Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,56 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-"""
-# Welcome to Streamlit!
+st.set_page_config(page_title="DocMed Demo", page_icon="🩺")
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+st.title("🩺 DocMed")
+st.subheader("Medical Study Assistant (Educational Use Only)")
 
-In the meantime, below is an example of what you can do with just a few lines of code:
+st.markdown(
+    """
+⚠️ **Disclaimer**  
+DocMed is an educational AI model.  
+It must **NOT** be used for diagnosis, treatment, or clinical decision-making.
 """
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+)
+
+@st.cache_resource
+def load_model():
+    model_id = "jip7e/DocMed"  # 🔴 change if username differs
+
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        device_map="auto"
+    )
+    model.eval()
+    return tokenizer, model
+
+with st.spinner("Loading DocMed model..."):
+    tokenizer, model = load_model()
+
+question = st.text_area(
+    "Ask a medical question (student level):",
+    placeholder="e.g. What is hydronephrosis?"
+)
+
+if st.button("Ask DocMed"):
+    if question.strip() == "":
+        st.warning("Please enter a question.")
+    else:
+        prompt = f"Explain simply for a medical student: {question}"
+
+        inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+        with torch.no_grad():
+            output = model.generate(
+                **inputs,
+                max_new_tokens=120,
+                temperature=0.7,
+                top_p=0.9
+            )
+
+        answer = tokenizer.decode(output[0], skip_special_tokens=True)
+        st.markdown("### 🧠 DocMed says:")
+        st.write(answer)