Update app.py

app.py

@@ -13,55 +13,68 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 import os
 from huggingface_hub import InferenceClient
+from langchain.tools import Tool
+from langchain_community.utilities import DuckDuckGoSearchRun
+from langchain.prompts import PromptTemplate
+import re
 
 class BasicAgent:
     def __init__(self):
-        print("BasicAgent initialized with Llama 3.1-8B Instruct.")
-        # Make sure HF_TOKEN is set in your Space secrets
         self.client = InferenceClient(
-            model="meta-llama/Llama-3.1-8B-Instruct",
+            model="meta-llama/Meta-Llama-3.1-70B-Instruct",
             token=os.getenv("HF_TOKEN")
         )
+        # Define tools
+        self.search = DuckDuckGoSearchRun()  # Web search tool
+        self.tools = {
+            "web_search": Tool(
+                name="web_search",
+                func=self.search.run,
+                description="Search the web for current or historical facts. Input: query string."
+            )
+        }
+        self.tool_names = list(self.tools.keys())
 
     def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        # ReAct prompt: Guides model to think, act, observe
+        react_prompt = PromptTemplate.from_template("""
+You are an AI agent solving GAIA questions. Answer exactly, no extra text.
 
-        # GAIA prompt optimized for exact match
-        prompt = f"""
-Answer the question **with only the exact answer**.
 Rules:
-- Only write the answer.
-- No extra words, no explanations.
-- No punctuation unless required.
-- Do not write 'Final Answer'.
-
-Question:
-{question}
-
-Answer:
-"""
-
-        try:
-            # Use chat-completion API
-            response = self.client.chat.completions.create(
-                model="meta-llama/Llama-3.1-8B-Instruct",
-                messages=[{"role": "user", "content": prompt}],
-                max_tokens=32,       # Level 1 questions are short
-                temperature=0.0,     # deterministic output
-            )
-
-            # Take first line only and strip extra spaces
-            answer = response.choices[0].message.content.strip().split("\n")[0].strip()
-
-            # Optional: normalize case/punctuation for GAIA exact match
-            answer = answer.replace(".", "").replace(",", "").strip()
-
-        except Exception as e:
-            print(f"Error generating answer: {e}")
-            answer = "AGENT ERROR"
-
-        print(f"Agent answer: {answer}")
-        return answer
+- Think step-by-step: Thought, Action (if needed), Observation, then repeat until Final Answer.
+- Use tools only if necessary: Available tools: {tool_names}.
+- For Action: Output 'Action: tool_name [input]'.
+- Stop with 'Final Answer: <exact answer>'.
+- Be precise, no punctuation unless needed.
+
+Question: {question}
+
+Thought:
+""")
+        prompt = react_prompt.format(question=question, tool_names=", ".join(self.tool_names))
+        
+        max_steps = 5  # Limit for Level 1
+        response = ""
+        for _ in range(max_steps):
+            # Generate next step
+            gen = self.client.text_generation(prompt + response, max_new_tokens=200, temperature=0.1)
+            response += gen
+            
+            # Parse Action if present
+            action_match = re.search(r"Action: (\w+) \[(.*)\]", response)
+            if action_match:
+                tool_name, tool_input = action_match.groups()
+                if tool_name in self.tools:
+                    obs = self.tools[tool_name].func(tool_input)
+                    response += f"\nObservation: {obs[:1000]}...\nThought: "  # Truncate long obs
+                else:
+                    response += "\nObservation: Invalid tool.\nThought: "
+            # Check for Final Answer
+            if "Final Answer:" in response:
+                answer = response.split("Final Answer:")[-1].strip().split("\n")[0].strip()
+                return answer.replace(".", "").replace(",", "").strip()  # Normalize as in your code
+        
+        return "Unable to answer"  # Fallback