        )
-
-        return response.choices[0].message.content.strip()
+    Args:
+        question (str): The question to be answered.
+        file_context (str, optional): Optional context extracted from a file.
+        do_search (bool): Ignored for local mode.
 
+    Returns:
+        str: The generated answer.
+    """
+    prompt = question if not file_context else f"Context: {file_context}\nQuestion: {question}"
+    try:
+        result = qa_pipeline(prompt, max_length=256, do_sample=False)
+        return result[0]["generated_text"].strip()
     except Exception as e:
-        return f"[ERROR] Failed to answer: {str(e)}"
+        return f"[ERROR] Hugging Face pipeline failed: {e}"