Update app.py

app.py

@@ -1,23 +1,193 @@
 import os
+import re
+import json
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
+from urllib.parse import quote
+from bs4 import BeautifulSoup
+from dotenv import load_dotenv
+
+load_dotenv()
 
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+HF_TOKEN = os.getenv("HF_TOKEN", "")
+REACT_MAX_STEPS = 10
+LLM_MODEL = "Qwen/Qwen2.5-7B-Instruct"
+
+
+# --- Tools (DuckDuckGo search, web page view, code agent) ---
+def tool_web_search(query: str, max_results: int = 5) -> str:
+    """Search the web using DuckDuckGo. Input: search query string."""
+    try:
+        from duckduckgo_search import DDGS
+        results = list(DDGS().text(query, max_results=max_results))
+        if not results:
+            return "No search results found."
+        out = []
+        for i, r in enumerate(results, 1):
+            out.append(f"{i}. {r.get('title', '')}\n   URL: {r.get('href', '')}\n   {r.get('body', '')}")
+        return "\n\n".join(out)
+    except Exception as e:
+        return f"Web search error: {e}"
+
+
+def tool_web_page_view(url: str) -> str:
+    """View the main text content of a web page. Input: full URL string."""
+    try:
+        headers = {"User-Agent": "Mozilla/5.0 (compatible; ReActAgent/1.0)"}
+        r = requests.get(url, timeout=15, headers=headers)
+        r.raise_for_status()
+        soup = BeautifulSoup(r.text, "html.parser")
+        for tag in soup(["script", "style", "nav", "footer", "header"]):
+            tag.decompose()
+        text = soup.get_text(separator="\n", strip=True)
+        return text[:8000] if len(text) > 8000 else text or "No text content found."
+    except Exception as e:
+        return f"Web page view error: {e}"
+
+
+def tool_code_agent(code: str) -> str:
+    """Run Python code to compute an answer. Input: a single Python expression or block (e.g. print(2+2)). No file or network access."""
+    import builtins
+    import io
+    import sys
+    safe_builtins = {
+        "abs": builtins.abs, "all": builtins.all, "any": builtins.any,
+        "bin": builtins.bin, "bool": builtins.bool, "chr": builtins.chr,
+        "dict": builtins.dict, "divmod": builtins.divmod, "enumerate": builtins.enumerate,
+        "filter": builtins.filter, "float": builtins.float, "format": builtins.format,
+        "hash": builtins.hash, "int": builtins.int, "len": builtins.len,
+        "list": builtins.list, "map": builtins.map, "max": builtins.max,
+        "min": builtins.min, "next": builtins.next, "pow": builtins.pow,
+        "print": builtins.print, "range": builtins.range, "repr": builtins.repr,
+        "reversed": builtins.reversed, "round": builtins.round, "set": builtins.set,
+        "sorted": builtins.sorted, "str": builtins.str, "sum": builtins.sum,
+        "tuple": builtins.tuple, "zip": builtins.zip,
+    }
+    try:
+        code = code.strip()
+        if not code.startswith("print(") and "print(" not in code:
+            code = f"print({code})"
+        buf = io.StringIO()
+        old_stdout = sys.stdout
+        sys.stdout = buf
+        try:
+            exec(code, {"__builtins__": safe_builtins, "print": builtins.print}, {})
+        finally:
+            sys.stdout = old_stdout
+        return buf.getvalue().strip() or "Code ran (no printed output)."
+    except Exception as e:
+        return f"Code error: {e}"
+
+
+TOOLS = {
+    "web_search": tool_web_search,
+    "web_page_view": tool_web_page_view,
+    "code_agent": tool_code_agent,
+}
+
+TOOL_DESCRIPTIONS = """Available tools:
+- web_search: search the web with DuckDuckGo. Input: search query (string).
+- web_page_view: get main text from a web page. Input: URL (string).
+- code_agent: run Python code (math, string ops). Input: code (string)."""
+
+
+# --- ReAct Agent: Plan -> Act -> Observe -> Reflect ---
+class ReActAgent:
+    def __init__(self, token: str | None = None, model: str = LLM_MODEL, max_steps: int = REACT_MAX_STEPS):
+        self.token = (token or HF_TOKEN or "").strip()
+        self.model = model
+        self.max_steps = max_steps
+        print("ReActAgent initialized (plan -> act -> observe -> reflect).")
+
+    def _llm(self, messages: list[dict]) -> str:
+        if not self.token:
+            return "Error: HF_TOKEN not set. Add your token in .env to use the LLM."
+        url = f"https://api-inference.huggingface.co/models/{self.model}"
+        headers = {"Authorization": f"Bearer {self.token}", "Content-Type": "application/json"}
+        payload = {"inputs": self._messages_to_prompt(messages), "parameters": {"max_new_tokens": 512, "return_full_text": False}}
+        try:
+            r = requests.post(url, json=payload, headers=headers, timeout=60)
+            r.raise_for_status()
+            data = r.json()
+            if isinstance(data, list) and len(data) > 0:
+                return (data[0].get("generated_text") or "").strip()
+            if isinstance(data, dict) and "generated_text" in data:
