Create agent.py

agent.py

@@ -0,0 +1,138 @@
+# agent.py
+
+import os
+import json
+import pandas as pd
+from smolagents import (
+    CodeAgent, 
+    LiteLLMModel, 
+    DuckDuckGoSearchTool, 
+    FinalAnswerTool,
+    VisitWebpageTool,
+    WikipediaSearchTool,
+    WebSearchTool,
+    tool,
+    OpenAIServerModel
+)
+from langchain_community.document_loaders import ArxivLoader
+from dotenv import load_dotenv
+import requests
+import yaml
+
+load_dotenv()
+
+# Custom tools
+@tool
+def arxiv_search(query: str) -> str:
+    search_docs = ArxivLoader(query=query, load_max_docs=3).load()
+    return "\n\n---\n\n".join([
+        f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
+        for doc in search_docs
+    ])
+
+@tool
+def extract_text_from_image(image_path: str) -> str:
+    try:
+        import pytesseract
+        from PIL import Image
+        image = Image.open(image_path)
+        text = pytesseract.image_to_string(image)
+        return f"Extracted text from image:\n\n{text}"
+    except ImportError:
+        return "Error: pytesseract is not installed."
+    except Exception as e:
+        return f"Error extracting text: {str(e)}"
+
+# Model and agent setup
+API_KEY = os.getenv("OPENAI_API_KEY_AG")
+MODEL_ID = "openai/gpt-4.1-nano"
+
+model = OpenAIServerModel(model_id="gpt-4.1-nano", api_key=API_KEY)
+
+agent = CodeAgent(
+    model=model,
+    tools=[
+        WebSearchTool(),
+        VisitWebpageTool(),
+        WikipediaSearchTool(),
+        arxiv_search,
+        FinalAnswerTool(),
+        extract_text_from_image
+    ],
+    planning_interval=3,
+    max_steps=10,
+    verbosity_level=-1,
+    additional_authorized_imports=[
+        "pandas", "numpy", "requests", "os", "math", "sympy", "scipy",
+        "markdownify", "unicodedata", "stat", "datetime", "random", "itertools",
+        "statistics", "queue", "time", "collections", "re"
+    ],
+    add_base_tools=True
+)
+
+def fetch_questions():
+    DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+    try:
+        response = requests.get(f"{DEFAULT_API_URL}/questions")
+        response.raise_for_status()
+        data = response.json()
+        return data
+    except Exception as e:
+        print(f"Error fetching questions: {e}")
+        return []
+
+def fetch_file(task_id: str, file_name: str):
+    DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+    try:
+        response = requests.get(f"{DEFAULT_API_URL}/files/{task_id}")
+        response.raise_for_status()
+        os.makedirs("data/question_files", exist_ok=True)
+        path = f"data/question_files/{file_name}"
+        with open(path, "wb") as f:
+            f.write(response.content)
+        return path
+    except Exception as e:
+        print(f"Error fetching file: {e}")
+        return None
+
+def run_agent_on_question(q):
+    if q.get("file_name"):
+        file_path = fetch_file(q["task_id"], q["file_name"])
+        prompt = f"""You are a general AI assistant. Use tools and web search as needed.
+Question: {q['question']}
+file_path: {file_path}
+YOUR FINAL ANSWER should be a number OR few words OR comma-separated values. Follow instructions strictly."""
+    else:
+        prompt = f"""You are a general AI assistant. Use tools and web search as needed.
+Question: {q['question']}
+YOUR FINAL ANSWER should be a number OR few words OR comma-separated values. Follow instructions strictly."""
+    
+    output = agent.run(prompt)
+
+    # Optional: Extract final answer if embedded in logs
+    if isinstance(output, str):
+        return output.strip()
+    elif isinstance(output, dict):
+        return output.get("final_answer", str(output))
+    else:
+        return str(output)
+
+def submit_answers(answers):
+    DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+    request_payload = {
+        "username": "GoReed",
+        "agent_code": "test",
+        "answers": answers
+    }
+    try:
+        response = requests.post(
+            f"{DEFAULT_API_URL}/submit",
+            json=request_payload  # ✅ FIXED
+        )
+        response.raise_for_status()
+        print("✅ Submission success:", response.json())
+    except requests.exceptions.HTTPError as http_err:
+        print(f"❌ HTTP Error: {http_err}")
+        print("Response text:", response.text)
+    except Exception as e:
+        print(f"❌ Submission Error: {e}")