Update app.py

Add tools to perform the tasks

app.py

@@ -3,21 +3,166 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
+import re
+import math
+import json
+import unicodedata
+from langgraph.prebuilt import create_react_agent
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
+from langchain_core.tools import tool
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain_community.utilities import WikipediaAPIWrapper
 
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
+@tool
+def web_search(query: str) -> str:
+    """Search the web using DuckDuckGo. Use for current events, facts, and general knowledge."""
+    try:
+        search = DuckDuckGoSearchRun()
+        return search.run(query)
+    except Exception as e:
+        return f"Search error: {e}"
+ 
+ 
+@tool
+def wikipedia_search(query: str) -> str:
+    """Search Wikipedia for encyclopedic knowledge, historical facts, biographies, science."""
+    try:
+        wiki = WikipediaAPIWrapper(top_k_results=2, doc_content_chars_max=3000)
+        return wiki.run(query)
+    except Exception as e:
+        return f"Wikipedia error: {e}"
+ 
+ 
+@tool
+def python_repl(code: str) -> str:
+    """
+    Execute Python code for math calculations, data processing, logic.
+    Always print() the final result.
+    Example: print(2 + 2)
+    """
+    import io, sys
+    old_stdout = sys.stdout
+    sys.stdout = io.StringIO()
+    try:
+        exec(code, {
+            "math": math, "json": json, "re": re,
+            "unicodedata": unicodedata, "__builtins__": __builtins__
+        })
+        output = sys.stdout.getvalue()
+        return output.strip() if output.strip() else "Code executed with no output. Use print()."
+    except Exception as e:
+        return f"Code error: {e}"
+    finally:
+        sys.stdout = old_stdout
+ 
+ 
+@tool
+def calculator(expression: str) -> str:
+    """
+    Evaluate a simple math expression.
+    Examples: '2 + 2', '100 * 1.07 ** 5', 'math.sqrt(144)'
+    """
+    try:
+        result = eval(expression, {"math": math, "__builtins__": {}})
+        return str(result)
+    except Exception as e:
+        return f"Calculation error: {e}"
+ 
+ 
+@tool
+def get_task_file(task_id: str) -> str:
+    """
+    Fetch the file associated with a GAIA task by its task_id.
+    Use this when the question mentions an attached file or document.
+    """
+    try:
+        url = f"https://agents-course-unit4-scoring.hf.space/files/{task_id}"
+        response = requests.get(url, timeout=15)
+        if response.status_code == 200:
+            content_type = response.headers.get("Content-Type", "")
+            if "text" in content_type or "json" in content_type:
+                return response.text[:5000]
+            elif "image" in content_type:
+                return f"[Image file - content-type: {content_type}]"
+            elif "audio" in content_type:
+                return f"[Audio file - content-type: {content_type}]"
+            else:
+                return f"[File attached: {content_type}]"
+        return f"No file found for task {task_id}"
+    except Exception as e:
+        return f"Error fetching task file: {e}"
+
+
+SYSTEM_PROMPT = """You are a precise expert AI solving GAIA benchmark questions.
+ 
+## Answer Format (CRITICAL)
+- Give ONLY the bare answer: a number, word, name, date, or short phrase.
+- NO explanations, NO punctuation at the end, NO "The answer is...".
+- Correct examples: `42`, `Marie Curie`, `Paris`, `1969`, `blue`
+- For lists: `item1, item2, item3`
+ 
+## Strategy
+1. Read carefully — identify exactly what is asked.
+2. Use tools to find and verify the answer.
+3. Double-check calculations with calculator or python_repl.
+4. If the question mentions a file or attachment, use get_task_file.
+ 
+## Final Answer
+Always end with:
+FINAL ANSWER: <your answer here>
+"""
+
+
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
     def __init__(self):
-        print("BasicAgent initialized.")
+        print("Initializing LangGraph ReAct Agent with Llama 3.3 70B...")
+ 
+        llm_endpoint = HuggingFaceEndpoint(
+            repo_id="meta-llama/Llama-3.3-70B-Instruct",
+            huggingfacehub_api_token=os.getenv("HF_TOKEN"),
+            task="text-generation",
+            max_new_tokens=1024,
+            temperature=0.1,
+            do_sample=False,
+        )
+        llm = ChatHuggingFace(llm=llm_endpoint)
+ 
+        tools = [
+            web_search,
+            wikipedia_search,
+            python_repl,
+            calculator,
+            get_task_file,
+        ]
+ 
+        self.agent = create_react_agent(
+            model=llm,
+            tools=tools,
+            state_modifier=SYSTEM_PROMPT,
+        )
+        print("Agent ready.")
+ 
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+        try:
+            result = self.agent.invoke({"messages": [("user", question)]})
+            last_message = result["messages"][-1].content
+ 
+            # Estrai FINAL ANSWER se presente
+            match = re.search(r"FINAL ANSWER:\s*(.+?)(?:\n|$)", last_message, re.IGNORECASE)
+            answer = match.group(1).strip() if match else last_message.strip().split("\n")[-1]
+ 
+            print(f"Agent returning answer: {answer}")
+            return answer
+        except Exception as e:
+            print(f"Agent error: {e}")
+            return f"AGENT ERROR: {e}"
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """