Update app.py

app.py

@@ -1,11 +1,86 @@
-# TEST THIS FIRST
-try:
-    client_test = Client("E-motionAssistant/TherapyEnglish", token=hf_token)
-    # Try a tiny, simple string first
-    res = client_test.predict(
-        message={"role": "user", "content": [{"type": "text", "text": "Hi"}]},
-        api_name="/chat"
-    )
-    print("✅ TherapyEnglish is ALIVE:", res)
-except Exception as e:
-    print("❌ TherapyEnglish is DEAD:", e)
+import gradio as gr
+# CRITICAL: This is the line the error is complaining about
+from gradio_client import Client, handle_file 
+import os
+
+# Get the token from Space Secrets
+hf_token = os.environ.get("HF_TOKEN")
+
+# 1. SETUP CLIENTS
+# We wrap this in a function to handle errors gracefully
+def safe_client(space_id):
+    try:
+        # If the import worked, this will now run
+        return Client(space_id, token=hf_token)
+    except Exception as e:
+        print(f"⚠️ Could not connect to {space_id}: {e}")
+        return None
+
+# Initializing your sub-spaces
+speech_emo_client = safe_client("E-motionAssistant/ser-wav2vec")
+text_emo_client   = safe_client("E-motionAssistant/Space4")
+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:
+        history = []
+    
+    # Verify LLM is connected
+    if not llm_english:
+        return history + [["System", "Error: TherapyEnglish client not initialized."]], None
+
+    # --- EMOTION LOGIC ---
+    emotion = "Neutral"
+    try:
+        if audio_input:
+            emotion = speech_emo_client.predict(handle_file(audio_input), api_name="/predict")
+        elif text_input:
+            emotion = text_emo_client.predict(text_input, api_name="/predict")
+    except:
+        emotion = "Neutral"
+
+    # --- LLM LOGIC ---
+    # Convert history for Gradio 6
+    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)}]})
+
+    prompt = f"Context: User is {emotion}. Message: {text_input}"
+    
+    try:
+        response = llm_english.predict(
+            message={"role": "user", "content": [{"type": "text", "text": prompt}]},
+            history=api_history,
+            api_name="/chat"
+        )
+    except Exception as e:
+        response = f"LLM Error: {str(e)}"
+
+    # --- TTS ---
+    audio_res = None
+    try:
+        if tts_client:
+            audio_res = tts_client.predict(str(response), api_name="/predict")
+    except:
+        audio_res = None
+
+    history.append([text_input, response])
+    return history, audio_res
+
+# --- UI ---
+with gr.Blocks() as demo:
+    state = gr.State([])
+    with gr.Row():
+        with gr.Column():
+            audio_in = gr.Audio(label="Voice", type="filepath")
+            text_in = gr.Textbox(label="Message")
+            btn = gr.Button("Send")
+        with gr.Column():
+            chat = gr.Chatbot(label="Therapy History", type="messages")
+            audio_out = gr.Audio(autoplay=True)
+
+    btn.click(main_orchestrator, [audio_in, text_in, state], [chat, audio_out])
+
+demo.launch()