Update app.py

app.py

@@ -1,53 +1,50 @@
-
-# app.py
 import gradio as gr
-#from eeg_model import simulate_eeg_data, predict_mental_state
-from generative_model import generate_response
-from audio_utils import record_audio
-from tts_utils import nigerian_accent_tts
-from conversation_memory import ConversationMemory
-
-memory = ConversationMemory()
-
-# Logic
-def chat(user_text=None, record_audio_flag=False):
-    if record_audio_flag:
-        user_text = record_audio()
-
-    if not user_text:
-        return "No input detected.", None
-
-    # 1. Predict mental state
-    eeg_features = simulate_eeg_data()
-    mental_state = predict_mental_state(eeg_features)
-
-    # 2. Generate response with memory
-    history = memory.get_formatted_history()
-    ai_response = generate_response(mental_state, user_text, history)
-    
-    # 3. Update memory
-    memory.add_exchange(user_input=user_text, bot_response=ai_response)
-    
-    # 4. Nigerian Accent TTS
-    nigerian_accent_tts(ai_response, mental_state)
-
-    return ai_response, "response.mp3"
-
-# Gradio app
-with gr.Blocks() as demo:
-    gr.Markdown("# EEG Mental State Chatbot with Nigerian Voice (Upgraded)")
-    
-    with gr.Row():
-        user_input = gr.Textbox(label="Type your message")
-        record_audio_flag = gr.Checkbox(label="Use Microphone?", value=False)
-    
-    with gr.Row():
-        submit_btn = gr.Button("Send")
-    
-    with gr.Row():
-        chatbot_response = gr.Textbox(label="Chatbot response")
-        chatbot_audio = gr.Audio(label="Chatbot voice", type="filepath")
-
-    submit_btn.click(chat, inputs=[user_input, record_audio_flag], outputs=[chatbot_response, chatbot_audio])
-
-demo.launch()
+import google.generativeai as genai
+import os
+from gtts import gTTS
+import random
+from tempfile import NamedTemporaryFile
+from dotenv import load_dotenv
+
+load_dotenv()
+API_KEY = os.getenv("GEMINI_API_KEY") or "YOUR_GEMINI_API_KEY"
+
+genai.configure(api_key=API_KEY)
+model = genai.GenerativeModel("gemini-1.5-pro")
+
+def predict_emotion():
+    return random.choice(["positive", "neutral", "negative"])
+
+def chat_with_ai(audio):
+    if audio is None:
+        return "No audio provided", None
+
+    import speech_recognition as sr
+    recognizer = sr.Recognizer()
+    with sr.AudioFile(audio) as source:
+        audio_data = recognizer.record(source)
+        try:
+            text = recognizer.recognize_google(audio_data)
+        except sr.UnknownValueError:
+            return "Sorry, I couldn't understand the audio.", None
+
+    emotion = predict_emotion()
+    prompt = f"The user is feeling {emotion}. Respond appropriately.\nUser said: {text}"
+    response = model.generate_content(prompt).text
+
+    # Convert response to audio
+    tts = gTTS(text=response, lang='en', tld='com.ng')
+    temp_audio = NamedTemporaryFile(delete=False, suffix=".mp3")
+    tts.save(temp_audio.name)
+
+    return response, temp_audio.name
+
+iface = gr.Interface(
+    fn=chat_with_ai,
+    inputs=gr.Audio(source="microphone", type="filepath", label="Speak"),
+    outputs=[gr.Textbox(label="AI Response"), gr.Audio(label="AI Voice")],
+    title="Voice-Based AI Assistant",
+    description="Speak to the AI and get an emotional, spoken response."
+)
+
+iface.launch()