Update app.py

app.py

@@ -1,29 +1,31 @@
 import gradio as gr
-from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
+from transformers import RagTokenizer, RagTokenForGeneration
 
-# Load the RAG model and tokenizer from Hugging Face
+# Load tokenizer and model
 tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
-retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="legacy", use_dummy_dataset=True, trust_remote_code=True)
-model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-nq")
+model = RagTokenForGeneration.from_pretrained("facebook/rag-token-nq")
 
-# Function to process the user query
-def rag_query(query):
-    # Tokenize the input query
+def rag_generate(query):
+    # Tokenize the input question
     inputs = tokenizer(query, return_tensors="pt")
 
-    # Retrieve relevant documents using the retriever
-    retrieved_docs = retriever(input_ids=inputs["input_ids"], return_tensors="pt")
+    # Generate output
+    generated_ids = model.generate(**inputs)
 
-    # Generate an answer using the RAG model
-    generated_ids = model.generate(input_ids=inputs["input_ids"], context_input_ids=retrieved_docs["context_input_ids"])
-
-    # Decode the generated answer and return it
-    answer = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+    # Decode the generated response
+    answer = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    
     return answer
 
-# Create a Gradio interface for the app
-iface = gr.Interface(fn=rag_query, inputs="text", outputs="text", 
-                     title="RAG Demo", description="Ask a question and get an answer using Retrieval-Augmented Generation (RAG).")
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 🤖 RAG Token QA with facebook/rag-token-nq")
+    with gr.Row():
+        question = gr.Textbox(label="Ask your question")
+    with gr.Row():
+        answer = gr.Textbox(label="Answer")
+
+    submit_btn = gr.Button("Generate Answer")
+    submit_btn.click(fn=rag_generate, inputs=question, outputs=answer)
 
-# Launch the Gradio interface
-iface.launch()
+demo.launch()