Update app.py

app.py

@@ -39,36 +39,38 @@ def bot_streaming(message, history):
       if type(hist[0])==tuple:
         image = hist[0][0]
 
+
     try:
-        if image is None:
-            # Handle the case where image is None
-            gr.Error("You need to upload an image for LLaVA to work.")
-        else:
-            prompt=f"<|start_header_id|>user<|end_header_id|>\n\n<image>\n{message['text']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
-            print(f"prompt: {prompt}")
-            image = Image.open(image)
-            inputs = processor(prompt, image, return_tensors='pt').to(0, torch.float16)
-            
-            streamer = TextIteratorStreamer(processor, **{"skip_special_tokens": True})
-            generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)
-            generated_text = ""
-            
-            thread = Thread(target=model.generate, kwargs=generation_kwargs)
-            thread.start()
-            
-            text_prompt =f"<|start_header_id|>user<|end_header_id|>\n\n{message['text']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
-            print(f"text_prompt: {text_prompt}")
-            
-            buffer = ""
-            for new_text in streamer:
-            
-            buffer += new_text
-            
-            generated_text_without_prompt = buffer[len(text_prompt):]
-            time.sleep(0.08)
-            yield generated_text_without_prompt
+    if image is None:
+        # Handle the case where image is None
+        gr.Error("You need to upload an image for LLaVA to work.")    
     except NameError:
-        gr.Error("You need to upload an image for LLaVA to work.")
+        # Handle the case where 'image' is not defined at all
+        gr.Error("You need to upload an image for LLaVA to work.")    
+        
+    prompt=f"<|start_header_id|>user<|end_header_id|>\n\n<image>\n{message['text']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+    print(f"prompt: {prompt}")
+    image = Image.open(image)
+    inputs = processor(prompt, image, return_tensors='pt').to(0, torch.float16)
+    
+    streamer = TextIteratorStreamer(processor, **{"skip_special_tokens": True})
+    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)
+    generated_text = ""
+    
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+    
+    text_prompt =f"<|start_header_id|>user<|end_header_id|>\n\n{message['text']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+    print(f"text_prompt: {text_prompt}")
+    
+    buffer = ""
+    for new_text in streamer:
+    
+    buffer += new_text
+    
+    generated_text_without_prompt = buffer[len(text_prompt):]
+    time.sleep(0.08)
+    yield generated_text_without_prompt
 
 
 demo = gr.ChatInterface(fn=bot_streaming, css=CSS, fill_height=True, title="LLaVA Llama-3-8B", examples=[{"text": "What is on the flower?", "files":["./bee.jpg"]},