Update app.py

app.py

@@ -4,181 +4,45 @@ import requests
 import pandas as pd
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-# ---------- Imports for Advanced Agent ----------
-import re
-from langgraph.graph import StateGraph, MessagesState
-from langgraph.prebuilt import tools_condition, ToolNode
-from langchain_core.messages import SystemMessage, HumanMessage
-from langchain_core.tools import tool
-from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
-from langchain_community.tools.tavily_search import TavilySearchResults
-from groq import Groq
-
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# ---------- Tools ----------
-from langchain_core.tools import tool
-from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
-from langchain_community.tools.tavily_search import TavilySearchResults
-
-@tool
-def wiki_search(query: str) -> str:
-    """Search Wikipedia for a given query and return content from up to 2 relevant pages."""
-    docs = WikipediaLoader(query=query, load_max_docs=2).load()
-    return "\n\n".join([doc.page_content for doc in docs])
-
-@tool
-def web_search(query: str) -> str:
-    """Search the web using the Tavily API and return content from up to 3 search results."""
-    docs = TavilySearchResults(max_results=3).invoke(query)
-    return "\n\n".join([doc.page_content for doc in docs])
-
-@tool
-def arvix_search(query: str) -> str:
-    """Search academic papers on Arxiv for a given query and return up to 3 result summaries."""
-    docs = ArxivLoader(query=query, load_max_docs=3).load()
-    return "\n\n".join([doc.page_content[:1000] for doc in docs])
-
-# Tool-based LangGraph builder
-def build_tool_graph(system_prompt):
-    llm = AutoModelForCausalLM.from_pretrained("gpt2")  # Load Hugging Face GPT-2 model
-    tokenizer = AutoTokenizer.from_pretrained("gpt2")
-
-    def assistant(state: MessagesState):
-        input_text = state["messages"][-1]["content"]
-        inputs = tokenizer(input_text, return_tensors="pt")
-        outputs = llm.generate(**inputs)
-        result = tokenizer.decode(outputs[0], skip_special_tokens=True)
-        return {"messages": [{"content": result}]}
-
-    builder = StateGraph(MessagesState)
-    builder.add_node("assistant", assistant)
-    builder.add_node("tools", ToolNode([wiki_search, web_search, arvix_search]))
-    builder.set_entry_point("assistant")
-    builder.set_finish_point("assistant")
-    builder.add_conditional_edges("assistant", tools_condition)
-    builder.add_edge("tools", "assistant")
-    return builder.compile()
-
-
-# --- Advanced BasicAgent Class ---
+# --- Advanced Agent Logic ---
 class BasicAgent:
     def __init__(self):
         print("BasicAgent initialized.")
-        self.client = Groq(api_key=os.environ.get("GROQ_API_KEY", ""))
+        self.client = None  # Placeholder for Groq or another API client
         self.agent_prompt = (
             """You are a general AI assistant. I will ask you a question. Report your thoughts, and
-            finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER].
-            YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated
-            list of numbers and/or strings.
-            If you are asked for a number, don't use comma to write your number neither use units such as $
-            or percent sign unless specified otherwise.
-            If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the
-            digits in plain text unless specified otherwise.
-            If you are asked for a comma separated list, apply the above rules depending of whether the element
-            to be put in the list is a number or a string."""
+            finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]."""
         )
-        self.tool_chain = build_tool_graph(self.agent_prompt)
-
-    def format_final_answer(self, answer: str) -> str:
-        cleaned = " ".join(answer.split())
-        match = re.search(r"FINAL ANSWER:\s*(.*)", cleaned, re.IGNORECASE)
-        return f"FINAL ANSWER: {match.group(1).strip()}" if match else f"FINAL ANSWER: {cleaned}"
-
+        
+        # Assuming some model for queries
+        self.llm = AutoModelForCausalLM.from_pretrained("gpt2")
+        self.tokenizer = AutoTokenizer.from_pretrained("gpt2")
+        
     def query_groq(self, question: str) -> str:
-        full_prompt = f"{self.agent_prompt}\n\nQuestion: {question}"
-        try:
-            response = self.client.chat.completions.create(
-                model="llama3-8b-8192",
-                messages=[{"role": "user", "content": full_prompt}]
-            )
-            answer = response.choices[0].message.content
-            print(f"[Groq Raw Response]: {answer}")
-            return self.format_final_answer(answer).upper()
-        except Exception as e:
-            print(f"[Groq ERROR]: {e}")
-            return self.format_final_answer("GROQ_ERROR")
+        # Placeholder for Groq query handling
+        return f"FINAL ANSWER: {question}"
 
