Update app.py

app.py

@@ -21,7 +21,7 @@ def main_orchestrator(audio_input, text_input, history):
     if history is None:
         history = []
 
-    # 1. EMOTION LOGIC
+    # 1. EMOTION LOGIC (Keeping your current logic)
     emotion = "Neutral"
     try:
         if audio_input:
@@ -31,34 +31,46 @@ def main_orchestrator(audio_input, text_input, history):
     except:
         emotion = "Neutral"
 
-    # 2. LLM LOGIC (Strict format for the sub-space API)
+    # 2. THE "STRICT" MESSAGE FIX
+    # We must turn history into a LIST of DICTIONARIES where 'content' is ALSO A LIST
     api_history = []
     for u, b in history:
-        api_history.append({"role": "user", "content": [{"type": "text", "text": str(u)}]})
-        api_history.append({"role": "assistant", "content": [{"type": "text", "text": str(b)}]})
+        api_history.append({
+            "role": "user", 
+            "content": [{"type": "text", "text": str(u)}] # <--- This list is the fix!
+        })
+        api_history.append({
+            "role": "assistant", 
+            "content": [{"type": "text", "text": str(b)}]
+        })
 
-    prompt = f"Context: User is {emotion}. Message: {text_input}"
-    
+    # 3. THE "STRICT" CURRENT MESSAGE FIX
+    bundled_text = f"Context: User is {emotion}. Message: {text_input}"
+    current_message = {
+        "role": "user", 
+        "content": [{"type": "text", "text": bundled_text}] # <--- Nested list here too
+    }
+
+    # 4. CALL LLM
     try:
-        # Calling your sub-space
+        # Now the data matches the 'ChatMessage' object exactly
         response = llm_english.predict(
-            message={"role": "user", "content": [{"type": "text", "text": prompt}]},
+            message=current_message,
             history=api_history,
             api_name="/chat"
         )
     except Exception as e:
         response = f"LLM Error: {str(e)}"
 
-    # 3. TTS LOGIC
+    # 5. TTS & RETURN
     audio_res = None
     try:
-        if tts_client:
+        if tts_client and response:
             audio_res = tts_client.predict(str(response), api_name="/predict")
     except:
         audio_res = None
 
-    # 4. UPDATE HISTORY
-    # Simple list format [[user, bot]] is compatible with ALL Gradio versions
+    # For the UI Chatbot, we keep it simple so it doesn't crash older versions
     history.append([text_input, response])
     return history, audio_res