Create app.py

app.py

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+
+# Import necessary libraries
+import whisper
+from gtts import gTTS
+import os
+import tempfile
+import gradio as gr
+from groq import Groq
+from dotenv import load_dotenv
+
+# Load environment variables from .env file (if available)
+load_dotenv()
+
+# Set up Groq API key (fallback method if not set as an environment variable)
+if "GROQ_API_KEY" not in os.environ:
+    os.environ["GROQ_API_KEY"] = "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"  # Replace with your Groq API key
+
+# Initialize Groq client
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+# Load the Whisper model
+whisper_model = whisper.load_model("base")  # "base" is lightweight; use "large" for higher accuracy
+
+# Define chatbot function
+def voice_to_voice_chatbot(input_audio):
+    """
+    Transcribes the audio input using Whisper, queries Groq's LLM API, 
+    converts the response to audio, and returns the audio file.
+    """
+    # Step 1: Transcribe audio to text using Whisper
+    try:
+        transcription_result = whisper_model.transcribe(input_audio)
+        user_query = transcription_result["text"]
+        print("User Query:", user_query)
+    except Exception as e:
+        return f"Error in transcription: {str(e)}"
+
+    # Step 2: Query the Groq API with the transcribed text
+    try:
+        chat_completion = client.chat.completions.create(
+            messages=[{"role": "user", "content": user_query}],
+            model="llama3-8b-8192",
+            stream=False
+        )
+        bot_response = chat_completion.choices[0].message.content
+        print("Bot Response:", bot_response)
+    except Exception as e:
+        return f"Error in LLM response: {str(e)}"
+
+    # Step 3: Convert the LLM response to speech using gTTS
+    try:
+        tts = gTTS(text=bot_response, lang="en")
+        output_audio_path = tempfile.NamedTemporaryFile(suffix=".mp3").name
+        tts.save(output_audio_path)
+    except Exception as e:
+        return f"Error in text-to-speech conversion: {str(e)}"
+
+    # Return the audio file path
+    return output_audio_path
+
+# Create Gradio interface for the chatbot
+interface = gr.Interface(
+    fn=voice_to_voice_chatbot,
+    inputs=gr.Audio(type="filepath"),  # Input: File path of recorded audio
+    outputs=gr.Audio(type="filepath"), # Output: File path of the generated audio
+    live=True,                         # Enable real-time interaction
+    description="This Voice to Voice Chatbot is created by Rizwan Sajad using OpenAI Whisper, Groq API, and gTTS."
+)
+
+# Launch Gradio interface
+interface.launch()