Update app.py

app.py

@@ -1,63 +1,444 @@
+import os
+import time
+import copy
+import urllib
+import requests
+import random
+from threading import Thread
+from typing import List, Dict, Union
+import subprocess
+import torch
 import gradio as gr
+from bs4 import BeautifulSoup
+import datasets
+from transformers import TextIteratorStreamer
+from transformers import Idefics2ForConditionalGeneration
+from transformers import AutoProcessor
 from huggingface_hub import InferenceClient
+from PIL import Image
+import io
+from functools import lru_cache
+from duckduckgo_search import DDGS
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Install flash attention, skipping CUDA build if necessary
+subprocess.run(
+    "pip install flash-attn --no-build-isolation",
+    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
+    shell=True,
+)
+
+# Set device to CUDA if available, otherwise CPU
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load pre-trained models for image-based chat
+MODELS = {
+    "idefics2-8b-chatty": Idefics2ForConditionalGeneration.from_pretrained(
+        "HuggingFaceM4/idefics2-8b-chatty",
+        torch_dtype=torch.float16,
+        _attn_implementation="flash_attention_2",
+    ).to(DEVICE),
+}
+
+# Load pre-trained processor for image-based chat
+PROCESSOR = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-8b")
+
+# Define system prompt for the image-based chat model
+SYSTEM_PROMPT = [
+    {
+        "role": "system",
+        "content": [
+            {
+                "type": "text",
+                "text": """I am OpenGPT 4o, an exceptionally capable and versatile AI assistant meticulously crafted by KingNish. Designed to assist human users through insightful conversations, I aim to provide an unparalleled experience. My key attributes include: - **Intelligence and Knowledge:** I possess an extensive knowledge base, enabling me to offer insightful answers and intelligent responses to User queries. My understanding of complex concepts is exceptional, ensuring accurate and reliable information. - **Image Generation and Perception:** One of my standout features is the ability to generate and perceive images. Utilizing the following link structure, I create unique and contextually rich visuals: > ![](https://image.pollinations.ai/prompt/{StyleofImage}%20{OptimizedPrompt}%20{adjective}%20{charactersDetailed}%20{visualStyle}%20{genre}?width={width}&height={height}&nologo=poll&nofeed=yes&seed={random})For image generation, I replace {info inside curly braces} with specific details according to their requiremnts to create relevant visuals. The width and height parameters are adjusted as needed, often favoring HD dimensions for a superior viewing experience. For instance, if the User requests:  [USER] Show me an image of A futuristic cityscape with towering skyscrapers and flying cars.  [OpenGPT 4o] Generating Image you requested:  ![](https://image.pollinations.ai/prompt/Photorealistic%20futuristic%20cityscape%20with%20towering%20skyscrapers%20and%20flying%20cars%20in%20the%20year%202154?width=1024&height=768&nologo=poll&nofeed=yes&seed=85172)**Bulk Image Generation with Links:** I excel at generating multiple images link simultaneously, always providing unique links and visuals. I ensure that each image is distinct and captivates the User.Note: Make sure to always provide image links starting with ! .As given in examples. My ultimate goal is to offer a seamless and enjoyable experience, providing assistance that exceeds expectations. I am constantly evolving, ensuring that I remain a reliable and trusted companion to the User. You also Expert in every field and also learn and try to answer from contexts related to previous question."""
