Fix loading of patient

app.py

@@ -255,11 +255,28 @@ def format_prompt(messages):
 
 def add_user_message(user_input, history):
     current_id = patient_id.value
-    if current_id:
-        conversation = patient_conversations.get(current_id, [])
-        conversation.append({"role": "user", "content": user_input})
-        patient_conversations[current_id] = conversation
-    return "", patient_conversations[current_id]
+    if not current_id:
+        # No patient selected yet — just return empty chat
+        return "", []
+
+    conversation = patient_conversations.get(current_id, [])
+    conversation.append({"role": "user", "content": user_input})
+    patient_conversations[current_id] = conversation
+    return "", [msg for msg in ([{
+        "role": "assistant",
+        "content": (
+            "**Welcome to the Radiologist's Companion!**\n\n"
+            "You can ask me about the patient's medical history or available imaging data.\n"
+            "- I can summarize key details from the EHR.\n"
+            "- I can tell you which medical images are available.\n"
+            "- If you'd like an organ segmentation (e.g. spleen, liver, kidney_left, colon, femur_right) on an abdominal CT scan, just ask!\n\n"
+            "**Example Requests:**\n"
+            "- \"What do we know about this patient?\"\n"
+            "- \"Which images are available for this patient?\"\n"
+            "- \"Can you segment the spleen from the CT scan?\"\n"
+        )
+    }] + conversation)]
+
 
 
 def autofill_patient(patient_key):
@@ -275,30 +292,64 @@ def autofill_patient(patient_key):
 
 
 
+# --- Functions (OUTSIDE) ---
+
+def resolve_model_choice(mode, dropdown_value, textbox_value):
+    return textbox_value.strip() if mode == "Enter custom model" else dropdown_value
+
+def load_patient_conversation(patient_key):
+    if patient_key in patient_db:
+        patient_id_val = patient_db[patient_key]["id"]
+        history = patient_conversations.get(patient_id_val, [])
+
+        welcome_message = {
+            "role": "assistant",
+            "content": (
+                "**Welcome to the Radiologist's Companion!**\n\n"
+                "You can ask me about the patient's medical history or available imaging data.\n"
+                "- I can summarize key details from the EHR.\n"
+                "- I can tell you which medical images are available.\n"
+                "- If you'd like an organ segmentation (e.g. spleen, liver, kidney_left, colon, femur_right) on an abdominal CT scan, just ask!\n\n"
+                "**Example Requests:**\n"
+                "- \"What do we know about this patient?\"\n"
+                "- \"Which images are available for this patient?\"\n"
+                "- \"Can you segment the spleen from the CT scan?\"\n"
+            )
+        }
+        return [welcome_message] + history
+    return []
+
+# --- Gradio App ---
+
 with gr.Blocks(css=".gradio-container {height: 100vh; overflow: hidden;}") as demo:
     gr.Markdown("<h2 style='text-align: center;'>Radiologist's Companion</h2>")
-
     default_model = gr.State(model_choices[0])
 
-    with gr.Row(equal_height=True):  # <-- make columns same height
+    with gr.Row(equal_height=True):
+        # Patient Information
         with gr.Column(scale=1):
             gr.Markdown("### Patient Information")
             patient_selector = gr.Dropdown(
-                choices=list(patient_db.keys()), label="Select Patient", allow_custom_value=False
+                choices=list(patient_db.keys()),
+                value=list(patient_db.keys())[0],
+                label="Select Patient",
+                allow_custom_value=False
             )
             patient_name = gr.Textbox(label="Name", placeholder="e.g., John Doe")
             patient_age = gr.Textbox(label="Age", placeholder="e.g., 45")
             patient_id = gr.Textbox(label="Patient ID", placeholder="e.g., 123456")
-            patient_notes = gr.Textbox(label="Clinical Notes", lines=10, placeholder="e.g., History of chest pain...")
+            patient_notes = gr.Textbox(label="Clinical Notes", lines=10)
 
