Create app.py

app.py

@@ -0,0 +1,62 @@
+import streamlit as st
+from transformers import pipeline
+
+# Load the model and tokenizer from the models/ directory
+qa_pipeline = pipeline(
+    "question-answering",
+    model="models/qa_arabic_model_final",
+    tokenizer="models/qa_arabic_model_final"
+)
+
+# Default settings
+default_settings = {
+    "max_new_tokens": 512,
+    "temperature": 0.7,
+    "top_p": 0.9,
+    "min_p": 0,
+    "top_k": 0,
+    "repetition_penalty": 1.0,
+    "presence_penalty": 0,
+    "frequency_penalty": 0,
+    "max_answer_len": 50,
+    "doc_stride": 128,
+}
+
+# Streamlit UI
+st.title("Arabic AI Question Answering")
+st.subheader("Provide context and ask a question to get answers.")
+
+# Input fields
+context = st.text_area("Context", placeholder="Enter the context here...", height=200)
+question = st.text_input("Question", placeholder="Enter your question here...")
+
+# Settings sliders
+st.subheader("Settings")
+max_new_tokens = st.number_input("Max New Tokens", min_value=1, max_value=1000000, value=512)
+temperature = st.slider("Temperature", min_value=0.0, max_value=1.0, value=0.7, step=0.1)
+top_p = st.slider("Top P", min_value=0.0, max_value=1.0, value=0.9, step=0.1)
+min_p = st.slider("Min P", min_value=0.0, max_value=1.0, value=0.0, step=0.1)
+top_k = st.number_input("Top K", min_value=0, max_value=1000, value=0)
+repetition_penalty = st.slider("Repetition Penalty", min_value=0.01, max_value=5.0, value=1.0, step=0.1)
+presence_penalty = st.slider("Presence Penalty", min_value=-2.0, max_value=2.0, value=0.0, step=0.1)
+frequency_penalty = st.slider("Frequency Penalty", min_value=-2.0, max_value=2.0, value=0.0, step=0.1)
+max_answer_len = st.number_input("Max Answer Length", min_value=1, value=50)
+doc_stride = st.number_input("Document Stride", min_value=1, value=128)
+
+# Generate Answer button
+if st.button("Get Answer"):
+    if not context or not question:
+        st.error("Both context and question fields are required.")
+    else:
+        # Generate answer
+        try:
+            prediction = qa_pipeline(
+                {"context": context, "question": question},
+                max_answer_len=max_answer_len,
+                doc_stride=doc_stride
+            )
+            st.subheader("Result")
+            st.write(f"**Question:** {question}")
+            st.write(f"**Answer:** {prediction['answer']}")
+        except Exception as e:
+            st.error(f"Error: {e}")