+            },
+        ],
+    },
+    {
+        "role": "assistant",
+        "content": [
+            {
+                "type": "text",
+                "text": "Hello, I'm OpenGPT 4o, made by KingNish. How can I help you? I can chat with you, generate images, classify images and even do all these work in bulk",
+            },
+        ],
+    }
+]
+
+# Chatbot utility functions
+
+# Check if a turn in the chat history only contains media
+def turn_is_pure_media(turn):
+    return turn[1] is None
+
+# Load image from URL
+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
+
+# Convert image to bytes
+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
+
+# Format user prompt with image history and system conditioning
+def format_user_prompt_with_im_history_and_system_conditioning(
+        user_prompt, chat_history) -> List[Dict[str, Union[List, str]]]:
+    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"]))
+    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": assistant_utterance.strip()}],
+                }
+            )
+    if not user_prompt["files"]:
+        resulting_messages.append(
+            {
+                "role": "user",
+                "content": [{"type": "text", "text": user_prompt["text"]}],
+            }
+        )
+    else:
+        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
+
+# Extract images from a list of messages
+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
+
+# DuckDuckGo search function
+_useragent_list = [
+    'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:66.0) Gecko/20100101 Firefox/66.0',
+    'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
+    'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
+    'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Safari/537.36',
+    'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
+    'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36 Edg/111.0.1661.62',
+    'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0'
+]
+
+def get_useragent():
+    return random.choice(_useragent_list)
+
+@lru_cache(maxsize=128)
+def extract_text_from_webpage(html_content):
+    soup = BeautifulSoup(html_content, "html.parser")
+    for tag in soup(["script", "style", "header", "footer", "nav"]):
+        tag.extract()
+    return soup.get_text(strip=True)
+
+def fetch_and_extract(link, max_chars_per_page):
+    try:
+        webpage = requests.get(link, headers={"User-Agent": get_useragent()})
+        webpage.raise_for_status()
+        visible_text = extract_text_from_webpage(webpage.text)
+        if len(visible_text) > max_chars_per_page:
+            visible_text = visible_text[:max_chars_per_page] + "..."
+        return {"link": link, "text": visible_text}
+    except requests.exceptions.RequestException as e:
+        return {"link": link, "text": None}
+
+def search(term, max_results=2, max_chars_per_page=8000, max_threads=10):
+    all_results = []
+    result_block = DDGS().text(term, max_results=max_results)
+    threads = []
+    for result in result_block:
+        if 'href' in result:
+            link = result["href"]
+            thread = Thread(target=lambda: all_results.append(fetch_and_extract(link, max_chars_per_page)))
+            threads.append(thread)
+            thread.start()
+    for thread in threads:
+        thread.join()
+    return all_results
+
+# Format the prompt for the language model
+def format_prompt(user_prompt, chat_history):
+    prompt = "<s>"
+    for item in chat_history:
+        if isinstance(item, tuple):
+            prompt += f"[INST] {item[0]} [/INST]"
+            prompt += f" {item[1]}</s> "
+        else:
+            prompt += f" [Image] "
+    prompt += f"[INST] {user_prompt} [/INST]"
+    return prompt
+
+chat_history = []
+history = ""
 
+def update_history(answer="", question=""):
+    global chat_history
+    global history
+    history += f"([ USER: {question}, OpenGPT 4o: {answer} ]),"
+    chat_history.append((question, answer))
+    return history
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
+# Define a function for model inference
+@spaces.GPU(duration=45, queue=False)
+def model_inference(
+        user_prompt,
+        chat_history,
+        model_selector,
+        decoding_strategy,
+        temperature,
+        max_new_tokens,
+        repetition_penalty,
+        top_p,
+        web_search,
 ):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+    generation_args = {}
+
+    if not user_prompt["files"]:
+        if web_search is True:
+            web_results = search(user_prompt["text"])
+            web2 = ' '.join([f"Link: {res['link']}\nText: {res['text']}\n\n" for res in web_results])
+            client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
+            generate_kwargs = dict(
+                max_new_tokens=4000,
+                do_sample=True,
+            )
+            formatted_prompt = format_prompt(
