Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+
+st.set_page_config(page_title="USMLE-style Medical Cases Demo")
+st.title("USMLE-style Medical Cases Demo")
+
+# Initialize session state for step tracking
+if "step_case1" not in st.session_state:
+    st.session_state.step_case1 = 0
+if "step_case2" not in st.session_state:
+    st.session_state.step_case2 = 0
+
+tab1, tab2 = st.tabs(["Case 1: ALS Patient", "Case 2: Congenital Mutism"])
+
+########################
+# Case 1: ALS Patient
+########################
+with tab1:
+    img_display = st.empty()
+    
+    # Step 0: initial image and case
+    if st.session_state.step_case1 == 0:
+        img1 = Image.open("1.png")
+        img_display.image(img1, caption="Patient MRI", use_column_width=True)
+        st.markdown("""
+        **Case:**  
+        A 57-year-old male presents with progressive weakness in his right hand over the past 6 months. He reports muscle cramps and twitching in his arms and legs.  
+        Over the past 2 months, he has noticed **slurred speech and difficulty projecting his voice**, making it harder to be understood. He also reports occasional choking when drinking liquids.  
+        Past medical history is unremarkable. Examination shows fasciculations, hyperreflexia, and mild dysarthria. EMG shows denervation. MRI is unremarkable.  
+        Diagnosis: **Amyotrophic Lateral Sclerosis (ALS)** with early bulbar involvement.
+        """)
+        if st.button("Next: Segment"):
+            st.session_state.step_case1 += 1
+
+    # Step 1: Segment
+    elif st.session_state.step_case1 == 1:
+        img2 = Image.open("2.png")
+        img_display.image(img2, caption="Segmented MRI", use_column_width=True)
+        if st.button("Next: Extract Vocal Tract"):
+            st.session_state.step_case1 += 1
+
+    # Step 2: Extract Vocal Tract
+    elif st.session_state.step_case1 == 2:
+        img3 = Image.open("3.png")
+        img_display.image(img3, caption="Vocal Tract Extraction", use_column_width=True)
+        if st.button("Next: Classify"):
+            st.session_state.step_case1 += 1
+
+    # Step 3: Classify
+    elif st.session_state.step_case1 == 3:
+        img4 = Image.open("4.png")
+        img_display.image(img4, caption="Classification Result", use_column_width=True)
+        if st.button("Next: Predict Audio"):
+            st.session_state.step_case1 += 1
+
+    # Step 4: Predict Audio
+    elif st.session_state.step_case1 == 4:
+        st.audio("1.wav", format="audio/wav")
+        if st.button("Next: Display Score"):
+            st.session_state.step_case1 += 1
+
+    # Step 5: Display Score
+    elif st.session_state.step_case1 == 5:
+        st.success("Score: 0.940 / 1.0")
+
+########################
+# Case 2: Congenital Mutism
+########################
+with tab2:
+    img_display2 = st.empty()
+
+    # Step 0: initial image and case
+    if st.session_state.step_case2 == 0:
+        img5 = Image.open("5.png")
+        img_display2.image(img5, caption="Patient MRI", use_column_width=True)
+        st.markdown("""
+        **Case:**  
+        A 23-year-old male was born with congenital mutism and has never spoken. He now seeks to **restore his voice** using speech synthesis and vocal tract reconstruction.  
+        The patient has normal cognition and hearing. Physical exam shows no structural abnormalities. Imaging studies (shown above) guide personalized speech restoration.
+        """)
+        if st.button("Next: Segment Case 2"):
+            st.session_state.step_case2 += 1
+
+    # Step 1: Segment
+    elif st.session_state.step_case2 == 1:
+        img6 = Image.open("6.png")
+        img_display2.image(img6, caption="Segmented MRI", use_column_width=True)
+        if st.button("Next: Extract Vocal Tract"):
+            st.session_state.step_case2 += 1
+
+    # Step 2: Extract Vocal Tract
+    elif st.session_state.step_case2 == 2:
+        img7 = Image.open("7.png")
+        img_display2.image(img7, caption="Vocal Tract Extraction", use_column_width=True)
+        if st.button("Next: Classify"):
+            st.session_state.step_case2 += 1
+
+    # Step 3: Classify
+    elif st.session_state.step_case2 == 3:
+        img8 = Image.open("8.png")
+        img_display2.image(img8, caption="Classification Result", use_column_width=True)
+        if st.button("Next: Predict Audio"):
+            st.session_state.step_case2 += 1
+
+    # Step 4: Predict Audio
+    elif st.session_state.step_case2 == 4:
+        st.audio("1.wav", format="audio/wav")
+        st.audio("2.wav", format="audio/wav")
+        st.audio("3.wav", format="audio/wav")
+        if st.button("Next: Display Score"):
+            st.session_state.step_case2 += 1
+
+    # Step 5: Display Score
+    elif st.session_state.step_case2 == 5:
+        st.success("Score: 0.750 / 1.0")