Updated to new test-model

app.py

@@ -1,6 +1,5 @@
 import streamlit as st
-#from transformers import pipeline
-from transformers import AutoProcessor, UdopForConditionalGeneration
+from transformers import pipeline
 import docx
 #from datasets import load_dataset
 
@@ -12,12 +11,9 @@ def read_docx(file_path):
         text.append(paragraph.text) 
     return "\n".join(text)
 
-# Load model and processor 
-processor = AutoProcessor.from_pretrained("microsoft/udop-large", apply_ocr=False) 
-model = UdopForConditionalGeneration.from_pretrained("microsoft/udop-large")
-
 #pipe = pipeline("question-answering")
 #pipe = pipeline("text-generation")
+pipe = pipeline("question-answering", model="deepset/roberta-base-squad2")
 
 st.title("Adrega AI Help")
 #dataset = load_dataset("andreska/adregadocs", split="test")
@@ -31,11 +27,7 @@ if st.button("Submit"):
         #text_inputs = f"Context: {context}\nQuestion: {user_input}\nAnswer:"
         #result = pipe(text_inputs, max_length=200, num_return_sequences=1)[0]['generated_text']
         #answer = result.split("Answer:")[1].strip()
-        encoding = processor(context, user_input, return_tensors="pt")
-        
-        #answer = pipe(question=user_input, context=context)
-        predicted_ids = model.generate(**encoding) 
-        answer = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+        answer = pipe(question=user_input, context=context)
         
         st.write(f"Adrega AI: {answer}")
     else: