Create app.py

app.py

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+import os
+import whisper
+import gradio as gr
+from groq import Groq
+from TTS.api import TTS
+
+# ✅ Set up Groq API Key
+os.environ["GROQ_API_KEY"] = "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"
+
+# ✅ Load OpenAI Whisper model
+whisper_model = whisper.load_model("base")
+
+# ✅ Function to Transcribe Speech to Text
+def transcribe_audio(audio):
+    print("📝 Transcribing...")
+    result = whisper_model.transcribe(audio)
+    return result["text"]
+
+# ✅ Function to Get Response from Groq API
+def get_groq_response(prompt):
+    print("🤖 Generating Response...")
+    client = Groq(api_key=os.environ["GROQ_API_KEY"])
+    chat_completion = client.chat.completions.create(
+        messages=[{"role": "user", "content": prompt}],
+        model="llama-3.3-70b-versatile"
+    )
+    return chat_completion.choices[0].message.content
+
+# ✅ Function to Convert Text to Speech using Coqui TTS
+def text_to_speech(text):
+    print("🔊 Generating Speech...")
+    tts = TTS(model_name="tts_models/en/ljspeech/glow-tts")
+    output_file = "response.wav"
+    tts.tts_to_file(text=text, file_path=output_file)
+    return output_file
+
+# ✅ Gradio Interface Function
+def chatbot_pipeline(audio):
+    # Step 1: Convert Speech to Text
+    text = transcribe_audio(audio)
+    
+    # Step 2: Get Response from Groq API
+    response = get_groq_response(text)
+    
+    # Step 3: Convert Text Response to Speech
+    speech_file = text_to_speech(response)
+    
+    return text, response, speech_file
+
+# ✅ Build Gradio UI
+interface = gr.Interface(
+    fn=chatbot_pipeline,
+    inputs=gr.Audio(type="filepath"),  # Mic input
+    outputs=[
+        gr.Textbox(label="Transcribed Text"),
+        gr.Textbox(label="Chatbot Response"),
+        gr.Audio(label="Generated Speech")
+    ],
+    title="🗣️ Speech-to-Text AI Chatbot",
+    description="🎙️ Speak into the microphone → Get AI response → Listen to the reply!"
+)
+
+# ✅ Launch Gradio UI
+interface.launch(share=True)