Update app.py

app.py

@@ -1,28 +1,25 @@
-import os 
+import os
 import whisper
 from gtts import gTTS
 import gradio as gr
-from groq import Groq
+from googleapiclient.discovery import build
 from googletrans import Translator  # Translator for language conversion
 
-GROQ_API_KEY = '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'
+# Gemini API key
+API_KEY = "AIzaSyAh5Q4iJYJOH6hBZnIg1Gikb_jb8qDl-x0"
+service = build("gemini", "v1", developerKey=API_KEY)  # Adjust service name and version if necessary
 
 # Load Whisper model for transcription (use a multilingual model to support Urdu)
 model = whisper.load_model("large")  # Use "large" or "multilingual" for better Urdu support
 
-# Set up Groq API client
-client = Groq(api_key=GROQ_API_KEY)
-
 # Set up Translator
 translator = Translator()
 
-# Function to get the LLM response from Groq
+# Function to get the LLM response from Gemini
 def get_llm_response(user_input):
-    chat_completion = client.chat.completions.create(
-        messages=[{"role": "user", "content": user_input}],
-        model="llama3-8b-8192",  # Replace with your desired model
-    )
-    return chat_completion.choices[0].message.content
+    # Call the Gemini API
+    request = service.text().generate(body={"prompt": user_input}).execute()
+    return request.get("generatedText", "No response generated")  # Default to "No response" if empty
 
 # Function to convert text to speech using gTTS
 def text_to_speech(text, output_audio="output_audio.mp3"):
@@ -36,7 +33,7 @@ def chatbot(audio):
     result = model.transcribe(audio, language="ur")  # Specify Urdu language
     user_text = result["text"]
 
-    # Step 2: Get LLM response from Groq (in English)
+    # Step 2: Get LLM response from Gemini (in English)
     response_text = get_llm_response(user_text)
 
     # Step 3: Translate the English response to Urdu