Create app.py

app.py

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+# Import libraries
+import whisper
+import os
+from gtts import gTTS
+import gradio as gr
+from groq import Groq
+
+# Load Whisper model for transcription
+model = whisper.load_model("base")
+
+GROQ_API_KEY = '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'
+client = Groq(api_key=GROQ_API_KEY)
+
+# Set up Groq API client (ensure GROQ_API_KEY is set in your environment)
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+# Function to get the LLM response from Groq
+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
+
+# Function to convert text to speech using gTTS
+def text_to_speech(text, output_audio="output_audio.mp3"):
+    tts = gTTS(text)
+    tts.save(output_audio)
+    return output_audio
+
+# Main chatbot function to handle audio input and output
+def chatbot(audio):
+    # Step 1: Transcribe the audio using Whisper
+    result = model.transcribe(audio)
+    user_text = result["text"]
+
+    # Step 2: Get LLM response from Groq
+    response_text = get_llm_response(user_text)
+
+    # Step 3: Convert the response text to speech
+    output_audio = text_to_speech(response_text)
+
+    return response_text, output_audio
+
+# Gradio interface for real-time interaction
+iface = gr.Interface(
+    fn=chatbot,
+    inputs=gr.Audio(type="filepath"),  # Input from mic or file
+    outputs=[gr.Textbox(), gr.Audio(type="filepath")],  # Output: response text and audio
+    live=True
+)
+
+# Launch the Gradio app
+iface.launch()