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app (1).py

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+
+import gradio as gr
+from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration
+
+tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-base")
+retriever = RagRetriever.from_pretrained("facebook/rag-token-base", index_name="exact", use_dummy_dataset=True)
+model = RagTokenForGeneration.from_pretrained("facebook/rag-token-base", retriever=retriever)
+
+def ask_medication(question):
+    input_ids = tokenizer(question, return_tensors="pt").input_ids
+    outputs = model.generate(input_ids)
+    return tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+
+demo = gr.Interface(fn=ask_medication, inputs="text", outputs="text", title="💊 Medication Assistant", description="Ask about common medications and their uses!")
+demo.launch()

requirements (1).txt

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+
+transformers
+torch
+gradio
+datasets
+faiss-cpu