Update app.py

app.py

@@ -1,19 +1,268 @@
+import os
+import subprocess
+
+# Install flash attention
+subprocess.run(
+    "pip install flash-attn --no-build-isolation",
+    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
+    shell=True,
+)
+
+
+import copy
+import spaces
+import time
+import numpy as np
+import torch
+
+from threading import Thread
+from typing import List, Dict, Union
+import urllib
+from PIL import Image
+import io
+import datasets
+
 import gradio as gr
+from transformers import AutoProcessor, TextIteratorStreamer
+from transformers import Idefics2ForConditionalGeneration
+
+
+DEVICE = torch.device("cuda")
+MODELS = {
+    "idefics2-8b-chatty": Idefics2ForConditionalGeneration.from_pretrained(
+        "HuggingFaceM4/idefics2-8b-chatty",
+        torch_dtype=torch.bfloat16,
+        _attn_implementation="flash_attention_2",
+    ).to(DEVICE),
+}
+PROCESSOR = AutoProcessor.from_pretrained(
+    "HuggingFaceM4/idefics2-8b",
+)
+
+SYSTEM_PROMPT = [
+    {
+        "role": "system",
+        "content": [
+            {
+                "type": "text",
+                "text": "The following is a conversation between Idefics2, a highly knowledgeable and intelligent visual AI assistant created by Hugging Face, referred to as Assistant, and a human user called User. In the following interactions, User and Assistant will converse in natural language, and Assistant will do its best to answer User’s questions. Assistant has the ability to perceive images and reason about them, but it cannot generate images. Assistant was built to be respectful, polite and inclusive. It knows a lot, and always tells the truth. When prompted with an image, it does not make up facts.",
+            },
+        ],
+    },
+    {
+        "role": "assistant",
+        "content": [
+            {
+                "type": "text",
+                "text": "Hello, I'm Idefics2, Huggingface's latest multimodal assistant. How can I help you?",
+            },
+        ],
+    }
+]
+examples_path = os.path.dirname(__file__)
+EXAMPLES = [
+    [
+        {
+            "text": "For 2024, the interest expense is twice what it was in 2014, and the long-term debt is 10% higher than its 2015 level. Can you calculate the combined total of the interest and long-term debt for 2024?",
+            "files": [f"{examples_path}/example_images/mmmu_example_2.png"],
+        }
+    ],
+]
+
+BOT_AVATAR = "IDEFICS_logo.png"
+
+
+# Chatbot utils
+def turn_is_pure_media(turn):
+    return turn[1] is None
+
+
+def load_image_from_url(url):
+    with urllib.request.urlopen(url) as response:
+        image_data = response.read()
+        image_stream = io.BytesIO(image_data)
+        image = Image.open(image_stream)
+        return image
+
+
+def img_to_bytes(image_path):
+    image = Image.open(image_path).convert(mode='RGB')
+    buffer = io.BytesIO()
+    image.save(buffer, format="JPEG")
+    img_bytes = buffer.getvalue()
+    image.close()
+    return img_bytes
+
+
+def format_user_prompt_with_im_history_and_system_conditioning(
+    user_prompt, chat_history
+) -> List[Dict[str, Union[List, str]]]:
+    """
+    Produces the resulting list that needs to go inside the processor.
+    It handles the potential image(s), the history and the system conditionning.
+    """
+    resulting_messages = copy.deepcopy(SYSTEM_PROMPT)
+    resulting_images = []
+    for resulting_message in resulting_messages:
+        if resulting_message["role"] == "user":
+            for content in resulting_message["content"]:
+                if content["type"] == "image":
+                    resulting_images.append(load_image_from_url(content["image"]))
+
+    # Format history
+    for turn in chat_history:
+        if not resulting_messages or (
+            resulting_messages and resulting_messages[-1]["role"] != "user"
+        ):
+            resulting_messages.append(
+                {
+                    "role": "user",
+                    "content": [],
+                }
+            )
+
+        if turn_is_pure_media(turn):
+            media = turn[0][0]
+            resulting_messages[-1]["content"].append({"type": "image"})
+            resulting_images.append(Image.open(media))
+        else:
+            user_utterance, assistant_utterance = turn
+            resulting_messages[-1]["content"].append(
+                {"type": "text", "text": user_utterance.strip()}
+            )
+            resulting_messages.append(
+                {
+                    "role": "assistant",
+                    "content": [{"type": "text", "text": user_utterance.strip()}],
+                }
+            )
+
+    # Format current input
+    if not user_prompt["files"]:
+        resulting_messages.append(
+            {
+                "role": "user",
+                "content": [{"type": "text", "text": user_prompt["text"]}],
+            }
+        )
+    else:
+        # Choosing to put the image first (i.e. before the text), but this is an arbiratrary choice.
+        resulting_messages.append(
+            {
+                "role": "user",
+                "content": [{"type": "image"}] * len(user_prompt["files"])
+                + [{"type": "text", "text": user_prompt["text"]}],
+            }
+        )
+        resulting_images.extend([Image.open(path) for path in user_prompt["files"]])
+
+    return resulting_messages, resulting_images
+
+
+def extract_images_from_msg_list(msg_list):
+    all_images = []
+    for msg in msg_list:
+        for c_ in msg["content"]:
+            if isinstance(c_, Image.Image):
+                all_images.append(c_)
+    return all_images
+
+
+@spaces.GPU(duration=180)
+def model_inference(
+    user_prompt,
+    chat_history,
+    model_selector,
+    temperature,
+    top_p,
+):
+    if not user_prompt["files"]:
+        gr.Error("Please give me a picture of someone to rate!")
+
+    streamer = TextIteratorStreamer(
+        PROCESSOR.tokenizer,
+        skip_prompt=True,
+        timeout=5.0,
+    )
+
+    # Common parameters to all decoding strategies
+    # This documentation is useful to read: https://huggingface.co/docs/transformers/main/en/generation_strategies
+    generation_args = {
+        "max_new_tokens": 512,
+        "repetition_penalty": 1.1,
+        "streamer": streamer,
+    }
+
+    generation_args["temperature"] = temperature
+    generation_args["do_sample"] = True
+    generation_args["top_p"] = top_p
+
+    # Creating model inputs
+    (
+        resulting_text,
+        resulting_images,
+    ) = format_user_prompt_with_im_history_and_system_conditioning(
+        user_prompt=user_prompt,
+        chat_history=chat_history,
+    )
+    prompt = PROCESSOR.apply_chat_template(resulting_text, add_generation_prompt=True)
+    inputs = PROCESSOR(
+        text=prompt,
+        images=resulting_images if resulting_images else None,
+        return_tensors="pt",
+    )
+    inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
+    generation_args.update(inputs)
+
+    # # The regular non streaming generation mode
+    # _ = generation_args.pop("streamer")
+    # generated_ids = MODELS[model_selector].generate(**generation_args)
+    # generated_text = PROCESSOR.batch_decode(generated_ids[:, generation_args["input_ids"].size(-1): ], skip_special_tokens=True)[0]
+    # return generated_text
+
+    # The streaming generation mode
+    thread = Thread(
+        target=MODELS[model_selector].generate,
+        kwargs=generation_args,
+    )
+    thread.start()
+
+    print("Start generating")
+    acc_text = ""
+    for text_token in streamer:
+        time.sleep(0.04)
+        acc_text += text_token
+        if acc_text.endswith("<end_of_utterance>"):
+            acc_text = acc_text[:-18]
+        yield acc_text
+    print("Success - generated the following text:", acc_text)
+    print("-----")
+
+
+chatbot = gr.Chatbot(
+    label="Idefics2-Chatty",
+    avatar_images=[None, BOT_AVATAR],
+    height=450,
+)
 
