Create app.py

app.py

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+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the model and tokenizer
+@st.cache_resource
+def load_model_and_tokenizer():
+    model_name_or_path = "m42-health/med42-70b"
+    model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto")
+    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
+    return model, tokenizer
+
+# Function to generate the response
+@st.cache_data
+def generate_response(prompt):
+    prompt_template = f'''
+    <|system|>: You are a helpful medical assistant created by M42 Health in the UAE.
+    <|prompter|>:{prompt}
+    <|assistant|>:
+    '''
+    input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
+    output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id, max_new_tokens=512)
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    return response
+
+# Streamlit app
+def main():
+    st.title("Med42 - Clinical Large Language Model")
+    model, tokenizer = load_model_and_tokenizer()
+
+    prompt = st.text_area("Enter your medical query:")
+    if st.button("Submit"):
+        with st.spinner("Generating response..."):
+            response = generate_response(prompt)
+            st.write(response)
+
+if __name__ == "__main__":
+    main()