Create app.py

app.py

@@ -1,9 +1,23 @@
-import streamlit as st 
-from transformers import pipeline 
+import streamlit as st
+from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
 
-pipe = pieline('sentiment-analysis')
-text = st.text_area('enter some text!')
+# Initialize the model
+tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
+model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-nq")
+retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", use_dummy_dataset=True)
 
-if text:
-    out =pipe(text)
-    st.json(out)
+# Streamlit UI
+st.title("AI Health Assistant (RAG-based)")
+
+def get_answer_rag(question):
+    inputs = tokenizer(question, return_tensors="pt")
+    retrieved_docs = retriever.retrieve(inputs['input_ids'], top_k=3)
+    outputs = model.generate(input_ids=inputs['input_ids'], context_input_ids=retrieved_docs['input_ids'])
+    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return answer
+
+# Ask the user for a health-related question
+question = st.text_input("Ask a health-related question:")
+if question:
+    answer = get_answer_rag(question)
+    st.write(f"Answer: {answer}")