Update app.py

app.py

@@ -26,7 +26,7 @@ from tqdm import tqdm
 import json
 
 root_path = os.path.dirname(os.path.abspath(__file__))
-print(f'\033[92m{root_path}\033[0m')
+print(root_path)
 os.environ['GRADIO_TEMP_DIR'] = root_path
 
 parser = argparse.ArgumentParser()
@@ -52,6 +52,7 @@ tokenizer, llava_model, image_processor, context_len = load_pretrained_model(
 
 @spaces.GPU
 def bot_streaming(message, history):
+    print ("triggered")
     print(message)
     image_file = None
     if message["files"]:
@@ -70,7 +71,7 @@ def bot_streaming(message, history):
     
     streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
     def generate():
-        print('\033[92mRunning chat\033[0m')
+        print('Running chat')
         output = chat_llava(
                     args=args,
                     image_file=image_file,
@@ -92,6 +93,7 @@ def bot_streaming(message, history):
         buffer += new_text
         generated_text_without_prompt = buffer
         time.sleep(0.06)
+        print (generated_text_without_prompt)
         yield generated_text_without_prompt
 
 chatbot = gr.Chatbot(scale=1)
@@ -104,8 +106,6 @@ with gr.Blocks(fill_height=True) as demo:
             {"text": "What is in this picture?", "files": ["http://images.cocodataset.org/val2017/000000039769.jpg"]},
             {"text": "What is the spending on Healthcare in July? A. 450 B. 600 C. 520 D. 510", "files": ["image_107.png"]},
             {"text": "If 2012 net periodic opeb cost increased at the same pace as the pension cost, what would the estimated 2013 cost be in millions? A. 14.83333 B. 12.5 C. 15.5 D. 13.5", "files": ["image_659.png"]},
-
-
         ],
         description="",
         stop_btn="Stop Generation",