Update app.py

app.py

@@ -18,10 +18,11 @@ llm_english       = safe_client("E-motionAssistant/TherapyEnglish")
 tts_client         = safe_client("E-motionAssistant/Space3")
 
 def main_orchestrator(audio_input, text_input, history):
+    # If history is None, initialize it
     if history is None:
         history = []
 
-    # 1. EMOTION LOGIC (Keeping your current logic)
+    # 1. EMOTION LOGIC
     emotion = "Neutral"
     try:
         if audio_input:
@@ -31,38 +32,42 @@ def main_orchestrator(audio_input, text_input, history):
     except:
         emotion = "Neutral"
 
-    # 2. THE "STRICT" MESSAGE FIX
-    # We must turn history into a LIST of DICTIONARIES where 'content' is ALSO A LIST
+    # 2. THE STRICT MESSAGE FIX (This stops the "Data Incompatible" error)
+    # Convert simple history [[u,b]] to strict Gradio 5/6 api_history
     api_history = []
     for u, b in history:
         api_history.append({
             "role": "user", 
-            "content": [{"type": "text", "text": str(u)}] # <--- This list is the fix!
+            "content": [{"type": "text", "text": str(u)}]
         })
         api_history.append({
             "role": "assistant", 
             "content": [{"type": "text", "text": str(b)}]
         })
 
-    # 3. THE "STRICT" CURRENT MESSAGE FIX
+    # Prepare the current message as a content block list
     bundled_text = f"Context: User is {emotion}. Message: {text_input}"
     current_message = {
         "role": "user", 
-        "content": [{"type": "text", "text": bundled_text}] # <--- Nested list here too
+        "content": [{"type": "text", "text": bundled_text}]
     }
 
-    # 4. CALL LLM
+    # 3. CALL LLM
     try:
-        # Now the data matches the 'ChatMessage' object exactly
+        # We send the strictly formatted current_message and api_history
         response = llm_english.predict(
             message=current_message,
             history=api_history,
             api_name="/chat"
         )
     except Exception as e:
-        response = f"LLM Error: {str(e)}"
+        # Fallback for LLMs that might still want simple strings
+        try:
+            response = llm_english.predict(message=bundled_text, history=history, api_name="/chat")
+        except:
+            response = f"LLM Error: {str(e)}"
 
-    # 5. TTS & RETURN
+    # 4. TTS LOGIC
     audio_res = None
     try:
         if tts_client and response:
@@ -70,7 +75,8 @@ def main_orchestrator(audio_input, text_input, history):
     except:
         audio_res = None
 
-    # For the UI Chatbot, we keep it simple so it doesn't crash older versions
+    # 5. UPDATE HISTORY
+    # Return as list of lists for the UI Chatbot
     history.append([text_input, response])
     return history, audio_res
 
@@ -83,10 +89,11 @@ with gr.Blocks() as demo:
             text_in = gr.Textbox(label="Message")
             btn = gr.Button("Send")
         with gr.Column():
-            # REMOVED type="messages" to fix the TypeError
-            chatbot_ui = gr.Chatbot(label="Therapy History") 
+            # type="messages" creates the dictionary format seen in your docs
+            chatbot_ui = gr.Chatbot(label="Therapy History", type="messages") 
             audio_out = gr.Audio(autoplay=True)
 
     btn.click(main_orchestrator, [audio_in, text_in, state], [chatbot_ui, audio_out])
 
+# Launch with ssr_mode=False if you see flickering
 demo.launch()