Create app.py

app.py

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+import gradio as gr
+import os
+import whisper
+from groq import Groq
+from gtts import gTTS
+
+# Initialize Whisper model for transcription
+model = whisper.load_model("base")
+
+# Set up Groq API
+GROQ_API_KEY="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"
+client = Groq(api_key=GROQ_API_KEY)
+
+
+# Function to query the LLM using Groq API
+def get_llm_response(input_text):
+    chat_completion = client.chat.completions.create(
+        messages=[{
+            "role": "user",
+            "content": input_text,
+        }],
+        model="llama3-8b-8192",
+    )
+    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
+
+
+def chatbot(audio):
+  result=model.transcribe(audio)
+  user_text=result['text']
+  response_text=get_llm_response(user_text)
+  output_audio=text_to_speech(response_text)
+  return response_text,output_audio
+
+
+# Create Gradio interface for microphone input
+iface = gr.Interface(
+    fn=chatbot,
+    inputs=gr.Audio(type="filepath"),  # Capturing audio from the microphone
+    outputs=[gr.Textbox(),gr.Audio(type="filepath")],  # Outputting audio file path
+    live=True
+)
+
+iface.launch()