Create app.py

app.py

@@ -0,0 +1,37 @@
+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+@st.cache_resource
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+    model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+    return tokenizer, model
+
+def generate_response(user_input, tokenizer, model):
+    inputs = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt")
+    output = model.generate(inputs, max_length=1000, pad_token_id=tokenizer.eos_token_id)
+    response = tokenizer.decode(output[:, inputs.shape[-1]:][0], skip_special_tokens=True)
+    return response
+
+def main():
+    st.title("Medical Chatbot")
+    st.write("This chatbot is designed to assist with general medical inquiries. Please consult a healthcare provider for any serious issues.")
+
+    tokenizer, model = load_model()
+
+    if 'chat_history' not in st.session_state:
+        st.session_state['chat_history'] = []
+
+    user_input = st.text_input("You: ", key="input")
+
+    if st.button("Send"):
+        if user_input:
+            st.session_state['chat_history'].append(f"You: {user_input}")
+            response = generate_response(user_input, tokenizer, model)
+            st.session_state['chat_history'].append(f"Bot: {response}")
+
+    for message in st.session_state['chat_history']:
+        st.write(message)
+
+if __name__ == "__main__":
+    main()