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app.py

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+import streamlit as st
+import torch
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+
+
+# Load model and tokenizer
+model_path = "model/SmallDisMedLM.pt"
+model = torch.load(model_path, map_location='cpu')
+tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
+
+# Define UI elements with clear labels and spacing
+st.title("Medical Chatbot 🩺")
+
+st.header("Ask a question about your health:")
+user_input = st.text_input("", placeholder="Type your query here...")
+
+if st.button("Get Symptoms for a disease"):
+    with st.spinner("Generating response..."):
+        input_ids = tokenizer.encode(user_input, return_tensors='pt')
+        output = model.generate(
+            input_ids,
+            max_length=100,
+            num_return_sequences=1,
+            do_sample=True,
+            top_k=8,
+            top_p=0.95,
+            temperature=0.5,
+            repetition_penalty=1.2
+        )
+        decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)
+
+    st.success("Chatbot Response:")
+    st.write(decoded_output)
+
+st.markdown("---")
+st.info("Disclaimer: This chatbot is not a substitute for professional medical advice. Always consult with a healthcare provider for diagnosis and treatment.")
+
+st.sidebar.header("About")
+st.sidebar.write("This chatbot is powered by a GPT-2 language model trained on medical text data. It aims to provide general information and advice, but it should not be relied upon for diagnosis or treatment.")
+
+st.sidebar.header("Feedback")
+st.sidebar.text_area("Please share any feedback or suggestions:")

model/SmallDisMedLM.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:61c4d5c83e519c284a27ab2dd397691e8021e33db4be20011de9f6d37a201d7e
+size 333994102

requirements.txt

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+torch
+transformers
+sentencepiece
+pandas
+tqdm
+datasets
+streamlit