Update app.py

app.py

@@ -7,6 +7,9 @@ from typing import List, Dict, Union
 import requests
 import wikipediaapi
 import pandas as pd
+from transformers import HuggingFaceAgent
+from transformers import pipeline  # or HfAgent if you want the higher-level agent
+
 
 load_dotenv()
 
@@ -16,30 +19,24 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 
 # --- Basic Agent Definition ---
+
 class BasicAgent:
     def __init__(self, model="google/gemma-7b"):
-        self.api_url = f"https://api-inference.huggingface.co/models/{model}"
-        self.headers = {"Authorization": f"Bearer {os.getenv('HF_API_KEY')}"}
+        # Using pipeline for text generation
+        self.agent = pipeline("text-generation", model=model)
         print("BasicAgent initialized.")
 
-        #usage
-        #agent = HuggingFaceAgent("google/gemma-7b")  # Same architecture as Gemini
-        #print(agent.generate("Explain quantum computing"))
-      
-    
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = self.agent.generate(question)
-        print(f"Agent returning answer: {fixed_answer}")
-        return fixed_answer
-
+        answer = self.agent(question, max_length=100)[0]['generated_text']
+        print(f"Agent returning answer (first 50 chars): {answer[:50]}...")
+        return answer
 
-     # to check
     def generate_response(self, prompt: str) -> str:
-        """Get response from Gema"""
+        """Get response from model"""
         try:
-            response = self.model.generate_content(prompt)
-            return response.text
+            response = self.agent(prompt, max_length=100)[0]['generated_text']
+            return response
         except Exception as e:
             return f"Error generating response: {str(e)}"