Create app.py

app.py

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+# Import libraries
+import whisper
+from gtts import gTTS
+from groq import Groq
+import os
+import gradio as gr
+
+# Load Whisper model
+model = whisper.load_model("base")
+
+GROQ_API_KEY = "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"
+client = Groq(api_key=GROQ_API_KEY)
+
+# Function to process audio
+def process_audio(audio_path):
+    # Transcribe input audio
+    transcription = model.transcribe(audio_path)["text"]
+    
+    # Interact with the GROQ model
+    chat_completion = client.chat.completions.create(
+        messages=[{"role": "user", "content": transcription}],
+        model="llama3-8b-8192"
+    )
+    response_text = chat_completion.choices[0].message.content
+    
+    # Convert LLM response to audio
+    tts = gTTS(response_text)
+    response_audio_path = "response.mp3"
+    tts.save(response_audio_path)
+    
+    return response_text, response_audio_path
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("### Real Voice-to-Voice Chatbot")
+    with gr.Row():
+        input_audio = gr.Audio(type="filepath", label="Record Your Voice")
+        output_text = gr.Textbox(label="LLM Response", lines=4)
+        output_audio = gr.Audio(label="Response Audio")
+    submit_button = gr.Button("Submit")
+    submit_button.click(
+        process_audio,
+        inputs=[input_audio],
+        outputs=[output_text, output_audio],
+    )
+
+demo.launch()