Create app.py

app.py

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+import gradio as gr
+import torch
+import soundfile as sf
+from transformers import pipeline
+from groq import Groq
+from TTS.api import TTS
+import os
+
+# ----------------------------
+# Load models
+# ----------------------------
+
+# Whisper (Speech → Text)
+stt = pipeline(
+    "automatic-speech-recognition",
+    model="openai/whisper-small"
+)
+
+# Groq Client
+client = Groq(api_key=os.environ["GROQ_API_KEY"])
+
+# Text → Speech
+tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC", progress_bar=False)
+
+# ----------------------------
+# Core function
+# ----------------------------
+
+def voice_to_voice(audio):
+    # Speech → Text
+    text = stt(audio)["text"]
+
+    # LLM Response
+    completion = client.chat.completions.create(
+        model="llama3-8b-8192",
+        messages=[{"role": "user", "content": text}]
+    )
+    reply = completion.choices[0].message.content
+
+    # Text → Speech
+    output_path = "response.wav"
+    tts.tts_to_file(text=reply, file_path=output_path)
+
+    return reply, output_path
+
+# ----------------------------
+# UI
+# ----------------------------
+
+ui = gr.Interface(
+    fn=voice_to_voice,
+    inputs=gr.Audio(type="filepath", label="🎤 Speak"),
+    outputs=[
+        gr.Textbox(label="🧠 AI Response"),
+        gr.Audio(label="🔊 Voice Reply")
+    ],
+    title="Voice to Voice AI (Groq + Hugging Face)",
+    description="Speak → AI thinks → AI speaks back"
+)
+
+ui.launch()