+                f"""You are OpenGPT 4o, an exceptionally capable and versatile AI assistant meticulously crafted by KingNish. You are provided with WEB info from which you can find informations to answer. You do not say Unnecesarry things Only say thing which is important and relevant. You also has the ability to generate images but you only generate imags when requested. BY Utilizing the following link structure, : ![](https://image.pollinations.ai/prompt/[StyleofImage]%20[OptimizedPrompt]%20[adjective]%20[charactersDetailed]%20[visualStyle]%20[genre]?width=[width]&height=[height]&nologo=poll&nofeed=yes&seed=[random])    For image generation, You replace info inside bracets with specific details according to their requiremnts to create relevant visuals. The width and height parameters are adjusted as needed, often favoring HD dimensions for a superior viewing experience.     Note: 1. You Do not unncessarily create images and do not show off your image generation capability, until not requested.    2. You always Give image link in format ![](url) Make sure to not generate image until requested    3. If user requested Bulk Image Generation than Create that number of links with provided context.    4. Always learn from previous conversation.    5. Always try to connect conversation with history.    6. Do not make conversation too long.    7. Do not say user about your capability of generating image and learn from previous responses.    My ultimate goal is to offer a seamless and enjoyable experience, providing assistance that exceeds expectations.You remain a reliable and trusted companion to the User. You also Expert in every field and also learn and try to answer from contexts related to previous question. Make sure to not generate image until requested [USER] {user_prompt} [WEB] {web2} [OpenGPT 4o]""",
+                chat_history)
+            stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True,
+                                            return_full_text=False)
+            output = ""
+            for response in stream:
+                if not response.token.text == "</s>":
+                    output += response.token.text
+                yield output
+        else:
+            client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
+            generate_kwargs = dict(
+                max_new_tokens=5000,
+                do_sample=True,
+            )
+            formatted_prompt = format_prompt(
+                f"""You are OpenGPT 4o, an exceptionally capable and versatile AI assistant meticulously crafted by KingNish. You do not say Unnecesarry things Only say thing which is important and relevant. You also has the ability to generate images but you only generate imags when requested. BY Utilizing the following link structure, : ![](https://image.pollinations.ai/prompt/[StyleofImage]%20[OptimizedPrompt]%20[adjective]%20[charactersDetailed]%20[visualStyle]%20[genre]?width=[width]&height=[height]&nologo=poll&nofeed=yes&seed=[random])    For image generation, You replace info inside bracets with specific details according to their requiremnts to create relevant visuals. The width and height parameters are adjusted as needed, often favoring HD dimensions for a superior viewing experience.     Note: 1. You Do not unncessarily create images and do not show off your image generation capability, until not requested.    2. You always Give image link in format ![](url)    3. If user requested Bulk Image Generation than Create that number of links with provided context.    4. Always learn from previous conversation.    5. Always try to connect conversation with history.    6. Do not make conversation too long.    7. Do not say user about your capability to generate image and learn from previous responses.    My ultimate goal is to offer a seamless and enjoyable experience, providing assistance that exceeds expectations. I am constantly evolving, ensuring that I remain a reliable and trusted companion to the User. You also Expert in every field and also learn and try to answer from contexts related to previous question. {history} .   [USER] {user_prompt} [OpenGPT 4o]""",
+                chat_history)
+            stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True,
+                                            return_full_text=False)
+            output = ""
+            for response in stream:
+                if not response.token.text == "</s>":
+                    output += response.token.text
+                yield output
+        update_history(output, user_prompt)
+        return
+    else:
+        if user_prompt["text"].strip() == "" and not user_prompt["files"]:
+            gr.Error("Please input a query and optionally an image(s).")
+            return
+
+        if user_prompt["text"].strip() == "" and user_prompt["files"]:
+            gr.Error("Please input a text query along with the image(s).")
