Update app.py

app.py

@@ -7,56 +7,63 @@ from transformers import pipeline
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
 class BasicAgent:
     def __init__(self):
-        print("Loading FLAN-T5 base model...")
+        print("🔄 Loading model: flan-alpaca-base...")
         self.pipeline = pipeline(
             "text2text-generation",
-            model="google/flan-t5-base",
+            model="declare-lab/flan-alpaca-base",
             max_new_tokens=128,
             temperature=0.3
         )
-        print("Model loaded.")
+        print("✅ Model loaded.")
 
     def __call__(self, question: str, task_id: str = None) -> str:
-        # Custom routing logic
-        if "wikipedia" in question.lower() or "how many" in question.lower():
-            return self.search_wikipedia(question)
-        elif "attached Excel" in question.lower():
-            return self.parse_excel(task_id)
-        elif "reverse" in question.lower() or "write the opposite" in question.lower():
-            return self.reverse_sentence(question)
-        else:
-            return self.model_response(question)
+        question_lower = question.lower()
+
+        try:
+            if "wikipedia" in question_lower or "how many" in question_lower:
+                return self.search_wikipedia(question)
+            elif "excel" in question_lower or "attached" in question_lower:
+                return self.parse_excel(task_id)
+            elif "reverse" in question_lower or "write the opposite" in question_lower:
+                return self.reverse_sentence(question)
+            else:
+                return self.model_response(question)
+        except Exception as e:
+            return f"[Tool error: {e}]"
 
     def model_response(self, question: str) -> str:
-        prompt = f"Please answer step by step:\n{question}"
-        output = self.pipeline(prompt)[0]["generated_text"]
-        return output.strip().split("Answer:")[-1].strip()
+        few_shot = (
+            "Question: List just the vegetables from [milk, eggs, carrots, onions, cookies].\n"
+            "Answer: carrots, onions\n\n"
+        )
+        prompt = few_shot + f"Please answer this question step by step:\n{question}"
+        result = self.pipeline(prompt)[0]["generated_text"]
+        return result.strip().split("Answer:")[-1].strip()
 
     def search_wikipedia(self, question: str) -> str:
         import wikipedia
         try:
-            summary = wikipedia.summary(question, sentences=2)
-            return summary
+            return wikipedia.summary(question, sentences=2)
         except Exception as e:
-            return f"Could not find info: {e}"
+            return f"Couldn't find info: {e}"
 
     def parse_excel(self, task_id: str) -> str:
         try:
             file_url = f"https://agents-course-unit4-scoring.hf.space/files/{task_id}"
             df = pd.read_excel(file_url)
-            # Example logic: sum sales for food category
-            food_sales = df[df['category'] == 'food']['sales'].sum()
+            food_sales = df[df["category"].str.lower() == "food"]["sales"].sum()
             return f"${food_sales:.2f}"
         except Exception as e:
-            return f"Excel parsing error: {e}"
+            return f"Excel error: {e}"
 
     def reverse_sentence(self, question: str) -> str:
-        sentence = question.split("write")[0].strip()
-        reversed_text = sentence[::-1]
-        return reversed_text
+        try:
+            sentence = question.split(",")[0].strip()
+            return sentence[::-1]
+        except:
+            return "Could not reverse the sentence."
 
 
 def run_and_submit_all(profile: gr.OAuthProfile | None):