Create app.py

app.py

@@ -0,0 +1,59 @@
+import os
+import whisper
+from gtts import gTTS
+import gradio as gr
+from groq import Groq
+from googletrans import Translator
+
+GROQ_API_KEY = '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'
+
+# Load Whisper model for transcription
+model = whisper.load_model("base")
+
+# Set up Groq API client
+client = Groq(api_key=GROQ_API_KEY)
+
+# Translator for English to Urdu translation
+translator = Translator()
+
+# Function to get the LLM response from Groq
+def get_llm_response(user_input):
+    chat_completion = client.chat.completions.create(
+        messages=[{"role": "user", "content": user_input}],
+        model="llama3-8b-8192",  # Replace with your desired model
+    )
+    return chat_completion.choices[0].message.content
+
+# Function to convert text to speech using gTTS
+def text_to_speech(text, output_audio="output_audio.mp3"):
+    tts = gTTS(text, lang='ur')
+    tts.save(output_audio)
+    return output_audio
+
+# Main chatbot function to handle audio input and output
+def chatbot(audio):
+    # Step 1: Transcribe the audio using Whisper
+    result = model.transcribe(audio)
+    user_text = result["text"]
+
+    # Step 2: Get LLM response from Groq
+    response_text = get_llm_response(user_text)
+
+    # Step 3: Translate response to Urdu
+    translated_text = translator.translate(response_text, src='en', dest='ur').text
+
+    # Step 4: Convert the translated text to speech
+    output_audio = text_to_speech(translated_text)
+
+    return translated_text, output_audio
+
+# Gradio interface for real-time interaction
+iface = gr.Interface(
+    fn=chatbot,
+    inputs=gr.Audio(type="filepath"),  # Input from mic or file
+    outputs=[gr.Textbox(), gr.Audio(type="filepath")],  # Output: response text and audio
+    live=True
+)
+
+# Launch the Gradio app
+iface.launch()