Create app.py

app.py

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+# Install dependencies in Colab
+try:
+    import whisper, gradio as gr
+    from gtts import gTTS
+    from groq import Groq
+except:
+    import whisper, gradio as gr
+    from gtts import gTTS
+    from groq import Groq
+
+import os
+import tempfile
+
+# Load Whisper model
+whisper_model = whisper.load_model("base")
+
+# Groq API Key (replace with your actual key or set as env variable)
+GROQ_API_KEY = "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"
+client = Groq(api_key=GROQ_API_KEY)
+
+# Core logic
+def voice_chat(audio_path):
+    # Step 1: Transcribe audio
+    result = whisper_model.transcribe(audio_path)
+    user_text = result["text"]
+
+    # Step 2: Groq LLM response
+    response = client.chat.completions.create(
+        messages=[{"role": "user", "content": user_text}],
+        model="llama3-8b-8192",
+    )
+    bot_reply = response.choices[0].message.content
+
+    # Step 3: Text to speech using gTTS
+    tts = gTTS(bot_reply)
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as f:
+        tts.save(f.name)
+        audio_response_path = f.name
+
+    return user_text, bot_reply, audio_response_path
+
+# Gradio interface
+iface = gr.Interface(
+    fn=voice_chat,
+    inputs=gr.Microphone(label="🎤 Speak your question", type="filepath"),
+    outputs=[
+        gr.Text(label="📝 Transcribed Input"),
+        gr.Text(label="🤖 LLM Reply"),
+        gr.Audio(label="🔉 Spoken Reply", type="filepath")
+    ],
+    title="🗣️ Real-Time Voice-to-Voice Chatbot (Whisper + Groq + gTTS)",
+    live=True
+)
+
+if __name__ == "__main__":
+    iface.launch()