Update app.py

app.py

@@ -1,8 +1,31 @@
 import gradio as gr
 from transformers import pipeline
-pipe = pipeline(task="automatic-speech-recognition", 
-                model="openai/whisper-tiny")
-gr.Interface.from_pipeline(pipe,
-                           title="My Alexa",
-                           description="Your AI assistant"
-                           ).launch(inbrowser=True)
+
+# Create pipelines for ASR, QA, and TTS
+asr_pipeline = pipeline("automatic-speech-recognition", model="canary/asr-small-librispeech", device=0)  # Adjust device based on your hardware
+qa_pipeline = pipeline("question-answering", model="LLAMA/llama3-base-qa", tokenizer="LLAMA/llama3-base-qa")
+tts_pipeline = pipeline("text-to-speech", model="patrickvonplaten/vits-large", device=0)  # Adjust device based on your hardware
+
+def ai_assistant(audio_input):
+    # Perform automatic speech recognition (ASR)
+    transcribed_text = asr_pipeline(audio_input)[0]['transcription']
+
+    # Perform question answering (QA)
+    question = transcribed_text
+    context = "Insert your context here"  # Provide the context for the question answering model
+    answer = qa_pipeline(question=question, context=context)
+
+    # Convert the answer to speech using text-to-speech (TTS)
+    tts_output = tts_pipeline(answer['answer'])
+
+    # Output the speech
+    return tts_output[0]['audio']
+
+if __name__ == "__main__":
+    # Create a Gradio interface
+    gr.Interface(ai_assistant, 
+                 inputs=gr.inputs.Audio(source="microphone", type="microphone", label="Speak Here"),
+                 outputs=gr.outputs.Audio(type="audio", label="Assistant's Response"),
+                 title="AI Assistant",
+                 description="An AI Assistant that answers questions based on your speech input.")
+    .launch()