Update app.py

app.py

@@ -1,25 +1,26 @@
 import os
 import random
+
 import gradio as gr
 from groq import Groq
 
-client = Groq(
-    api_key = os.environ.get("Groq_Api_Key")
-)
-    
+client = Groq(api_key=os.environ.get("Groq_Api_Key"))
+
+
 def create_history_messages(history):
     history_messages = [{"role": "user", "content": m[0]} for m in history]
     history_messages.extend([{"role": "assistant", "content": m[1]} for m in history])
     return history_messages
 
+
 def generate_response(prompt, history, model, temperature, max_tokens, top_p, seed):
     messages = create_history_messages(history)
     messages.append({"role": "user", "content": prompt})
     print(messages)
 
     if seed == 0:
-        seed = random.randint(1, 100000)
-    
+        seed = random.randint(1, 2**32-1)
+
     stream = client.chat.completions.create(
         messages=messages,
         model=model,
@@ -40,18 +41,125 @@ def generate_response(prompt, history, model, temperature, max_tokens, top_p, se
 
     return response
 
-additional_inputs = [
-    gr.Dropdown(choices=["llama3-70b-8192", "llama3-8b-8192", "mixtral-8x7b-32768", "gemma-7b-it", "gemma2-9b-it"], value="llama3-70b-8192", label="Model"),
-    gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.5, label="Temperature", info="Controls diversity of the generated text. Lower is more deterministic, higher is more creative."),
-    gr.Slider(minimum=1, maximum=32192, step=1, value=4096, label="Max Tokens", info="The maximum number of tokens that the model can process in a single response.<br>Maximums: 8k for gemma 7b it, gemma2 9b it, llama 7b & 70b, 32k for mixtral 8x7b."),
-    gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.5, label="Top P", info="A method of text generation where a model will only consider the most probable next tokens that make up the probability p."),
-    gr.Number(precision=0, value=42, label="Seed", info="A starting point to initiate generation, use 0 for random")
-]
-
-gr.ChatInterface(
-    fn=generate_response,
-    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
-    additional_inputs=additional_inputs,
-    title="Groq API UI",
-    description="Inference by Groq. Hugging Face Space by [Nick088](https://linktr.ee/Nick088)",
-).launch()
+
+def transcribe_audio(audio_file, prompt, language):
+    with open(audio_file.name, "rb") as file:
+        transcription = client.audio.transcriptions.create(
+            file=(audio_file.name, file.read()),
+            model="whisper-large-v3",
+            prompt=prompt,
+            response_format="json",
+            language=language,
+            temperature=0.0,
+        )
+    return transcription.text
+
+
+def translate_audio(audio_file, prompt):
+    with open(audio_file.name, "rb") as file:
+        translation = client.audio.translations.create(
+            file=(audio_file.name, file.read()),
+            model="whisper-large-v3",
+            prompt=prompt,
+            response_format="json",
+            temperature=0.0,
+        )
+    return translation.text
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+    # Groq API UI
+    Inference by Groq. Hugging Face Space by [Nick088](https://linktr.ee/Nick088)
+    """
+    )
+    with gr.Tabs():
+        with gr.TabItem("LLMs"):
+            with gr.Row():
+                with gr.Column():
+                    model = gr.Dropdown(
+                        choices=[
+                            "llama3-70b-8192",
+                            "llama3-8b-8192",
+                            "mixtral-8x7b-32768",
+                            "gemma-7b-it",
+                            "gemma2-9b-it",
+                        ],
+                        value="llama3-70b-8192",
+                        label="Model",
+                    )
+                    temperature = gr.Slider(
+                        minimum=0.0,
+                        maximum=1.0,
+                        step=0.01,
+                        value=0.5,
+                        label="Temperature",
+                        info="Controls diversity of the generated text. Lower is more deterministic, higher is more creative.",
+                    )
+                    max_tokens = gr.Slider(
+                        minimum=1,
+                        maximum=32192,
+                        step=1,
+                        value=4096,
+                        label="Max Tokens",
+                        info="The maximum number of tokens that the model can process in a single response.<br>Maximums: 8k for gemma 7b it, gemma2 9b it, llama 7b & 70b, 32k for mixtral 8x7b.",
+                    )
+                    top_p = gr.Slider(
+                        minimum=0.0,
+                        maximum=1.0,
+                        step=0.01,
+                        value=0.5,
+                        label="Top P",
+                        info="A method of text generation where a model will only consider the most probable next tokens that make up the probability p.",
+                    )
+                    seed = gr.Number(
+                        precision=0, value=42, label="Seed", info="A starting point to initiate generation, use 0 for random"
+                    )
+                with gr.Column():
+                    chatbot = gr.ChatInterface(
+                        fn=generate_response,
+                        chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
+                        additional_inputs=[model, temperature, max_tokens, top_p, seed],
+                    )
+        with gr.TabItem("Whisper"):
+            with gr.Tabs():
+                with gr.TabItem("Transcription"):
+                    with gr.Row():
+                        audio_input = gr.Audio(
+                            source="upload", type="filepath", label="Upload Audio"
+                        )
+                        transcribe_prompt = gr.Textbox(
+                            label="Prompt (Optional)",
+                            info="Specify any context or spelling corrections.",
+                        )
+                        language = gr.Dropdown(
+                            choices=["en", "es", "fr", "de", "zh", "ja", "ko"],  # Add more language codes as needed
+                            value="en",
+                            label="Language",
+                        )
+                    transcribe_button = gr.Button("Transcribe")
+                    transcription_output = gr.Textbox(label="Transcription")
+                    transcribe_button.click(
+                        transcribe_audio,
+                        inputs=[audio_input, transcribe_prompt, language],
+                        outputs=transcription_output,
+                    )
+                with gr.TabItem("Translation"):
+                    with gr.Row():
+                        audio_input_translate = gr.Audio(
+                            source="upload", type="filepath", label="Upload Audio"
+                        )
+                        translate_prompt = gr.Textbox(
+                            label="Prompt (Optional)",
+                            info="Specify any context or spelling corrections.",
+                        )
+                    translate_button = gr.Button("Translate")
+                    translation_output = gr.Textbox(label="Translation")
+                    translate_button.click(
+                        translate_audio,
+                        inputs=[audio_input_translate, translate_prompt],
+                        outputs=translation_output,
+                    )
+
+demo.launch()