Update app.py

app.py

@@ -18,84 +18,23 @@ PLACEHOLDER = """
 </div>
 """
 
-model_id_llama3 = "xtuner/llava-llama-3-8b-v1_1-transformers"
-model_id_phi3 = "xtuner/llava-phi-3-mini-hf"
 
-processor = AutoProcessor.from_pretrained(model_id_llama3)
-processor = AutoProcessor.from_pretrained(model_id_phi3)
+model_id = "xtuner/llava-llama-3-8b-v1_1-transformers"
 
-model_llama3 = LlavaForConditionalGeneration.from_pretrained(
-    model_id_llama3,
-    torch_dtype=torch.float16,
-    low_cpu_mem_usage=True,
-)
-model_llama3.to("cuda:0")
-model_llama3.generation_config.eos_token_id = 128009
+processor = AutoProcessor.from_pretrained(model_id)
 
-model_phi3 = LlavaForConditionalGeneration.from_pretrained(
-    model_id_phi3,
+model = LlavaForConditionalGeneration.from_pretrained(
+    model_id,
     torch_dtype=torch.float16,
     low_cpu_mem_usage=True,
 )
-model_phi3.to("cuda:0")
-model_phi3.generation_config.eos_token_id = 128009
 
-
-@spaces.GPU
-def bot_streaming_llama3(message, history):
-    print(message)
-    if message["files"]:
-        # message["files"][-1] is a Dict or just a string
-        if type(message["files"][-1]) == dict:
-            image = message["files"][-1]["path"]
-        else:
-            image = message["files"][-1]
-    else:
-        # if there's no image uploaded for this turn, look for images in the past turns
-        # kept inside tuples, take the last one
-        for hist in 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.")
-    except NameError:
-        # 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": False, "skip_prompt": True})
-    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024, do_sample=False)
-
-    thread = Thread(target=model_llama3.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 = ""
-    time.sleep(0.5)
-    for new_text in streamer:
-        # find <|eot_id|> and remove it from the new_text
-        if "<|eot_id|>" in new_text:
-            new_text = new_text.split("<|eot_id|>")[0]
-        buffer += new_text
-
-        # generated_text_without_prompt = buffer[len(text_prompt):]
-        generated_text_without_prompt = buffer
-        # print(generated_text_without_prompt)
-        time.sleep(0.06)
-        # print(f"new_text: {generated_text_without_prompt}")
-        yield generated_text_without_prompt
+model.to("cuda:0")
+model.generation_config.eos_token_id = 128009
 
 
 @spaces.GPU
-def bot_streaming_phi3(message, history):
+def bot_streaming(message, history):
     print(message)
     if message["files"]:
         # message["files"][-1] is a Dict or just a string
@@ -125,7 +64,7 @@ def bot_streaming_phi3(message, history):
     streamer = TextIteratorStreamer(processor, **{"skip_special_tokens": False, "skip_prompt": True})
     generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024, do_sample=False)
 
-    thread = Thread(target=model_phi3.generate, kwargs=generation_kwargs)
+    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"
@@ -147,57 +86,20 @@ def bot_streaming_phi3(message, history):
         yield generated_text_without_prompt
 
 
-def print_like_dislike(x: gr.LikeData):
-    print(x.index, x.value, x.liked)
-#chatbot=gr.Chatbot(placeholder=PLACEHOLDER,scale=1)
-#chat_input = gr.MultimodalTextbox(interactive=True, file_types=["image"], placeholder="Enter message or upload file...", show_label=False)
-
+chatbot=gr.Chatbot(placeholder=PLACEHOLDER,scale=1)
+chat_input = gr.MultimodalTextbox(interactive=True, file_types=["image"], placeholder="Enter message or upload file...", show_label=False)
 with gr.Blocks(fill_height=True, ) as demo:
-  with gr.Row():
-    chatbot1 = gr.Chatbot(
-    [],
-    elem_id="llama3",
-    bubble_full_width=False,
-    label='LLaVa-Llama3'
-    )
-    chatbot2 = gr.Chatbot(
-    [],
-    elem_id="phi3",
-    bubble_full_width=False,
-    label='LLaVa-Phi3'
+    gr.ChatInterface(
+    fn=bot_streaming,
+    title="LLaVA Llama-3-8B",
+    examples=[{"text": "What is on the flower?", "files": ["./bee.jpg"]},
+              {"text": "How to make this pastry?", "files": ["./baklava.png"]}],
+    description="Try [LLaVA Llama-3-8B](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers). Upload an image and start chatting about it, or simply try one of the examples below. If you don't upload an image, you will receive an error.",
+    stop_btn="Stop Generation",
+    multimodal=True,
+    textbox=chat_input,
+    chatbot=chatbot,
     )
-      
-  chat_input = gr.MultimodalTextbox(interactive=True, file_types=["image"], placeholder="Enter message or upload file...", show_label=False)
-
-  gr.Examples(examples=[{"text": "What is on the flower?", "files": ["./bee.png"]},
-        {"text": "How to make this pastry?", "files": ["./baklava.png"]},],
-              inputs=chat_input)
-
-  #chat_input.submit(lambda: gr.MultimodalTextbox(interactive=False), None, [chat_input]).then(bot_streaming_llama3, [chat_input, chatbot1,], [chatbot1,])
-  
-  chat_msg1 = chat_input.submit(bot_streaming_llama3, [chat_input, chatbot1,], [chatbot1,])
-  chat_msg2 = chat_input.submit(bot_streaming_phi3, [chat_input, chatbot2,], [chatbot2,])
-
-  #bot_msg1 = chat_msg1.then(bot, chatbot1, chatbot1, api_name="bot_response1")
-  #chat_msg1.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])
-  #bot_msg2 = chat_msg2.then(bot, chatbot2, chatbot2, api_name="bot_response2")
-  #bot_msg2.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])
-
-  chatbot1.like(print_like_dislike, None, None)
-  chatbot2.like(print_like_dislike, None, None)
-
- 
-    #gr.ChatInterface(
-    #fn=bot_streaming_llama3,
-    #title="LLaVA Llama-3-8B",
-    #examples=[{"text": "What is on the flower?", "files": ["./bee.jpg"]},
-    #          {"text": "How to make this pastry?", "files": ["./baklava.png"]}],
-    #description="Try [LLaVA Llama-3-8B](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers). Upload an image and start chatting about it, or simply try one of the examples below. If you don't upload an image, you will receive an error.",
-    #stop_btn="Stop Generation",
-    #multimodal=True,
-    #textbox=chat_input,
-    #chatbot=chatbot,
-    #)
 
 demo.queue(api_open=False)
-demo.launch(show_api=False, share=False)
+demo.launch(show_api=False, share=False)