Update app.py

app.py

@@ -54,19 +54,18 @@
 # )
 
 # iface.launch()
-
 import os
 import gradio as gr
 import whisper
 from gtts import gTTS
-from gemani import Gemani  # Assuming you have a Gemani client similar to Groq
+from anthropic import Anthropic  # Import the Anthropic client
 import io  # Import io for BytesIO
 
-# 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
+# Get the Anthropic API key from environment variables
+ANTHROPIC_API_KEY = os.getenv("anthropic_key")
+if not ANTHROPIC_API_KEY:
+    raise ValueError("ANTHROPIC_API_KEY environment variable is not set.")
+client = Anthropic(api_key=ANTHROPIC_API_KEY)  # Initialize the Anthropic client
 
 # Load Whisper model
 model = whisper.load_model("base")
@@ -80,12 +79,13 @@ def chatbot(audio=None):
         transcription = model.transcribe(audio)
         user_input = transcription.get("text", "")
 
-        # Generate a response using Gemani API
-        chat_completion = client.chat.completions.create(
-            messages=[{"role": "user", "content": user_input}],
-            model="gemani-model-8b",  # Replace with the correct model name for Gemani
+        # Generate a response using Anthropic API
+        chat_completion = client.completions.create(
+            prompt=user_input,
+            model="claude-v1",  # Replace with the correct model name for Anthropic
+            max_tokens=100,  # Adjust as needed
         )
-        response_text = chat_completion.choices[0].message.content
+        response_text = chat_completion['completion']
 
         # Convert the response text to speech using gTTS
         tts = gTTS(text=response_text, lang='en')