Create app.py

app.py

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+import gradio as gr
+import requests
+import tempfile
+import pyttsx3
+from faster_whisper import WhisperModel
+
+# === Config ===
+GROQ_API_KEY = "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"
+GROQ_MODEL = "llama3-70b-8192"
+GROQ_API_URL = "https://api.groq.com/openai/v1/chat/completions"
+
+# === Init Whisper ===
+whisper = WhisperModel("base", device="cpu", compute_type="int8")
+
+# === Init TTS (offline) ===
+tts_engine = pyttsx3.init()
+
+def speak_to_file(text, output_file="output.wav"):
+    """Save TTS to a file using pyttsx3."""
+    tts_engine.save_to_file(text, output_file)
+    tts_engine.runAndWait()
+    return output_file
+
+def process_audio(audio_file):
+    # 1. Speech to Text
+    segments, _ = whisper.transcribe(audio_file)
+    user_text = " ".join([segment.text for segment in segments])
+
+    # 2. Groq API Call
+    headers = {
+        "Authorization": f"Bearer {GROQ_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    payload = {
+        "model": GROQ_MODEL,
+        "messages": [{"role": "user", "content": user_text}],
+        "temperature": 0.5
+    }
+
+    response = requests.post(GROQ_API_URL, headers=headers, json=payload)
+    if response.status_code != 200:
+        return f"Groq API Error: {response.text}", None
+
+    reply = response.json()["choices"][0]["message"]["content"]
+
+    # 3. TTS to file
+    audio_output = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
+    speak_to_file(reply, audio_output.name)
+
+    return reply, audio_output.name
+
+iface = gr.Interface(
+    fn=process_audio,
+    inputs=gr.Audio(source="microphone", type="filepath", label="🎤 Speak your question"),
+    outputs=[
+        gr.Textbox(label="🧠 Groq Response"),
+        gr.Audio(label="🔊 AI Voice Reply")
+    ],
+    title="🗣️ Voice AI Assistant (Groq + Whisper + Offline TTS)",
+    description="Use your mic. Whisper transcribes, Groq replies, voice response from pyttsx3 (offline).",
+    live=True
+)
+
+iface.launch()