Create app.py

app.py

@@ -0,0 +1,32 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Načtení modelu a tokenizeru
+model_name = "m42-health/Llama3-Med42-8B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+st.title('Healthcare Chatbot')
+
+# Uživatelský vstup
+user_input = st.text_input("You:", "")
+if user_input:
+    messages = [
+        {"role": "system", "content": (
+            "You are a helpful, respectful and honest medical assistant. "
+            "Always answer as helpfully as possible, while being safe. "
+            "Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. "
+            "Please ensure that your responses are socially unbiased and positive in nature. "
+            "If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. "
+            "If you don’t know the answer to a question, please don’t share false information."
+        )},
+        {"role": "user", "content": user_input}
+    ]
+    input_text = " ".join([f"{message['role']}: {message['content']}" for message in messages])
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")
+
+    # Vygenerování odpovědi
+    output_ids = model.generate(input_ids, max_length=512, do_sample=True, temperature=0.4, top_k=150, top_p=0.75)
+    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+    st.text_area("Bot:", response[len(input_text):])