Create app.py

app.py

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+import os
+import gradio as gr
+import whisper
+from gtts import gTTS
+import io
+from groq import Groq
+
+# Initialize the Groq client
+client = Groq(api_key="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")
+
+# Load the Whisper model
+model = whisper.load_model("base")  # You can choose other models like "small", "medium", "large"
+
+def process_audio(file_path):
+    try:
+        # Load the audio file
+        audio = whisper.load_audio(file_path)
+
+        # Transcribe the audio using Whisper
+        result = model.transcribe(audio)
+        text = result["text"]
+
+        # Generate a response using Groq
+        chat_completion = client.chat.completions.create(
+            messages=[{"role": "user", "content": text}],
+            model="llama3-8b-8192",  # Replace with the correct model if necessary
+        )
+
+        # Access the response using dot notation
+        response_message = chat_completion.choices[0].message.content.strip()
+
+        # Convert the response text to speech
+        tts = gTTS(response_message)
+        response_audio_io = io.BytesIO()
+        tts.write_to_fp(response_audio_io)  # Save the audio to the BytesIO object
+        response_audio_io.seek(0)
+
+        # Save audio to a file to ensure it's generated correctly
+        with open("response.mp3", "wb") as audio_file:
+            audio_file.write(response_audio_io.getvalue())
+
+        # Return the response text and the path to the saved audio file
+        return response_message, "response.mp3"
+
+    except Exception as e:
+        return f"An error occurred: {e}", None
+
+iface = gr.Interface(
+    fn=process_audio,
+    inputs=gr.Audio(type="filepath"),  # Use type="filepath"
+    outputs=[gr.Textbox(label="Response Text"), gr.Audio(label="Response Audio")],
+    live=True,
+    title="Voice-to-Voice Chatbot"
+)
+
+iface.launch()