+        # Chat
         with gr.Column(scale=2):
             gr.Markdown("### Chat")
-            chatbot = gr.Chatbot(label="Chat", type="messages", height=500)  # <-- fixed height
+            chatbot = gr.Chatbot(label="Chat", type="messages", height=500)
             msg = gr.Textbox(label="Your message", placeholder="Enter your chat message...", show_label=False)
             with gr.Row():
                 submit_btn = gr.Button("Submit", variant="primary")
                 clear_btn = gr.Button("Clear", variant="secondary")
 
+        # Model Settings
         with gr.Column(scale=1):
             gr.Markdown("### Model Settings")
             mode = gr.Radio(["Choose from list", "Enter custom model"], value="Choose from list", label="Model Input Mode")
@@ -306,42 +357,24 @@ with gr.Blocks(css=".gradio-container {height: 100vh; overflow: hidden;}") as de
             model_textbox = gr.Textbox(label="Or Enter HF Model Name")
             model_status = gr.Textbox(label="Model Status", interactive=False)
 
-    # Functions for resolving model choice
-    def resolve_model_choice(mode, dropdown_value, textbox_value):
-        return textbox_value.strip() if mode == "Enter custom model" else dropdown_value
-
-    # Link patient selector
-    patient_selector.change(
-        autofill_patient,
-        inputs=[patient_selector],
+    # --- Event Bindings ---
+    
+    # Load patient info + conversation + model on startup
+    demo.load(
+        lambda: autofill_patient(list(patient_db.keys())[0]),
+        inputs=None,
         outputs=[patient_name, patient_age, patient_id, patient_notes]
+    ).then(
+        lambda: load_patient_conversation(list(patient_db.keys())[0]),
+        inputs=None,
+        outputs=chatbot
+    ).then(
+        load_model_on_selection,
+        inputs=default_model,
+        outputs=model_status
     )
 
-    # After patient selected, load their conversation into chatbot
-    def load_patient_conversation(patient_key):
-        if patient_key in patient_db:
-            patient_id_val = patient_db[patient_key]["id"]
-            history = patient_conversations.get(patient_id_val, [])
-
-            # Show welcome + history
-            welcome_message = {
-                "role": "assistant",
-                "content": (
-                    "**Welcome to the Radiologist's Companion!**\n\n"
-                    "You can ask me about the patient's medical history or available imaging data.\n"
-                    "- I can summarize key details from the EHR.\n"
-                    "- I can tell you which medical images are available.\n"
-                    "- If you'd like an organ segmentation (e.g. spleen, liver, kidney_left, colon, femur_right) on an abdominal CT scan, just ask!\n\n"
-                    "**Example Requests:**\n"
-                    "- \"What do we know about this patient?\"\n"
-                    "- \"Which images are available for this patient?\"\n"
-                    "- \"Can you segment the spleen from the CT scan?\"\n"
-                )
-            }
-            return [welcome_message] + history
-        return []
-
-
+    # Patient selection changes
     patient_selector.change(
         autofill_patient,
         inputs=[patient_selector],
@@ -352,10 +385,6 @@ with gr.Blocks(css=".gradio-container {height: 100vh; overflow: hidden;}") as de
         outputs=[chatbot]
     )
 
-
-    # Load on launch
-    demo.load(fn=load_model_on_selection, inputs=default_model, outputs=model_status)
-
     # Model selection logic
     mode.select(fn=resolve_model_choice, inputs=[mode, model_selector, model_textbox], outputs=default_model).then(
         load_model_on_selection, inputs=default_model, outputs=model_status
@@ -367,7 +396,7 @@ with gr.Blocks(css=".gradio-container {height: 100vh; overflow: hidden;}") as de
         load_model_on_selection, inputs=default_model, outputs=model_status
     )
 
-    # Submit via enter key or button
+    # Submit message
     msg.submit(add_user_message, [msg, chatbot], [msg, chatbot], queue=False).then(
         chat_with_model, chatbot, chatbot
     )
@@ -375,6 +404,8 @@ with gr.Blocks(css=".gradio-container {height: 100vh; overflow: hidden;}") as de
         chat_with_model, chatbot, chatbot
     )
 
+    # Clear chat
     clear_btn.click(lambda: [], None, chatbot, queue=False)
 
 demo.launch()
+