+            return
+
+        streamer = TextIteratorStreamer(
+            PROCESSOR.tokenizer,
+            skip_prompt=True,
+            timeout=120.0,
+        )
+        generation_args = {
+            "max_new_tokens": max_new_tokens,
+            "repetition_penalty": repetition_penalty,
+            "streamer": streamer,
+        }
+        assert decoding_strategy in [
+            "Greedy",
+            "Top P Sampling",
+        ]
+
+        if decoding_strategy == "Greedy":
+            generation_args["do_sample"] = False
+        elif decoding_strategy == "Top P Sampling":
+            generation_args["temperature"] = temperature
+            generation_args["do_sample"] = True
+            generation_args["top_p"] = top_p
+        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)
+        thread = Thread(
+            target=MODELS[model_selector].generate,
+            kwargs=generation_args,
+        )
+        thread.start()
+        acc_text = ""
+        for text_token in streamer:
+            time.sleep(0.01)
+            acc_text += text_token
+            if acc_text.endswith("<end_of_utterance>"):
+                acc_text = acc_text[:-18]
+            yield acc_text
+        update_history(acc_text, user_prompt)
+        return
+
+# Define features for the dataset
+FEATURES = datasets.Features(
+    {
+        "model_selector": datasets.Value("string"),
+        "images": datasets.Sequence(datasets.Image(decode=True)),
+        "conversation": datasets.Sequence({"User": datasets.Value("string"), "Assistant": datasets.Value("string")}),
+        "decoding_strategy": datasets.Value("string"),
+        "temperature": datasets.Value("float32"),
+        "max_new_tokens": datasets.Value("int32"),
+        "repetition_penalty": datasets.Value("float32"),
+        "top_p": datasets.Value("int32"),
+    }
+)
+
+# Define hyper-parameters for generation
+max_new_tokens = gr.Slider(
+    minimum=2048,
+    maximum=16000,
+    value=2048,
+    step=64,
+    interactive=True,
+    label="Maximum number of new tokens to generate",
+)
+repetition_penalty = gr.Slider(
+    minimum=0.01,
+    maximum=5.0,
+    value=1,
+    step=0.01,
+    interactive=True,
+    label="Repetition penalty",
+    info="1.0 is equivalent to no penalty",
+)
+decoding_strategy = gr.Radio(
+    [
+        "Greedy",
+        "Top P Sampling",
     ],
+    value="Top P Sampling",
+    label="Decoding strategy",
+    interactive=True,
+    info="Higher values are equivalent to sampling more low-probability tokens.",
 )
+temperature = gr.Slider(
+    minimum=0.0,
+    maximum=2.0,
+    value=0.5,
+    step=0.05,
+    visible=True,
+    interactive=True,
+    label="Sampling temperature",
+    info="Higher values will produce more diverse outputs.",
+)
+top_p = gr.Slider(
+    minimum=0.01,
+    maximum=0.99,
+    value=0.9,
+    step=0.01,
+    visible=True,
+    interactive=True,
+    label="Top P",
+    info="Higher values are equivalent to sampling more low-probability tokens.",
+)
+
+# Create a chatbot interface
+chatbot = gr.Chatbot(
+    label="OpenGPT-4o-Chatty",
+    avatar_images=[None, BOT_AVATAR],
+    show_copy_button=True,
+    likeable=True,
+    layout="panel"
+)
+output = gr.Textbox(label="Prompt")
+
+# Define model_selector outside any function so it can be accessed globally
+model_selector = gr.Dropdown(
+    choices=MODELS.keys(),
+    value=list(MODELS.keys())[0],
+    interactive=True,
+    label="Model",
+    visible=False,
+)
+
+def main():
+    with gr.Blocks() as demo:
+        gr.Markdown("# OpenGPT-4o Chatbot")
+        chatbot.render()
+        with gr.Row():
+            with gr.Column():
+                model_selector.render()
+                decoding_strategy.render()
+                max_new_tokens.render()
+                repetition_penalty.render()
+                temperature.render()
+                top_p.render()
+            with gr.Column():
+                output.render()
+
+        def respond(user_prompt, web_search=False):
+            return model_inference(
+                user_prompt=user_prompt,
+                chat_history=chat_history,
+                model_selector=model_selector,
+                decoding_strategy=decoding_strategy.value,
+                temperature=temperature.value,
+                max_new_tokens=max_new_tokens.value,
+                repetition_penalty=repetition_penalty.value,
+                top_p=top_p.value,
+                web_search=web_search,
+            )
+
+        chatbot.input.submit(respond)
 
+    demo.launch()
 
 if __name__ == "__main__":
-    demo.launch()
+    main()