Update app.py

app.py

@@ -59,12 +59,14 @@ import os
 import gradio as gr
 import whisper
 from gtts import gTTS
-from groq import Groq
+from gemani import Gemani  # Assuming you have a Gemani client similar to Groq
+import io  # Import io for BytesIO
 
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")
-if not GROQ_API_KEY:
-    raise ValueError("GROQ_API_KEY environment variable is not set.")
-client = Groq(api_key=GROQ_API_KEY)
+# Get the Gemani API key from environment variables
+GEMANI_API_KEY = os.getenv("GEMANI_API_KEY")
+if not GEMANI_API_KEY:
+    raise ValueError("GEMANI_API_KEY environment variable is not set.")
+client = Gemani(api_key=GEMANI_API_KEY)  # Initialize the Gemani client
 
 # Load Whisper model
 model = whisper.load_model("base")
@@ -78,16 +80,18 @@ def chatbot(audio=None):
         transcription = model.transcribe(audio)
         user_input = transcription.get("text", "")
 
-        # Generate a response using Llama 8B via Groq API
+        # Generate a response using Gemani API
         chat_completion = client.chat.completions.create(
             messages=[{"role": "user", "content": user_input}],
-            model="llama3-8b-8192",
+            model="gemani-model-8b",  # Replace with the correct model name for Gemani
         )
         response_text = chat_completion.choices[0].message.content
 
         # Convert the response text to speech using gTTS
         tts = gTTS(text=response_text, lang='en')
-        response_audio_io = tts.write_to_fp(None)  # Save the audio to the BytesIO object
+        response_audio_io = io.BytesIO()  # Create a BytesIO object
+        tts.save(response_audio_io)  # Save the audio to the BytesIO object
+        response_audio_io.seek(0)  # Rewind the BytesIO object
 
         return response_text, response_audio_io