Update agent.py

agent.py

@@ -54,7 +54,6 @@ from langchain.agents import initialize_agent, Tool, AgentType
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 from huggingface_hub import login
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, BitsAndBytesConfig
-
 from langchain_huggingface import HuggingFaceEndpoint
 
 load_dotenv()
@@ -329,22 +328,35 @@ tools = [tool_map[name] for name in enabled_tool_names]
 # Step 2: Load the JSON file or tasks (Replace this part if you're loading tasks dynamically)
 # -------------------------------
 # Here we assume the tasks are already fetched from a URL or file.
-# For now, using an example JSON array directly. Replace this with the actual loading logic.
-
-tasks = [
-    {
-        "task_id": "8e867cd7-cff9-4e6c-867a-ff5ddc2550be",
-        "question": "How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)? You can use the latest 2022 version of English Wikipedia.",
-        "Level": "1",
-        "file_name": ""
-    },
-    {
-        "task_id": "a1e91b78-d3d8-4675-bb8d-62741b4b68a6",
-        "question": "In the video https://www.youtube.com/watch?v=L1vXCYZAYYM, what is the highest number of bird species to be on camera simultaneously?",
-        "Level": "1",
-        "file_name": ""
-    }
-]
+
+
+# Replace this with your actual URL
+json_url = "https://huggingface.co/spaces/wt002/Final_Assignment_Project/blob/main/questions.json"
+
+try:
+    # Fetch the JSON content from the URL
+    response = requests.get(json_url)
+    response.raise_for_status()  # Raise error if the request failed
+
+    tasks = response.json()  # Parse JSON content
+    print(f"✅ Loaded {len(tasks)} tasks from URL")
+
+    # Convert each task to a LangChain Document
+    docs = []
+    for task in tasks:
+        question = task.get("question", "").strip()
+        if not question:
+            print(f"⚠️ Skipping task with empty question: {task}")
+            continue
+
+        task["id"] = str(uuid.uuid4())
+        docs.append(Document(page_content=question, metadata=task))
+
+except requests.RequestException as e:
+    print(f"❌ Failed to fetch JSON from URL: {e}")
+except ValueError as e:
+    print(f"❌ Invalid JSON format: {e}")
+
 
 # -------------------------------
 # Step 3: Create Documents from Each JSON Object