+                return (data["generated_text"] or "").strip()
+            return ""
+        except Exception as e:
+            return f"LLM error: {e}"
+
+    def _messages_to_prompt(self, messages: list[dict]) -> str:
+        out = []
+        for m in messages:
+            role = m.get("role", "user")
+            content = m.get("content", "")
+            if role == "system":
+                out.append(f"System: {content}")
+            elif role == "user":
+                out.append(f"User: {content}")
+            else:
+                out.append(f"Assistant: {content}")
+        out.append("Assistant:")
+        return "\n\n".join(out)
+
+    def _parse_action(self, text: str) -> tuple[str | None, str | None, str | None]:
+        """Returns (thought, action, action_input) or (None, None, final_answer)."""
+        text = text.strip()
+        final_match = re.search(r"Final Answer\s*:\s*(.+?)(?=\n\n|\Z)", text, re.DOTALL | re.IGNORECASE)
+        if final_match:
+            return None, None, final_match.group(1).strip()
+        action_match = re.search(r"Action\s*:\s*(\w+)", text, re.IGNORECASE)
+        input_match = re.search(r"Action Input\s*:\s*(.+?)(?=\n\n|\nThought:|\Z)", text, re.DOTALL | re.IGNORECASE)
+        thought = None
+        thought_match = re.search(r"Thought\s*:\s*(.+?)(?=\nAction:|\Z)", text, re.DOTALL | re.IGNORECASE)
+        if thought_match:
+            thought = thought_match.group(1).strip()
+        action = action_match.group(1).strip() if action_match else None
+        action_input = input_match.group(1).strip() if input_match else None
+        if action_input:
+            action_input = action_input.strip().strip('"\'')
+        return thought, action, action_input
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized.")
     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
+        print(f"ReAct agent received question (first 50 chars): {question[:50]}...")
+        if not self.token:
+            return "HF_TOKEN not set. Add your Hugging Face token in .env to run the ReAct agent."
+        system = (
+            "You are a ReAct agent. For each turn you must either:\n"
+            "1. Output: Thought: <reasoning> then Action: <tool_name> then Action Input: <input>\n"
+            "2. Or when you have the answer: Final Answer: <your answer>\n\n"
+            + TOOL_DESCRIPTIONS
+        )
+        messages = [
+            {"role": "system", "content": system},
+            {"role": "user", "content": f"Question: {question}\n\nFirst, plan which tool(s) to use, then take action, then observe, then reflect. Give your final answer when done."},
+        ]
+        for step in range(self.max_steps):
+            response = self._llm(messages)
+            thought, action, action_input = self._parse_action(response)
+            if thought is None and action is None and action_input is not None:
+                return action_input  # Final Answer
+            if not action or action not in TOOLS:
+                messages.append({"role": "assistant", "content": response})
+                messages.append({"role": "user", "content": "You must use one of the tools (Action: tool_name, Action Input: input) or give Final Answer: your answer. Try again."})
+                continue
+            try:
+                observation = TOOLS[action](action_input)
+            except Exception as e:
+                observation = f"Tool error: {e}"
+            observation = (observation[:3000] + "...") if len(observation) > 3000 else observation
+            messages.append({"role": "assistant", "content": response})
+            messages.append({"role": "user", "content": f"Observation: {observation}\n\nReflect: does this answer the question? If yes, reply with Final Answer: <answer>. If not, use another tool (Thought / Action / Action Input)."})
+        last_assistant = next((m["content"] for m in reversed(messages) if m.get("role") == "assistant"), "")
+        final = self._parse_action(last_assistant)
+        if final[2] and final[0] is None and final[1] is None:
+            return final[2]
+        return last_assistant[:500] if last_assistant else "ReAct agent reached max steps without a final answer."
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -40,7 +210,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
 
     # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = BasicAgent()
+        agent = ReActAgent(token=os.getenv("HF_TOKEN"), max_steps=REACT_MAX_STEPS)
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
@@ -146,11 +316,9 @@ with gr.Blocks() as demo:
     gr.Markdown(
         """
         **Instructions:**
-
         1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
         2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
         3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-
         ---
         **Disclaimers:**
         Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).