Update app.py

app.py

@@ -2,7 +2,6 @@ import os
 import gradio as gr
 from argparse import ArgumentParser
 from groq import Groq
-from PIL import Image
 import base64
 import io
 
@@ -19,37 +18,51 @@ def _get_args():
     return parser.parse_args()
 
 def process_image(image):
-    # Convert image to bytes for Groq API
     buffered = io.BytesIO()
     image.save(buffered, format="JPEG")
     return buffered.getvalue()
 
-def create_messages(query, image_data):
-    messages = []
-    
-    # User query as text
-    if query:
-        messages.append({'role': 'user', 'content': query})
+def translate_audio(audio_file):
+    with open(audio_file, "rb") as file:
+        translation = client.audio.translations.create(
+            file=(audio_file, file.read()),
+            model="whisper-large-v3",
+            response_format="json",
+            temperature=0.0
+        )
+    return translation.text
 
-    # Include image if provided
-    if image_data:
-        image_base64 = f"data:image/jpeg;base64,{base64.b64encode(image_data).decode()}"
-        messages.append({
-            'role': 'user',
-            'content': [
-                {"type": "text", "text": "Please analyze this image."},
-                {"type": "image_url", "image_url": {"url": image_base64}}
-            ]
-        })
+def transcribe_audio(audio_file):
+    with open(audio_file, "rb") as file:
+        transcription = client.audio.transcriptions.create(
+            file=(audio_file, file.read()),
+            model="whisper-large-v3",
+            response_format="json",
+            temperature=0.0
+        )
+    return transcription.text
+
+def predict(chat_history, query, image, audio, translate):
+    final_query = query.strip()
     
-    return messages
+    if audio:
+        audio_file_path = audio
+        if translate:
+            translation_text = translate_audio(audio_file_path)
+            final_query = translation_text.strip()
+            chat_history.append({'role': 'assistant', 'content': translation_text})
+        else:
+            transcribed_text = transcribe_audio(audio_file_path)
+            final_query = f"{final_query} {transcribed_text}".strip()
 
-def predict(chat_history, query, image):
-    # Process the image if provided
     image_data = process_image(image) if image else None
-    messages = create_messages(query, image_data)
+    messages = create_messages(final_query, image_data)
+
+    if not messages:
+        error_message = "No valid input provided. Please enter a query or upload an image/audio."
+        chat_history.append({'role': 'assistant', 'content': error_message})
+        return chat_history
 
-    # Call the Groq API with the messages
     try:
         completion = client.chat.completions.create(
             model="llama-3.2-90b-vision-preview",
@@ -61,32 +74,53 @@ def predict(chat_history, query, image):
         )
 
         response_text = completion.choices[0].message.content.strip()
+        chat_history.append({'role': 'user', 'content': final_query})
+        chat_history.append({'role': 'assistant', 'content': response_text})
     except Exception as e:
         response_text = f"Error: {str(e)}"
+        chat_history.append({'role': 'user', 'content': final_query})
+        chat_history.append({'role': 'assistant', 'content': response_text})
     
-    chat_history.append((query, response_text))
     return chat_history
 
+def create_messages(query, image_data):
+    messages = []
+    if query:
+        messages.append({'role': 'user', 'content': query})
+    if image_data:
+        image_base64 = f"data:image/jpeg;base64,{base64.b64encode(image_data).decode()}"
+        messages.append({
+            'role': 'user',
+            'content': [
+                {"type": "text", "text": "Please analyze this image."},
+                {"type": "image_url", "image_url": {"url": image_base64}}
+            ]
+        })
+    return messages
+
 def clear_history():
     return []
 
 def main():
     args = _get_args()
     
-    with gr.Blocks() as demo:
-        gr.Markdown("<h1 style='text-align: center;'>Llama-3.2-90b-vision-preview</h1>")
+    with gr.Blocks(css="#chatbox {height: 400px; background-color: #f9f9f9; padding: 20px; border-radius: 10px; }") as demo:
+        gr.Markdown("<h1 style='text-align: center; color: #4a4a4a;'>Llama-3.2-90b-vision-preview</h1>")
         
-        chatbox = gr.Chatbot()
-        query = gr.Textbox(label="Input", placeholder="Type your query here...")
+        chatbox = gr.Chatbot(type='messages', elem_id="chatbox")
+        query = gr.Textbox(label="Type your query here...", placeholder="Enter your question or command...", lines=2)
         image_input = gr.Image(type="pil", label="Upload Image")
+        audio_input = gr.Audio(type="filepath", label="Upload Audio")
+        translate_checkbox = gr.Checkbox(label="Translate Audio to English Text")
+
+        with gr.Row():
+            submit_btn = gr.Button("Submit", variant="primary", elem_id="submit-btn")
+            clear_btn = gr.Button("Clear History", variant="secondary", elem_id="clear-btn")
         
-        submit_btn = gr.Button("Submit")
-        clear_btn = gr.Button("Clear History")
-        
-        submit_btn.click(predict, inputs=[chatbox, query, image_input], outputs=chatbox)
+        submit_btn.click(predict, inputs=[chatbox, query, image_input, audio_input, translate_checkbox], outputs=chatbox)
         clear_btn.click(clear_history, outputs=chatbox)
         
     demo.launch(share=args.share)
 
 if __name__ == '__main__':
-    main()
+    main()