     def query_tools(self, question: str) -> str:
-        try:
-            input_state = {
-                "messages": [
-                    SystemMessage(content=self.agent_prompt),
-                    HumanMessage(content=question)
-                ]
-            }
-            result = self.tool_chain.invoke(input_state)
-            final_msg = result["messages"][-1].content
-            print(f"[LangGraph Final Response]: {final_msg}")
-            return self.format_final_answer(final_msg)
-        except Exception as e:
-            print(f"[LangGraph ERROR]: {e}")
-            return self.format_final_answer("TOOL_ERROR")
+        # Placeholder for using tools
+        return f"FINAL ANSWER: {question}"
 
     def __call__(self, question: str) -> str:
-        print(f"Received question: {question[:50]}...")
-        if "commutative" in question.lower():
-            return self.check_commutativity()
-        if self.maybe_reversed(question):
-            print("Detected likely reversed riddle.")
-            return self.solve_riddle(question)
+        # Decide based on the question type, here we use placeholder logic
         if "use tools" in question.lower():
             return self.query_tools(question)
         return self.query_groq(question)
 
-    def check_commutativity(self):
-        S = ['a', 'b', 'c', 'd', 'e']
-        counter_example_elements = set()
-        index = {'a': 0, 'b': 1, 'c': 2, 'd': 3, 'e': 4}
-        self.operation_table = [
-            ['a', 'b', 'c', 'b', 'd'],
-            ['b', 'c', 'a', 'e', 'c'],
-            ['c', 'a', 'b', 'b', 'a'],
-            ['b', 'e', 'b', 'e', 'd'],
-            ['d', 'b', 'a', 'd', 'c']
-        ]
-        for x in S:
-            for y in S:
-                x_idx = index[x]
-                y_idx = index[y]
-                if self.operation_table[x_idx][y_idx] != self.operation_table[y_idx][x_idx]:
-                    counter_example_elements.add(x)
-                    counter_example_elements.add(y)
-        return self.format_final_answer(", ".join(sorted(counter_example_elements)))
-
-    def maybe_reversed(self, text: str) -> bool:
-        words = text.split()
-        reversed_ratio = sum(
-            1 for word in words if word[::-1].lower() in {
-                "if", "you", "understand", "this", "sentence", "write",
-                "opposite", "of", "the", "word", "left", "answer"
-            }
-        ) / len(words)
-        return reversed_ratio > 0.3
-
-    def solve_riddle(self, question: str) -> str:
-        question = question[::-1]
-        if "opposite of the word" in question:
-            match = re.search(r"opposite of the word ['\"](\w+)['\"]", question)
-            if match:
-                word = match.group(1).lower()
-                opposites = {
-                    "left": "right", "up": "down", "hot": "cold",
-                    "true": "false", "yes": "no", "black": "white"
-                }
-                opposite = opposites.get(word, f"UNKNOWN_OPPOSITE_OF_{word}")
-                return f"FINAL ANSWER: {opposite.upper()}"
-        return self.format_final_answer("COULD_NOT_SOLVE")
-
-# --- Evaluation Logic ---
-def run_and_submit_all(profile, test_mode):
+# --- Evaluation and Submission Logic ---
+def run_and_submit_all(profile):
     space_id = os.getenv("SPACE_ID")
     if profile:
         username = profile
         print(f"User logged in: {username}")
     else:
-        return "Please Login to Hugging Face with the button.", None
+        return "Please provide a username.", None
 
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
@@ -249,19 +113,14 @@ with gr.Blocks() as demo:
         3. Click the button to run evaluation and submit your answers.
         """
     )
-    
-    # Simulate OAuth profile with a textbox for user
-    test_checkbox = gr.Checkbox(label="Enable Test Mode (Skip Submission)", value=False)
+    profile_input = gr.Textbox(label="Enter Username", placeholder="Enter your username", interactive=True)
     run_button = gr.Button("Run Evaluation")
     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-
-    # Simulate OAuth Profile with a mock profile for now
-    mock_oauth_profile = gr.Textbox(label="Simulated OAuth Profile", value="mock_user", interactive=False)
     
     run_button.click(
         fn=run_and_submit_all,
-        inputs=[mock_oauth_profile, test_checkbox],
+        inputs=[profile_input],
         outputs=[status_output, results_table]
     )