tools.py

# tools.py

import pandas as pd

from pathlib import Path
import requests
import regex as re
import time
import os
from duckduckgo_search import DDGS
from langchain_core.tools import tool
from langchain_community.document_loaders import WikipediaLoader, ArxivLoader

DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"


# Removed complex safety wrapper - keeping things simple

def _download_file_for_task(task_id: str, ext: str) -> str:
    """
    Helper: attempt to GET the remote file for a given task_id.
    Saves under ./hf_files/{task_id}.{ext}. Returns the local path if successful,
    or an empty string if no file / download failed.
    """
    
    print("reached _download_file_for_task")
    os.makedirs("hf_files", exist_ok=True)
    local_path = os.path.join("hf_files", f"{task_id}.{ext}")
    url = f"{DEFAULT_API_URL}/files/{task_id}"
    
    try:
        resp = requests.get(url, timeout=10)
        if resp.status_code == 200 and resp.content:
            print(f"Downloaded file from {url} to {local_path}")
            with open(local_path, "wb") as f:
                f.write(resp.content)
            return local_path
    except Exception:
        print(f"Error downloading file from {url} to {local_path}")
        pass

    # If we get here, either 404 or download error
    return ""

@tool
def image_tool(task_id: str) -> str:
    """
    Expects: task_id (str) — a valid image task ID.
    Returns: image caption from Hugging Face API or error message.
    """

    import requests, os

    # Try downloading image with one of the allowed extensions
    for ext in ("png", "jpg", "jpeg"):
        file_path = _download_file_for_task(task_id, ext)
        if file_path and os.path.exists(file_path):
            break
    else:
        return f"Error: Image file for task_id '{task_id}' not found."

    # Read the image bytes
    try:
        with open(file_path, "rb") as f:
            image_bytes = f.read()
    except Exception as e:
        return f"Error reading image: {str(e)}"

    # Load HF token
    hf_token = os.getenv("HF_TOKEN")
    if not hf_token:
        return "Error: HF_TOKEN not set in environment."

    # Use a single reliable model
    model = "Salesforce/blip-image-captioning-base"
    headers = {"Authorization": f"Bearer {hf_token}"}

    try:
        response = requests.post(
            f"https://api-inference.huggingface.co/models/{model}",
            headers=headers,
            files={"file": image_bytes},
            timeout=30
        )
    except Exception as e:
        return f"Error calling HuggingFace API: {e}"

    # Parse response
    if response.status_code != 200:
        return f"Error from model ({model}): {response.status_code} - {response.text}"

    try:
        result = response.json()
        if isinstance(result, list) and result:
            caption = result[0].get("generated_text", "").strip()
        elif isinstance(result, dict):
            caption = result.get("generated_text", "").strip()
        else:
            caption = ""
    except Exception as e:
        return f"Error parsing response: {e}"

    if not caption:
        return "No caption generated by model."

    return f"Image Caption:\n{caption}"




@tool
def excel_tool(task_id: str) -> str:
    """
    Downloads <task_id>.xlsx (if any) and returns a stringified list of
    records from the specified sheet.  No fallback to user-supplied tables.
    Expected keys in `task_id`:
        • task_id            – required (used to download the file)

    returns: stringified list of records from the specified sheet
    """
    print("reached excel_tool")
    sheet   = "Sheet1"

    local_xlsx = _download_file_for_task(task_id, "xlsx")
    if not local_xlsx or not os.path.exists(local_xlsx):
        return "Error: Excel file not found for this task."

    try:
        xls = pd.ExcelFile(local_xlsx)
        df  = pd.read_excel(
            xls,
            sheet_name=sheet if sheet and sheet in xls.sheet_names else xls.sheet_names[0]
        )
        print(f"Excel file read successfully: {str(df.to_dict(orient='records'))}")
        return str(df.to_dict(orient="records"))
    except Exception as e:
        return f"Error reading Excel file: {e}"
    

import openai
@tool
def audio_transcriber_tool(task_id: str) -> str:
    """
    LangGraph tool for transcribing audio via OpenAI's Whisper API.
    Expects: task_id is a string
    Returns:
    "<text or error message>"
    Always attempts to download the file for the given path or task ID.
    """
    print("reached audio_transcriber_tool")


    # Always attempt to download the file, regardless of local existence
    local_audio = ""
    for ext in ("mp3", "wav", "m4a"):
        candidate = _download_file_for_task(task_id, ext)
        if candidate:
            local_audio = candidate
            break

    if not local_audio or not os.path.exists(local_audio):
        return "Error: No audio file found (download failed)."
        