-max_textboxes = 10
+with gr.Blocks(
+    fill_height=True,
+    css=""".gradio-container .avatar-container {height: 40px width: 40px !important;} #duplicate-button {margin: auto; color: white; background: #f1a139; border-radius: 100vh; margin-top: 2px; margin-bottom: 2px;}""",
+) as demo:
 
-def variable_outputs(k):
-    k = int(k)
-    return [gr.Textbox(visible=True)]*k + [gr.Textbox(visible=False)]*(max_textboxes-k)
+    gr.Markdown("# 🐶 Hugging Face Idefics2 8B Chatty")
+    gr.Markdown("In this demo you'll be able to chat with [Idefics2-8B-chatty](https://huggingface.co/HuggingFaceM4/idefics2-8b-chatty), a variant of [Idefics2-8B](https://huggingface.co/HuggingFaceM4/idefics2-8b-chatty) further fine-tuned on chat datasets.")
+    gr.Markdown("If you want to learn more about Idefics2 and its variants, you can check our [blog post](https://huggingface.co/blog/idefics2).")
+    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
+    # model selector should be set to `visbile=False` ultimately
 
-with gr.Blocks() as demo:
-    s = gr.Slider(1, max_textboxes, value=max_textboxes, step=1, label="How many textboxes to show:")
-    textboxes = []
-    for i in range(max_textboxes):
-        t = gr.Textbox(f"Textbox {i}")
-        textboxes.append(t)
 
-    s.change(variable_outputs, s, textboxes)
+    gr.ChatInterface(
+        fn=model_inference,
+        chatbot=chatbot,
+        examples=EXAMPLES,
+        multimodal=True,
+        cache_examples=False,
+    )
 
-if __name__ == "__main__":
-   demo.launch()
+demo.launch()