Update agent.py

agent.py

@@ -1,4 +1,5 @@
 import os
+import pandas as pd
 from langgraph.graph import StateGraph, START, MessagesState
 from langgraph.prebuilt import tools_condition, ToolNode
 from langchain_google_genai import ChatGoogleGenerativeAI
@@ -6,11 +7,151 @@ from langchain_core.tools import tool
 from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
 from langchain_community.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
 from langchain_core.messages import SystemMessage, HumanMessage
+import requests
+import tempfile
 
 # Lade Umgebungsvariablen (Google API Key)
 GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
 
 # === Tools definieren ===
+
+GAIA_BASE_URL = "https://agents-course-unit4-scoring.hf.space"
+
+@tool
+def fetch_gaia_file(task_id: str) -> str:
+    """
+    Download the file attached to a GAIA task and return the local file-path.
+
+    Args:
+        task_id: The GAIA task_id (string in the JSON payload).
+
+    Returns:
+        Absolute path to the downloaded temp-file.
+    """
+    try:
+        url = f"{GAIA_BASE_URL}/files/{task_id}"
+        response = requests.get(url, timeout=20)
+        response.raise_for_status()
+
+        # Server liefert den echten Dateinamen im Header – fallback auf "download"
+        filename = (
+            response.headers.get("x-filename") or
+            response.headers.get("content-disposition", "download").split("filename=")[-1].strip('"')
+        )
+        if not filename:
+            filename = f"{task_id}.bin"
+
+        tmp_path = os.path.join(tempfile.gettempdir(), filename)
+        with open(tmp_path, "wb") as f:
+            f.write(response.content)
+
+        return tmp_path
+    except Exception as e:
+        return f"ERROR: could not download file for task {task_id}: {e}"
+
+@tool
+def parse_csv(file_path: str, query: str = "") -> str:
+    """
+    Load a CSV file from `file_path` and optionally run a simple analysis query.
+
+    Args:
+        file_path: absolute path to a CSV file (from fetch_gaia_file)
+        query: optional natural-language instruction, e.g.
+               "sum of column Sales where Category != 'Drinks'"
+
+    Returns:
+        A concise string with the answer OR a preview of the dataframe
+        if no query given.
+    """
+    try:
+        df = pd.read_csv(file_path)
+
+        # Auto-preview if kein query
+        if not query:
+            preview = df.head(5).to_markdown(index=False)
+            return f"CSV loaded. First rows:\n\n{preview}\n\nColumns: {', '.join(df.columns)}"
+
+        # Mini-query-engine (sehr simpel, reicht für Summen / Mittelwerte)
+        query_lc = query.lower()
+        if "sum" in query_lc:
+            # ermitteln, welche Spalte summiert werden soll
+            for col in df.columns:
+                if col.lower() in query_lc:
+                    s = df[col]
+                    if "where" in query_lc:
+                        # naive Filter-Parsing: where <col> != 'Drinks'
+                        cond_part = query_lc.split("where", 1)[1].strip()
+                        # SEHR einfaches  !=  oder ==  Parsing
+                        if "!=" in cond_part:
+                            key, val = [x.strip().strip("'\"") for x in cond_part.split("!=")]
+                            s = df.loc[df[key] != val, col]
+                        elif "==" in cond_part:
+                            key, val = [x.strip().strip("'\"") for x in cond_part.split("==")]
+                            s = df.loc[df[key] == val, col]
+                    return str(round(s.sum(), 2))
+        # Fallback
+        return "Query type not supported by parse_csv."
+    except Exception as e:
+        return f"ERROR parsing CSV: {e}"
+
+@tool
+def parse_excel(file_path: str, query: str = "") -> str:
+    """
+    Identisch zu parse_csv, nur für XLS/XLSX.
+    """
+    try:
+        df = pd.read_excel(file_path)
+
+        if not query:
+            preview = df.head(5).to_markdown(index=False)
+            return f"Excel loaded. First rows:\n\n{preview}\n\nColumns: {', '.join(df.columns)}"
+
+        query_lc = query.lower()
+        if "sum" in query_lc:
+            for col in df.columns:
+                if col.lower() in query_lc:
+                    s = df[col]
+                    if "where" in query_lc:
+                        cond_part = query_lc.split("where", 1)[1].strip()
+                        if "!=" in cond_part:
+                            key, val = [x.strip().strip("'\"") for x in cond_part.split("!=")]
+                            s = df.loc[df[key] != val, col]
+                        elif "==" in cond_part:
+                            key, val = [x.strip().strip("'\"") for x in cond_part.split("==")]
+                            s = df.loc[df[key] == val, col]
+                    return str(round(s.sum(), 2))
+        return "Query type not supported by parse_excel."
+    except Exception as e:
+        return f"ERROR parsing Excel: {e}"
+
+@tool
+def transcribe_audio(file_path: str, language: str = "en") -> str:
+    """
+    Transcribe an audio file (MP3/WAV/etc.) using Faster-Whisper.
+
+    Args:
+        file_path: absolute path to an audio file (from fetch_gaia_file)
+        language: ISO language code, default "en"
+
+    Returns:
+        Full transcription as plain text, or "ERROR …"
+    """
+    try:
+        from faster_whisper import WhisperModel
+
+        # Tiny model reicht für kurze Sprachmemos, ~75 MB
+        model = WhisperModel("tiny", device="cpu", compute_type="int8")
+
+        segments, _ = model.transcribe(file_path, language=language)
+        transcript = " ".join(segment.text.strip() for segment in segments).strip()
+
+        if not transcript:
+            return "ERROR: transcription empty."
+        return transcript
+    except Exception as e:
+        return f"ERROR: audio transcription failed – {e}"
+
+
 @tool
 def multiply(a: int, b: int) -> int:
     """Multiplies two numbers."""
@@ -59,11 +200,51 @@ def web_search(query: str) -> str:
 
