Update agent.py

agent.py

@@ -1,76 +1,37 @@
 import os
-import requests
 import base64
-from langchain_openai import ChatOpenAI
-from langchain_community.tools import DuckDuckGoSearchRun
-from langchain.agents import initialize_agent, Tool
-from langchain.agents.agent_types import AgentType
-from langchain.memory import ConversationBufferMemory
-from langchain_core.messages import HumanMessage
+import requests
+from openai import OpenAI
+from duckduckgo_search import DDGS
 
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+# You can expand with image/audio/etc tools as needed
 
 class BasicAgent:
     def __init__(self):
+        self.llm = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
         print("BasicAgent initialized.")
-        # Use DuckDuckGo Search only
-        tools = [
-            Tool(
-                name="DuckDuckGo Search",
-                func=DuckDuckGoSearchRun().run,
-                description="Use this tool to find factual information or recent events."
-            ),
-            Tool(
-                name="Image Analyzer",
-                func=self.describe_image,
-                description="Analyzes and describes what's in an image. Input is an image path."
-            )
-        ]
 
-        memory = ConversationBufferMemory(memory_key="chat_history")
-        self.model = ChatOpenAI(model="gpt-4.1-mini", temperature=0)
-
-        self.agent = initialize_agent(
-            tools=tools,
-            llm=self.model,
-            agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
-            verbose=True,
-            memory=memory
-        )
-        
-    def describe_image(self, img_path: str) -> str:
-        all_text = ""
+    def web_search(self, query: str, max_results: int = 5) -> str:
+        """Search the web using DuckDuckGo for current information."""
         try:
-            r = requests.get(img_path, timeout=10)
-            image_bytes = r.content
-            image_base64 = base64.b64encode(image_bytes).decode("utf-8")
-            message = [
-                HumanMessage(
-                    content=[
-                        {
-                            "type": "text",
-                            "text": (
-                                "You're a chess assistant. Answer only with the best move in algebraic notation (e.g., Qd1#)."
-                            ),
-                        },
-                        {
-                            "type": "image_url",
-                            "image_url": {
-                                "url": f"data:image/png;base64,{image_base64}"
-                            },
-                        },
-                    ]
-                )
-            ]
-            response = self.model.invoke(message)
-            all_text += response.content + "\n\n"
-            return all_text.strip()
+            with DDGS() as ddgs:
+                results = list(ddgs.text(query, max_results=max_results))
+            if not results:
+                return f"No results found for query: {query}"
+            formatted_results = f"Web search results for '{query}':\n\n"
+            for i, result in enumerate(results, 1):
+                title = result.get('title', 'No title')
+                body = result.get('body', 'No description')
+                href = result.get('href', 'No URL')
+                formatted_results += f"{i}. {title}\n"
+                formatted_results += f"   URL: {href}\n"
+                formatted_results += f"   Description: {body}\n\n"
+            return formatted_results
         except Exception as e:
-            error_msg = f"Error extracting text: {str(e)}"
-            print(error_msg)
-            return ""
+            return f"Error performing web search: {str(e)}"
 
     def fetch_file(self, task_id):
+        DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
         try:
             url = f"{DEFAULT_API_URL}/files/{task_id}"
             r = requests.get(url, timeout=10)
@@ -78,13 +39,45 @@ class BasicAgent:
             return url, r.content, r.headers.get("Content-Type", "")
         except:
             return None, None, None
-        
-    def __call__(self, question: str, task_id: str) -> str:
-        print(f"Agent received question (first 50 chars) {task_id}: {question[:50]}...")
-        file_url, file_content, file_type = self.fetch_file(task_id)
-        print(f"Fetched file {file_type}")
-        if file_url is not None:
-            question = f"{question} This task has assigned file with URL: {file_url}"
-        fixed_answer = self.agent.run(question)
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+
+    def describe_image(self, img_path: str) -> str:
+        # This is optional: image-based chess move recognition
+        try:
+            r = requests.get(img_path, timeout=10)
+            image_bytes = r.content
+            image_base64 = base64.b64encode(image_bytes).decode("utf-8")
+            prompt = (
+                "You're a chess assistant. Answer only with the best move in algebraic notation (e.g., Qd1#)."
+            )
+            # In this template, you'd need your LLM to accept vision input.
+            # If not, just return an error or a stub.
+            return "[Image analysis not implemented in this agent.]"
+        except Exception as e:
+            return f"Error extracting text: {str(e)}"
+
+    def __call__(self, question: str, task_id: str = None) -> str:
+        # Step 1: Always web search and build prompt
+        search_snippet = self.web_search(question)
+        # Step 2: Compose the mandated prompt exactly as specified
+        full_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.\n\n"
+            f"Here are web search results and the question:\n{search_snippet}\n\nQuestion: {question}"
+        )
+
+        # Step 3: Get answer from OpenAI, temperature 0 for max determinism
+        response = self.llm.chat.completions.create(
+            model="gpt-4o",
+            messages=[
+                {"role": "system", "content": full_prompt},
+            ],
+            temperature=0.0,
+            max_tokens=512,
+        )
+        answer = response.choices[0].message.content.strip()
+        # You may want to extract just the "FINAL ANSWER: ..." line, or you can return as is
+        return answer