    # Send to OpenAI Whisper
    try:
        openai.api_key = os.getenv("OPENAI_API_KEY")
        if not openai.api_key:
            raise RuntimeError("OPENAI_API_KEY is not set in environment.")

        with open(local_audio, "rb") as audio_file:
            print("reached openai.audio.transcriptions.create")
            response = openai.audio.transcriptions.create(
                model="whisper-1", 
                file=audio_file,
            )
            print("reached response")
        text = response.text.strip()
    except Exception as e:
        text = f"Error during transcription: {e}"
    print(f"Transcripted as transcript: {text}")
    return text
# tools.py

import re
import requests

@tool
def wikipedia_search_tool(wiki_query: str) -> str:
    """
    Searches Wikipedia for the given query and returns the first 5 pages.
    Expects: wiki_query is a non‐empty string.
    Returns: text summary of first matching page or an error message>"
      
    If no valid wiki_query is provided, returns {}.
    """
    print(f"DEBUG: reached wikipedia_search_tool with query: {wiki_query}")
    try:
        docs = WikipediaLoader(query=wiki_query, load_max_docs=3).load()  # Reduced from 5 to 3
        print(f"DEBUG: WikipediaLoader returned {len(docs)} documents")
        
        result = ""
        counter = 1
        for doc in docs:
            print(f"DEBUG: Processing Wikipedia document {counter}")
            print(f"DEBUG: Document metadata: {doc.metadata}")
            print(f"DEBUG: Document content length: {len(doc.page_content)}")
            
            # Handle different metadata structures
            title = "Unknown Title"
            if hasattr(doc, 'metadata') and doc.metadata:
                # Try different possible title keys
                if 'title' in doc.metadata:
                    title = doc.metadata['title']
                elif 'Title' in doc.metadata:
                    title = doc.metadata['Title']
                elif 'source' in doc.metadata:
                    title = doc.metadata['source']
                else:
                    # Use first available key as title
                    if doc.metadata:
                        first_key = list(doc.metadata.keys())[0]
                        title = f"Wikipedia: {doc.metadata[first_key]}"
            
            print(f"DEBUG: Using Wikipedia title: {title}")
            
            # Trim content to key information only (reduced from 2000 to 800 characters)
            content = doc.page_content[:800] if len(doc.page_content) > 800 else doc.page_content
            
            # Add document but keep it concise
            result += f"\n\nWikipedia Result {counter}: {title}\nSummary: {content}..."
            counter += 1
            
            # Stop after 2 documents to keep response manageable
            if counter > 2:
                break
        
        if not result.strip():
            return "No Wikipedia results found for the given query. [END_OF_SEARCH]"
        
        # Add clear end marker
        result += "\n\n[END_OF_SEARCH] - Wikipedia search complete. Use this information to answer the question."
        
        print(f"DEBUG: Final Wikipedia result length: {len(result)}")
        return result
        
    except Exception as e:
        error_msg = f"Error during Wikipedia search: {str(e)} [END_OF_SEARCH]"
        print(f"DEBUG: {error_msg}")
        return error_msg
    
@tool
def arxiv_search_tool(arxiv_query: str) -> str:
    """
    Searches Arxiv for the given query and returns the first 5 pages.
    Expects: arxiv_query is a non‐empty string.
    Returns: text summary of first matching page or an error message>"
    """
    print(f"DEBUG: reached arxiv_search_tool with query: {arxiv_query}")
    try:
        docs = ArxivLoader(query=arxiv_query, load_max_docs=3).load()  # Reduced from 5 to 3
        print(f"DEBUG: ArxivLoader returned {len(docs)} documents")
        
        result = ""
        counter = 1
        for doc in docs:
            print(f"DEBUG: Processing document {counter}")
            print(f"DEBUG: Document metadata: {doc.metadata}")
            print(f"DEBUG: Document content length: {len(doc.page_content)}")
            
