Update app.py

app.py

@@ -3,6 +3,7 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
+from transformers import pipeline
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -12,46 +13,18 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
     def __init__(self):
-        self.answers = {
-            "vegetables": "acorns, basil, broccoli, celery, lettuce, sweet potatoes, zucchini",
-            "calculus recording": "12, 15, 18, 22, 24",
-            "nasa award number r. g. arendt": "80GSFC21M0002",
-            "vietnamese specimens nedoshivina": "Hanoi",
-            "least number of athletes 1928": "MLT",
-            "taishō tamai pitchers": "Tanaka, Yamamoto",
-            "menu items total sales": "10423.75",
-            "malko competition no longer exists": "Ivan",
-            "ozone highest values": "May",
-            "samantha octopus": "Cuttlefish",
-            "sunlight plants": "Chlorophyll",
-            "da vinci invention": "Helicopter",
-            "fastest land animal": "Cheetah",
-            "math graph": "Parabola",
-            "climate change report": "IPCC AR6",
-            "bonjour": "Hello",
-            "pi value": "3.14159",
-            "deep research": "OpenAI",
-            "timeline art movement": "Impressionism",
-            "attached pdf page number": "3",
-            "chess tournament winner": "Carlsen",
-            "fruit served breakfast ocean liner": "plums",
-            "first name malko recipient": "Ivan",
-            "earthquake deadliest century": "2004",
-            "capital of country that no longer exists": "Belgrade",
-            "painting artist signed bottom left": "Monet",
-            "company logo animal": "Puma",
-            "element in group 2 and period 3": "Magnesium",
-            "chemical formula for table salt": "NaCl",
-            "roman numeral for 2023": "MMXXIII",
-            "temperature scale used in us": "Fahrenheit"
-        }
+        print("Loading Hugging Face model...")
+        self.pipeline = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", max_new_tokens=256)
+        print("Model loaded.")
 
     def __call__(self, question: str) -> str:
-        q = question.lower()
-        for key, answer in self.answers.items():
-            if all(k in q for k in key.split()):
-                return answer
-        return "unknown"
+        print(f"Received question: {question[:60]}...")
+        prompt = f"Answer the question clearly and concisely:\n{question.strip()}\nAnswer:"
+        response = self.pipeline(prompt)[0]["generated_text"]
+        # Strip the prompt from the output
+        answer = response.split("Answer:")[-1].strip().split("\n")[0]
+        print(f"Generated answer: {answer}")
+        return answer