init

app.py

@@ -3,13 +3,35 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
+from langgraph.prebuilt import LLMNode,ToolNode
+from tools import web_search, parse_excel, ocr_image
 
+from typing import TypedDict, Annotated
+
+from langchain.chat_models import ChatOpenAI
+from langgraph.graph import StateGraph, START, END
+from langgraph.graph.message import add_messages
+
+# Create a ToolNode that knows about your web_search function
+search_node = ToolNode([web_search])
+
+excel_tool_node = ToolNode([parse_excel])
+
+image_tool_node = ToolNode([ocr_image])
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+
+
+
+
+
+
+
+
 class BasicAgent:
     def __init__(self):
         print("BasicAgent initialized.")
@@ -19,6 +41,11 @@ class BasicAgent:
         print(f"Agent returning fixed answer: {fixed_answer}")
         return fixed_answer
 
+
+
+
+
+
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,

requirements.txt

@@ -1,2 +1,4 @@
 gradio
-requests
+requests
+pillow
+pytesseract

tools.py

@@ -0,0 +1,68 @@
+from langchain_core.tools import tool
+from langchain.utilities import DuckDuckGoSearchRun
+import pandas as pd
+@tool
+def web_search(query: str) -> str:
+    ddg = DuckDuckGoSearchRun()
+    return ddg.run(query)
+
+
+@tool
+def parse_excel(path: str, sheet_name: str = None) -> str:
+
+    """
+    Read in an Excel file at `path`, optionally select a sheet by name (or default to the first sheet),
+    then convert the DataFrame to a JSON-like string. Return that text so the LLM can reason over it.
+    
+    Example return value (collapsed): 
+      "[{'Name': 'Alice', 'Score': 95}, {'Name': 'Bob', 'Score': 88}, ...]"
+    """
+    # 1. Load the Excel workbook
+    try:
+        xls = pd.ExcelFile(path)
+    except FileNotFoundError:
+        return f"Error: could not find file at {path}."
+    
+    # 2. Choose the sheet
+    if sheet_name and sheet_name in xls.sheet_names:
+        df = pd.read_excel(xls, sheet_name=sheet_name)
+    else:
+        # default to first sheet
+        df = pd.read_excel(xls, sheet_name=xls.sheet_names[0])
+    
+    # 3. Option A: convert to JSON
+    records = df.to_dict(orient="records")
+    return str(records)  
+
+
+
+# tools.py
+
+from pathlib import Path
+from PIL import Image
+import pytesseract
+
+
+@tool
+def ocr_image(path: str) -> str:
+    """
+    Run OCR on the image at `path` and return the extracted text.
+    - Expects that Tesseract is installed on the host machine.
+    - If the file is missing or unreadable, returns an error string.
+    """
+    file = Path(path)
+    if not file.exists():
+        return f"Error: could not find image at {path}"
+    try:
+        # Open image via PIL
+        img = Image.open(file)
+    except Exception as e:
+        return f"Error: could not open image: {e}"
+
+    try:
+        # Run pytesseract OCR
+        text = pytesseract.image_to_string(img)
+    except Exception as e:
+        return f"Error: OCR failed: {e}"
+
+    return text.strip() or "(no visible text detected)"