Upload app (1).py

Overview:
AI Health Assistant is an intelligent, AI-powered health query assistant built using Retrieval-Augmented Generation (RAG) models. This app allows users to ask health-related questions and get answers from a vast corpus of health information. The assistant uses advanced transformer models (such as RAG from Hugging Face) to retrieve relevant documents and generate concise, accurate answers.

How It Works:
The AI Health Assistant uses the RAG model to power its question-answering capabilities. When a user asks a health-related question, the app retrieves relevant documents from a large corpus of health data. It then uses a sequence-to-sequence model to generate the answer based on the retrieved context.

The system combines the benefits of transformers and a retrieval system to provide high-quality answers. It relies onon the Hugging Face transformers library to load pretrained models, including RAG Token NQ for the question-answering process.

Use Cases:
Symptom Checker: Ask the assistant about various health symptoms, such as “What are the symptoms of a heart attack?” or “What are common signs of diabetes?”
Health Information: Ask questions related to medical conditions or treatments, like “How is hypertension treated?” or “What is a stroke?”
General Health Advice: Ask for lifestyle advice such as “How can I improve my diet?” or “What are good exercises for heart health?”
Why It’s Useful:
The AI Health Assistant helps people access reliable health information in real-time. In a world where health misinformation is common, this tool can provide quick, trustworthy answers to common health questions. Whether you’re feeling unwell or curious about health topics, this app can offer valuable insights and explanations, powered by the latest AI research in natural language processing and medical knowledge.

Future Plans:
Expand the health topics covered by the app to include mental health, nutrition, and fitness.
Improve the accuracy of answers by integrating more specific datasets and health knowledge bases.
Add multilingual support to help users worldwide access accurate health information in their native language.

app (1).py

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+
+import streamlit as st
+from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
+
+# 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)
+
+# 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}")