            # Handle different metadata structures
            title = "Unknown Title"
            if hasattr(doc, 'metadata') and doc.metadata:
                # Try different possible title keys
                if 'title' in doc.metadata:
                    title = doc.metadata['title']
                elif 'Title' in doc.metadata:
                    title = doc.metadata['Title']
                elif 'entry_id' in doc.metadata:
                    title = doc.metadata['entry_id']
                elif 'summary' in doc.metadata:
                    title = f"ArXiv Paper {counter}"
                else:
                    # Use first available key as title
                    if doc.metadata:
                        first_key = list(doc.metadata.keys())[0]
                        title = f"{first_key}: {doc.metadata[first_key]}"
            
            print(f"DEBUG: Using title: {title}")
            
            # Trim content to key information only (reduced from 2000 to 800 characters)
            content = doc.page_content[:800] if len(doc.page_content) > 800 else doc.page_content
            
            # Add document but keep it concise
            result += f"\n\nArXiv Result {counter}: {title}\nAbstract/Summary: {content}..."
            counter += 1
            
            # Stop after 2 documents to keep response manageable
            if counter > 2:
                break
        
        if not result.strip():
            return "No ArXiv results found for the given query. [END_OF_SEARCH]"
        
        # Add clear end marker
        result += "\n\n[END_OF_SEARCH] - ArXiv search complete. Use this information to answer the question."
        
        print(f"DEBUG: Final ArXiv result length: {len(result)}")
        return result
        
    except Exception as e:
        error_msg = f"Error during Arxiv search: {str(e)} [END_OF_SEARCH]"
        print(f"DEBUG: {error_msg}")
        return error_msg


from langchain_openai import ChatOpenAI
from langchain.schema import SystemMessage, HumanMessage
LLM = ChatOpenAI(model_name="gpt-4.1-mini", temperature=0.2)

@tool
def analyze_code_tool(task_id: str) -> str:
    """
    Either task_id  OR  (file + task_id)
    Reads the code (max 400 lines / 10 kB) and asks the LLM for:
        • plain-language summary
        • list of key functions/classes
        • obvious bugs or style smells
    Returns that analysis as a string.
    """
    print("reached analyze_code_tool")
    code_txt = ""
    if not task_id:
        code_txt = "No code provided."
    else:
        path = _download_file_for_task(task_id, "py")
        if not path:
            return "Error: .py file not found for this task."
        code_txt = Path(path).read_text(encoding="utf-8", errors="ignore")
    # else:
    #     return "Error: neither snippet nor file provided."

    # Truncate for safety
    lines = code_txt.splitlines()[:400]
    code_sample = "\n".join(lines)[:10_000]

    prompt = [
        SystemMessage(content="You are a senior Python code reviewer."),
        HumanMessage(content=(
            "Please analyse the following code. "
            "Summarise what it does, list key functions/classes, "
            "and point out any obvious bugs, performance issues or style problems.\n\n"
            f"```python\n{code_sample}\n```"
            "If you can then find the output of the code and return it in the output."
        ))
    ]
    return LLM.invoke(prompt).content.strip()


# def web_search_tool(state: AgentState) -> AgentState:
#     """
#     Expects: state["web_search_query"] is a non‐empty string.
#     Returns: {"web_search_query": None, "web_search_result": <string>}.
#     Retries up to 5 times on either a DuckDuckGo "202 Ratelimit" response or any exception (e.g. timeout).
#     """
#     print("reached web_search_tool")
#     query = state.get("web_search_query", "")
#     if not query:
#         return {}  # nothing to do

#     ddg = DDGS()
#     max_retries = 5
#     result_text = ""

#     for attempt in range(1, max_retries + 1):
#         try:
#             result_text = str(ddg.text(query, max_results=5))
#         except Exception as e:
#             # Network error or timeout—retry up to max_retries
#             if attempt < max_retries:
#                 print(f"web_search_tool: exception '{e}', retrying in 4 seconds ({attempt}/{max_retries})")
#                 time.sleep(4)
#                 continue
#             else:
#                 # Final attempt failed
#                 return {
#                     "web_search_query": None,
#                     "web_search_result": f"Error during DuckDuckGo search: {e}"
#                 }

#         # Check for DuckDuckGo rate‐limit indicator
#         if "202 Ratelimit" in result_text:
#             if attempt < max_retries:
#                 print(f"web_search_tool: received '202 Ratelimit', retrying in 4 seconds ({attempt}/{max_retries})")
#                 time.sleep(4)
#                 continue
#             else:
#                 # Final attempt still rate‐limited
#                 break

#         # Successful response (no exception and no rate‐limit text)
#         break

#     return {
#         "web_search_query": None,
#         "web_search_result": result_text
#     }