Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,82 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import streamlit.components.v1 as components
 
-"""
-# Welcome to Streamlit!
-
-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).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-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])),
-    ))
+
+# Set Streamlit page config for a clinical look
+st.set_page_config(
+    page_title="Symptom2Disease Clinical AI",
+    page_icon="🩺",
+    layout="centered"
+)
+
+# Clinical-themed header
+st.title("🩺 Symptom2Disease Clinical AI")
+st.markdown("""
+Welcome to the **Symptom2Disease** clinical assistant.  
+Enter your symptoms below and our AI will suggest possible diseases.
+""")
+
+# Load model and tokenizer
+@st.cache_resource
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("alibidaran/Symptom2disease")
+    model = AutoModelForSequenceClassification.from_pretrained("alibidaran/Symptom2disease")
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+label_2id = {
+    'Psoriasis': 0, 'Varicose Veins': 1, 'Typhoid': 2, 'Chicken pox': 3, 'Impetigo': 4,
+    'Dengue': 5, 'Fungal infection': 6, 'Common Cold': 7, 'Pneumonia': 8, 'Dimorphic Hemorrhoids': 9,
+    'Arthritis': 10, 'Acne': 11, 'Bronchial Asthma': 12, 'Hypertension': 13, 'Migraine': 14,
+    'Cervical spondylosis': 15, 'Jaundice': 16, 'Malaria': 17, 'urinary tract infection': 18,
+    'allergy': 19, 'gastroesophageal reflux disease': 20, 'drug reaction': 21, 'peptic ulcer disease': 22,
+    'diabetes': 23
+}
+id2label = {i: v for v, i in label_2id.items()}
+
+def detect_symptom(symptoms):
+    inputs_id = tokenizer(symptoms, padding=True, truncation=True, return_tensors="pt")
+    output = model(inputs_id['input_ids'])
+    preds = torch.nn.functional.softmax(output.logits, -1).topk(5)
+    results = {id2label[preds.indices[0][i].item()]: float(preds.values[0][i].item()) for i in range(5)}
+    return results
+
+# Example symptoms for quick testing
+examples = [
+    "I can't stop sneezing and I feel really tired and crummy. My throat is really sore",
+    "I have been experiencing a severe headache that is accompanied by pain behind my eyes.",
+    "There are small red spots all over my body that I can't explain. It's worrying me.",
+    "I've been having a really hard time going to the bathroom lately. It's really painful"
+]
+
+with st.expander("📝 Example symptom descriptions"):
+    for ex in examples:
+        st.code(ex)
+
+# User input
+symptoms = st.text_area(
+    "Describe your symptoms:",
+    placeholder="e.g. I have a persistent cough and mild fever for the last 3 days.",
+    height=100
+)
+
+if st.button("Analyze Symptoms", type="primary"):
+    if symptoms.strip():
+        with st.spinner("Analyzing symptoms..."):
+            results = detect_symptom(symptoms)
+        st.subheader("Top 5 Predicted Diseases")
+        for disease, prob in results.items():
+            st.markdown(f"**{disease}**")
+            st.progress(prob)
+            st.caption(f"Probability: {prob:.2%}")
+    else:
+        st.warning("Please enter your symptoms above.")
+
+st.markdown("---")
+st.info("**Disclaimer:** This tool is for informational purposes only and does not provide medical advice. Please consult a healthcare professional for diagnosis and treatment.")
+components.iframe(
+    src="http://127.0.0.1:8080",
+    height=200)