Create app.py

app.py

@@ -0,0 +1,51 @@
+import streamlit as st
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+
+# Set Streamlit page config
+st.set_page_config(page_title="ChatDoctor", page_icon="🩺")
+
+# Title
+st.title("🩺 ChatDoctor - Medical Assistant")
+
+# Load model and tokenizer
+@st.cache_resource
+def load_model():
+    model = AutoModelForCausalLM.from_pretrained("abhiyanta/chatDoctor").to("cpu")
+    tokenizer = AutoTokenizer.from_pretrained("abhiyanta/chatDoctor")
+    return model, tokenizer
+
+model, tokenizer = load_model()
+
+# Alpaca-style prompt template
+alpaca_prompt = "### Instruction:\n{0}\n\n### Input:\n{1}\n\n### Output:\n{2}"
+
+# Text input for the user
+user_input = st.text_input("Ask your medical question:")
+
+# Button to trigger response
+if st.button("Ask ChatDoctor"):
+    if user_input:
+        # Format the prompt
+        formatted_prompt = alpaca_prompt.format(
+            user_input,
+            "",
+            ""
+        )
+
+        # Tokenize and move to CPU
+        inputs = tokenizer([formatted_prompt], return_tensors="pt").to("cpu")
+
+        # Stream the generated output
+        st.write("**ChatDoctor:**")
+        text_streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+        
+        with st.spinner('Generating response...'):
+            generated_ids = model.generate(**inputs, streamer=text_streamer, max_new_tokens=1000)
+
+    else:
+        st.warning("Please enter a question to ask ChatDoctor.")
+
+# Footer
+st.markdown("---")
+st.caption("Powered by Hugging Face 🤗 and bitsandbytes ⚡")