Update app.py

app.py

@@ -10,14 +10,43 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+#class BasicAgent:
+#    def __init__(self):
+#        print("BasicAgent initialized.")
+#    def __call__(self, question: str) -> str:
+#        print(f"Agent received question (first 50 chars): {question[:50]}...")
+#        fixed_answer = "This is a default answer."
+#        print(f"Agent returning fixed answer: {fixed_answer}")
+#        return fixed_answer
+
 class BasicAgent:
     def __init__(self):
-        print("BasicAgent initialized.")
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+        # Text model (can be improved to Mistral/Zephyr if used in API mode)
+        self.text_model = pipeline("text2text-generation", model="google/flan-t5-large")
+        # Image captioning model for questions with files
+        self.image_model = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
+
+    def run(self, question: str, context: dict = None) -> str:
+        """
+        :param question: A text question
+        :param context: Optional dict containing 'file' (BytesIO) if present
+        """
+        try:
+            prompt = f"Answer the following question concisely:\n{question}"
+            
+            # If file provided (e.g., image), extract caption and append to prompt
+            #if context and "file" in context:
+            #    image_bytes = context["file"]
+            #    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+            #    image_caption = self.image_model(image)[0]["generated_text"]
+            #    prompt += f"\n\nThis image may help:\n{image_caption}"
+
+            result = self.text_model(prompt, max_new_tokens=100, temperature=0.3)[0]["generated_text"]
+            return result.strip()
+
+        except Exception as e:
+            print(f"[ERROR]: {e}")
+            return "I'm unable to answer this question."
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """