Update app.py

app.py

@@ -3,54 +3,20 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
-from transformers import pipeline
-#from PIL import Image
-import requests
-import io
-
+from CustomAgent import alfred
 # (Keep Constants as is)
 # --- Constants ---
 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):
-        # 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."
+        print("BasicAgent initialized.")
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        response = alfred.invoke({"messages": [{"role": "user", "content": question}]})
+        return response['messages'][-1].content
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -113,7 +79,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
             print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
-            submitted_answer = agent.run(question_text)
+            submitted_answer = agent(question_text)
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
         except Exception as e: