Create app.py

app.py

@@ -0,0 +1,69 @@
+import os
+import torch
+from transformers import pipeline
+from gtts import gTTS
+import gradio as gr
+from groq import Groq
+
+# Load Whisper model from Hugging Face
+try:
+    pipe = pipeline(model="openai/whisper-small", device="cuda" if torch.cuda.is_available() else "cpu")
+except Exception as e:
+    print(f"Error loading Whisper model: {e}")
+    raise
+
+GROQ_API_KEY = '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'
+
+Client = Groq(api_key=GROQ_API_KEY)
+
+# Function to get response from Groq LLM
+def get_llm_response(transcribed_text):
+    try:
+        chat_completion = client.chat.completions.create(
+            messages=[{"role": "user", "content": transcribed_text}],
+            model="llama3-8b-8192",
+        )
+        return chat_completion.choices[0].message.content
+    except Exception as e:
+        print(f"Error getting response from LLM: {e}")
+        return "Sorry, I couldn't process your request."
+
+# Function to convert text to speech
+def text_to_speech(response_text):
+    try:
+        tts = gTTS(response_text, lang='en')
+        tts.save("response_audio.mp3")
+        return "response_audio.mp3"  # Returning the file path
+    except Exception as e:
+        print(f"Error converting text to speech: {e}")
+        return "Sorry, I couldn't convert the response to audio."
+
+# Function to handle the entire voice chat process
+def voice_chat(audio_input):
+    try:
+        # Transcribe the input audio using Hugging Face Whisper model
+        result = pipe(audio_input)["text"]
+        transcribed_text = result
+        print(f"Transcribed Text: {transcribed_text}")
+
+        # Get the LLM response
+        response_text = get_llm_response(transcribed_text)
+        print(f"LLM Response: {response_text}")
+
+        # Convert the response text to speech and return the audio file
+        response_audio = text_to_speech(response_text)
+
+        return response_audio
+    except Exception as e:
+        print(f"Error in voice chat process: {e}")
+        return "Sorry, there was an error processing your audio."
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=voice_chat,
+    inputs=gr.Audio(type="filepath"),  # Specify input type only
+    outputs="audio"
+)
+
+# Launch the Gradio interface
+iface.launch()