Upload app.py

app.py

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+import time
+from threading import Thread
+
+import gradio as gr
+import spaces
+import torch
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForCausalLM
+from transformers import TextIteratorStreamer
+
+import subprocess
+subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
+
+# thanks to https://huggingface.co/ysharma
+PLACEHOLDER = """
+<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
+   <img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/models/microsoft/Phi-3-vision-128k-instruct.png" style="width: 80%; max-width: 550px; height: auto; opacity: 0.55;  "> 
+   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">Microsoft's Phi3-Vision-128k-Context</h1>
+   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Phi-3-Vision is a 4.2B parameter multimodal model that brings together language and vision capabilities.</p>
+</div>
+"""
+
+user_prompt = '<|user|>\n'
+assistant_prompt = '<|assistant|>\n'
+prompt_suffix = "<|end|>\n"
+
+
+
+model_id = "microsoft/Phi-3-vision-128k-instruct"
+
+processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    trust_remote_code=True,
+)
+
+model.to("cuda:0")
+
+
+@spaces.GPU
+def bot_streaming(message, history):
+    print(f'message is - {message}')
+    print(f'history is - {history}')
+    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
+            raise gr.Error("You need to upload an image for Phi3-Vision to work. Close the error and try again with an Image.")
+    except NameError:
+        # Handle the case where 'image' is not defined at all
+        raise gr.Error("You need to upload an image for Phi3-Vision to work. Close the error and try again with an Image.")
+
+    conversation = []
+    flag=False
+    for user, assistant in history:
+        if assistant is None:
+            #pass
+            flag=True
+            conversation.extend([{"role": "user", "content":""}])
+            continue
+        if flag==True:
+            conversation[0]['content'] = f"<|image_1|>\n{user}"   
+            conversation.extend([{"role": "assistant", "content": assistant}])
+            flag=False
+            continue
+        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
+
+    if len(history) == 0:
+        conversation.append({"role": "user", "content": f"<|image_1|>\n{message['text']}"})
+    else:
+        conversation.append({"role": "user", "content": message['text']})
+    print(f"prompt is -\n{conversation}")
+    prompt = processor.tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
+    image = Image.open(image)
+    inputs = processor(prompt, image, return_tensors="pt").to("cuda:0") 
+
+    streamer = TextIteratorStreamer(processor, **{"skip_special_tokens": True, "skip_prompt": True, 'clean_up_tokenization_spaces':False,}) 
+    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024, do_sample=False, temperature=0.0, eos_token_id=processor.tokenizer.eos_token_id,)
+
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+
+    buffer = ""
+    for new_text in streamer:
+        buffer += new_text
+        yield buffer
+
+
+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:
+    gr.ChatInterface(
+        fn=bot_streaming,
+        title="Phi-3 Vision 128k Instruct",
+        examples=[{"text": "What is on the flower?", "files": ["./bee.jpg"]},
+                  {"text": "How to make this pastry?", "files": ["./baklava.png"]}],
+        description="Try [microsoft/Phi-3-vision-128k-instruct](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct). 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)