Create app.py

app.py

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+import os
+import tempfile
+import numpy as np
+import gradio as gr
+import whisper
+from gtts import gTTS
+from groq import Groq
+import soundfile as sf
+
+# Set up Groq API key
+os.environ['GROQ_API_KEY'] = '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'
+groq_client = Groq(api_key=os.environ.get('GROQ_API_KEY'))
+
+# Load Whisper model
+whisper_model = whisper.load_model("base")
+
+def process_audio(audio_file_path):
+    try:
+        # Ensure audio_file_path is valid
+        if not audio_file_path:
+            raise ValueError("No audio file provided")
+        
+        print(f"Received audio file path: {audio_file_path}")
+
+        # Read the audio file from the file path
+        with open(audio_file_path, 'rb') as f:
+            audio_data = f.read()
+
+        # Save the audio data to a temporary file
+        with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as temp_audio_file:
+            temp_audio_path = temp_audio_file.name
+            temp_audio_file.write(audio_data)
+        
+        # Ensure the temporary file is properly closed before processing
+        temp_audio_file.close()
+
+        # Transcribe audio using Whisper
+        result = whisper_model.transcribe(temp_audio_path)
+        user_text = result['text']
+        print(f"Transcribed text: {user_text}")
+        
+        # Generate response using Llama 8b model with Groq API
+        chat_completion = groq_client.chat.completions.create(
+            messages=[
+                {
+                    "role": "user",
+                    "content": user_text,
+                }
+            ],
+            model="llama3-8b-8192",
+        )
+        response_text = chat_completion.choices[0].message.content
+        print(f"Response text: {response_text}")
+        
+        # Convert response text to speech using gTTS
+        tts = gTTS(text=response_text, lang='en')
+        with tempfile.NamedTemporaryFile(delete=False, suffix='.mp3') as temp_audio_file:
+            response_audio_path = temp_audio_file.name
+            tts.save(response_audio_path)
+
+        # Ensure the temporary file is properly closed before returning the path
+        temp_audio_file.close()
+        
+        return response_text, response_audio_path
+    except Exception as e:
+        return f"Error: {str(e)}", None
+
+# Create Gradio interface with updated layout
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        <style>
+        .gradio-container {
+            font-family: Arial, sans-serif;
+            background-color: #e0f7fa;  /* Changed background color */
+            border-radius: 10px;
+            padding: 20px;
+            box-shadow: 0 4px 12px rgba(0,0,0,0.2);
+        }
+        .gradio-input, .gradio-output {
+            border-radius: 6px;
+            border: 1px solid #ddd;
+            padding: 10px;
+        }
+        .gradio-button {
+            background-color: #28a745;
+            color: white;
+            border-radius: 6px;
+            border: none;
+            padding: 8px 16px;  /* Adjusted padding */
+            font-size: 16px;  /* Adjusted font size */
+        }
+        .gradio-button:hover {
+            background-color: #218838;
+        }
+        .gradio-title {
+            font-size: 24px;
+            font-weight: bold;
+            margin-bottom: 20px;
+        }
+        .gradio-description {
+            font-size: 14px;
+            margin-bottom: 20px;
+            color: #555;
+        }
+        </style>
+        """
+    )
+    
+    gr.Markdown("# Voice-to-Voice Chatbot\nDeveloped by Salman Maqbool")
+    gr.Markdown("Upload an audio file to interact with the voice-to-voice chatbot. The chatbot will transcribe the audio, generate a response, and provide a spoken reply.")
+    
+    with gr.Row():
+        with gr.Column():
+            audio_input = gr.Audio(type="filepath", label="Upload Audio File")
+            submit_button = gr.Button("Submit")
+        
+        with gr.Column():
+            response_text = gr.Textbox(label="Response Text", placeholder="Generated response will appear here")
+            response_audio = gr.Audio(label="Response Audio", type="filepath")
+
+    submit_button.click(process_audio, inputs=audio_input, outputs=[response_text, response_audio])
+
+# Launch the Gradio app
+demo.launch()