Created Sample app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from gtts import gTTS
+import os
+
+# Initialize Whisper for speech-to-text
+whisper = pipeline("automatic-speech-recognition", model="openai/whisper-tiny")
+
+# Hardcoded knowledge base for Q&A
+knowledge_base = {
+    "what cars are available": "We have Toyota Camry, Honda Civic, and Ford Mustang.",
+    "price of camry": "The Toyota Camry starts at $25,000."
+}
+
+def transcribe(audio):
+    return whisper(audio)["text"]
+
+def text_to_speech(text):
+    tts = gTTS(text, lang="en")
+    tts.save("response.mp3")
+    return "response.mp3"
+
+def answer_question(text):
+    for key in knowledge_base:
+        if key in text.lower():
+            return knowledge_base[key]
+    return "Sorry, I can help with car availability and prices. Try again!"
+
+def process_audio(audio):
+    text = transcribe(audio)
+    response = answer_question(text)
+    audio_response = text_to_speech(response)
+    return response, audio_response
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# AI Support Agent: Car Dealership")
+    audio_input = gr.Audio(source="microphone", label="Speak to the Agent")
+    text_output = gr.Textbox(label="Agent Response")
+    audio_output = gr.Audio(label="Listen to Response")
+    gr.Interface(fn=process_audio, inputs=audio_input, outputs=[text_output, audio_output]).launch()
+
+demo.launch()