Upload app.py

app.py

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+# Install the required libraries (run this on Colab)
+!pip install git+https://github.com/openai/whisper.git
+!pip install gradio
+!pip install gTTS  # You can use other TTS libraries if you prefer
+!pip install groq
+!pip install --upgrade pip setuptools wheel
+!pip install git+https://github.com/openai/whisper.git --no-cache-dir
+!pip install gradio --upgrade
+!pip install gTTS --upgrade
+!pip install groq
+
+
+import whisper
+import numpy as np
+from groq import Groq
+import os
+from gtts import gTTS
+import gradio as gr
+
+# Initialize Whisper model
+model = whisper.load_model("base")
+
+# Function to transcribe audio using Whisper
+def transcribe_audio(audio_file):
+    result = model.transcribe(audio_file)
+    return result['text']
+
+# Function to interact with Groq LLM
+def generate_response(transcription):
+    client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+    chat_completion = client.chat.completions.create(
+        messages=[{"role": "user", "content": transcription}],
+        model="llama3-groq-8b-8192-tool-use-preview",
+    )
+    response = chat_completion.choices[0].message.content
+    return response
+
+# Function to convert text into speech using gTTS
+def text_to_speech(text):
+    tts = gTTS(text)
+    tts.save("output.mp3")
+    return "output.mp3"
+
+# Main function to handle the chatbot interaction
+def chatbot_interaction(audio):
+    # Transcribe the uploaded audio to text
+    transcription = transcribe_audio(audio)
+    print(f"Transcription: {transcription}")
+    
+    # Generate a response from Groq's LLM
+    response = generate_response(transcription)
+    print(f"LLM Response: {response}")
+    
+    # Convert the LLM response to speech
+    output_audio = text_to_speech(response)
+    return output_audio
+
+# Gradio interface to deploy the chatbot
+def chatbot_ui():
+    # Input: Audio file upload, Output: Audio file with the response
+    gr.Interface(fn=chatbot_interaction, inputs="audio", outputs="audio").launch()
+
+# Run the chatbot UI
+chatbot_ui()