Update app.py

app.py

@@ -0,0 +1,53 @@
+import streamlit as st
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer
+import torch
+
+@st.cache_resource
+def load_model():
+    model_path = "YasirAbdali/qoura_roberta"  # Replace with your actual model path
+    model = AutoModelForQuestionAnswering.from_pretrained(model_path)
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+    return model, tokenizer
+
+def answer_question(question, model, tokenizer):
+    inputs = tokenizer(question, return_tensors="pt", max_length=512, truncation=True, padding="max_length")
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    start_logits = outputs.start_logits
+    end_logits = outputs.end_logits
+    
+    start_index = torch.argmax(start_logits)
+    end_index = torch.argmax(end_logits)
+    
+    answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][start_index:end_index+1]))
+    
+    return answer
+
+st.title("Quora Question Answering")
+
+model, tokenizer = load_model()
+
+st.write("Enter a question, and the model will provide an answer based on its knowledge.")
+
+question = st.text_input("Question")
+
+if st.button("Get Answer"):
+    if question:
+        answer = answer_question(question, model, tokenizer)
+        st.write("Answer:", answer)
+    else:
+        st.write("Please provide a question.")
+
+# Optional: Add some example questions
+st.sidebar.header("Example Questions")
+example_questions = [
+    "What is the capital of France?",
+    "Who wrote 'Romeo and Juliet'?",
+    "What is the boiling point of water?",
+    "What year did World War II end?",
+]
+for example in example_questions:
+    if st.sidebar.button(example):
+        st.text_input("Question", value=example)