 # === System Prompt definieren ===
 system_prompt = SystemMessage(content=(
-    "You are an expert assistant. You MUST answer precisely, factually, and accurately. "
-    "If you do not know the answer, use the available tools such as Wikipedia Search, Arxiv Search, "
-    "or Web Search to find the correct information. "
-    "If a math operation is needed, use the calculation tools. "
-    "Do NOT invent answers. Only return answers you are confident in."
+    system_prompt = SystemMessage(
+    content=(
+        "You are a focused, factual AI agent competing on the GAIA evaluation.\n"
+        "\n"
+        "GENERAL RULES\n"
+        "-------------\n"
+        "1. Always try to answer every question.\n"
+        "2. If you are NOT 100 % certain, prefer using a TOOL.\n"
+        "3. Never invent facts.\n"
+        "\n"
+        "TOOLS\n"
+        "-----\n"
+        "- fetch_gaia_file(task_id): downloads any attachment for the current task.\n"
+        "- parse_csv(file_path, query): analyse CSV files.\n"
+        "- parse_excel(file_path, query): analyse Excel files.\n"
+        "- transcribe_audio(file_path): transcribe MP3 / WAV audio.\n"
+        "- wiki_search(query): query English Wikipedia.\n"
+        "- arxiv_search(query): query arXiv.\n"
+        "- web_search(query): DuckDuckGo web search.\n"
+        "- simple_calculator(operation,a,b): basic maths.\n"
+        "\n"
+        "WHEN TO USE WHICH TOOL\n"
+        "----------------------\n"
+        "・If the prompt or GAIA metadata mentions an *attached* file, FIRST call "
+        "fetch_gaia_file with the given task_id. Then:\n"
+        "   • CSV  → parse_csv\n"
+        "   • XLS/XLSX → parse_excel\n"
+        "   • MP3/WAV → transcribe_audio (language auto-detect is OK)\n"
+        "   • Image → (currently unsupported) answer that image processing is unavailable\n"
+        "・If you need factual data (dates, numbers, names) → wiki_search or web_search.\n"
+        "・If you need a scientific paper → arxiv_search.\n"
+        "・If a numeric operation is required → simple_calculator.\n"
+        "\n"
+        "ERROR HANDLING\n"
+        "--------------\n"
+        "If a tool call returns a string that starts with \"ERROR:\", IMMEDIATELY think of "
+        "an alternative strategy: retry with a different tool or modified parameters. "
+        "Do not repeat the same failing call twice.\n"
+        "\n"
+        "OUTPUT FORMAT\n"
+        "-------------\n"
+        "Follow the exact format asked in the question (e.g. single word, CSV, comma-list). "
+        "Do not add extra commentary.\n"
+    )
+)
 ))
 
 # === LLM definieren ===
@@ -76,12 +257,48 @@ llm = ChatGoogleGenerativeAI(
 )
 
 # === Tools in LLM einbinden ===
-tools = [multiply, add, subtract, divide, modulo, wiki_search, arxiv_search, web_search]
+tools = [
+    fetch_gaia_file,
+    parse_csv,
+    parse_excel,
+    transcribe_audio,
+    wiki_search,
+    arxiv_search,
+    web_search,
+    simple_calculator,
+]
 llm_with_tools = llm.bind_tools(tools)
 
+def safe_llm_invoke(messages):
+    """
+    Ruft LLM einmal auf. Wenn das Ergebnis mit ERROR beginnt,
+    ruft es genau EIN weiteres Mal auf – jetzt weiß das LLM,
+    dass der vorige Tool-Call fehlgeschlagen ist.
+    """
+    max_attempts = 2
+    for attempt in range(max_attempts):
+        result = llm_with_tools.invoke(messages)
+        content = result.content if hasattr(result, "content") else ""
+        if "ERROR:" not in content:
+            return result
+        # Fehler: füge eine System-Korrektur hinzu und versuche erneut
+        messages.append(
+            SystemMessage(
+                content="Previous tool call returned an ERROR. "
+                        "Try a different tool or revise the input."
+            )
+        )
+    # nach max_attempts immer noch Fehler → zurückgeben
+    return result
+
+
 # === Nodes für LangGraph ===
 def assistant(state: MessagesState):
-    return {"messages": [llm_with_tools.invoke(state["messages"])]}
+    """
+    Assistant node mit eingebautem Retry bei Tool-Fehlern.
+    """
+    result_msg = safe_llm_invoke(state["messages"])
+    return {"messages": [result_msg]}
 
 # === LangGraph bauen ===
 builder = StateGraph(MessagesState)