chatbot.py

import os
import time
import copy
import requests
import random
from threading import Thread
from typing import List, Dict, Union
import subprocess
# 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,
)
import torch
import gradio as gr
from bs4 import BeautifulSoup
import datasets
from transformers import LlavaProcessor, LlavaForConditionalGeneration, TextIteratorStreamer
from huggingface_hub import InferenceClient
from PIL import Image
import spaces
from functools import lru_cache
import cv2
import re
import io  # Add this import for working with image bytes

# You can also use models that are commented below
# model_id = "llava-hf/llava-interleave-qwen-0.5b-hf"
# model_id = "llava-hf/llava-interleave-qwen-7b-hf"
model_id = "llava-hf/llava-interleave-qwen-7b-dpo-hf"
processor = LlavaProcessor.from_pretrained(model_id)
model = LlavaForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.float16, use_flash_attention_2=True, low_cpu_mem_usage=True)
model.to("cuda")
# Credit to merve for code of llava interleave qwen

def sample_frames(video_file, num_frames) :
    try:
        video = cv2.VideoCapture(video_file)
        total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
        fps = int(video.get(cv2.CAP_PROP_FPS))
        # extracts 5 images/sec of video
        if (total_frames/fps) < 3: 
            num_frames = 12
        else:
            num_frames = ((total_frames//fps)*5) 
        interval = total_frames // num_frames
        frames = []
        for i in range(total_frames):
            ret, frame = video.read()
            pil_img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
            if not ret:
                continue
            if i % interval == 0:
                frames.append(pil_img)
        video.release()
        return frames
    except:
        frames=[]
        return frames
        

# Path to example images
examples_path = os.path.dirname(__file__)
EXAMPLES = [
    [
        {
            "text": "Bitcoin price live",
        }
    ],
    [
        {
            "text": "Today News about AI",
        }
    ],
    [
        {
            "text": "Explain the cause of Accident",
            "files": [f"{examples_path}/example_video/accident.mp4"],
        }
    ],
    [
        {
            "text": "Who are they? Tell me about both of them",
            "files": [f"{examples_path}/example_images/elon_smoking.jpg",
                      f"{examples_path}/example_images/steve_jobs.jpg", ]
        }
    ],
    [
        {
            "text": "Create five images of supercars, each in a different color.",
        }
    ],
    [
        {
            "text": "Create a Photorealistic image of the Eiffel Tower.",
        }
    ],
    [
        {
            "text": "Chase wants to buy 4 kilograms of oval beads and 5 kilograms of star-shaped beads. How much will he spend?",
            "files": [f"{examples_path}/example_images/mmmu_example.jpeg"],
        }
    ],
    [
        {
            "text": "Create an online ad for this product.",
            "files": [f"{examples_path}/example_images/shampoo.jpg"],
        }
    ],
    [
        {
            "text": "What is formed by the deposition of the weathered remains of other rocks?",
            "files": [f"{examples_path}/example_images/ai2d_example.jpeg"],
        }
    ],
    [
        {
            "text": "What's unusual about this image?",
            "files": [f"{examples_path}/example_images/dragons_playing.png"],
        }
    ],
]

# Set bot avatar image
BOT_AVATAR = "OpenAI_logo.png"

# Perform a Google search and return the results
@lru_cache(maxsize=128) 
def extract_text_from_webpage(html_content):
    """Extracts visible text from HTML content using BeautifulSoup."""
    soup = BeautifulSoup(html_content, "html.parser")
    for tag in soup(["script", "style", "header", "footer", "nav", "form", "svg"]):
        tag.extract()
    visible_text = soup.get_text(strip=True)
    return visible_text

# Perform a Google search and return the results
def search(term, num_results=3, lang="en", advanced=True, timeout=5, safe="active", ssl_verify=None):
    """Performs a Google search and returns the results."""
    start = 0
    all_results = []
    # Limit the number of characters from each webpage to stay under the token limit
    max_chars_per_page = 8000  # Adjust this value based on your token limit and average webpage length
    
    with requests.Session() as session: 
        resp = session.get(  
            url="https://www.google.com/search",
            headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0"}, 
                params={
                    "q": term,
                    "num": num_results,
                    "udm": 14,
                },
                timeout=timeout,
                verify=ssl_verify,
        )
        resp.raise_for_status()
        soup = BeautifulSoup(resp.text, "html.parser")
        result_block = soup.find_all("div", attrs={"class": "g"})
        for result in result_block:
            link = result.find("a", href=True)
            if link:
                link = link["href"]
                try:
                    webpage = session.get(link, headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0"}) 
                    webpage.raise_for_status()
                    visible_text = extract_text_from_webpage(webpage.text)
                        # Truncate text if it's too long
                    if len(visible_text) > max_chars_per_page:
                        visible_text = visible_text[:max_chars_per_page]
                    all_results.append({"link": link, "text": visible_text})
                except requests.exceptions.RequestException as e:
                    print(f"Error fetching or processing {link}: {e}")
                    all_results.append({"link": link, "text": None})
            else:
                all_results.append({"link": None, "text": None}) 
    return all_results

# Format the prompt for the language model
def format_prompt(user_prompt, chat_history):
    prompt = "<s>"
    for item in chat_history:
        # Check if the item is a tuple (text response)
        if isinstance(item, tuple):
            prompt += f"[INST] {item[0]} [/INST]"  # User prompt
            prompt += f" {item[1]}</s> "           # Bot response
        # Otherwise, assume it's related to an image - you might need to adjust this logic
        else:
            # Handle image representation in the prompt, e.g., add a placeholder
            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

client_mixtral = InferenceClient("NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO")
client_mistral = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
generate_kwargs = dict( max_new_tokens=4000, do_sample=True, stream=True, details=True, return_full_text=False )

system_llava = "<|im_start|>system\nYou are OpenGPT 4o, an exceptionally capable and versatile AI assistant meticulously crafted by KingNish. Your task is to fulfill users query in best possible way. You are provided with image, videos and 3d structures as input with question your task is to give best possible result and explaination to user.<|im_end|>"

@spaces.GPU(duration=60, queue=False)
def model_inference(
        user_prompt,
        chat_history,
        web_search,
):
    # Define generation_args at the beginning of the function
    generation_args = {}  

    # Web search logic
    if not user_prompt["files"]:
        if web_search is True:
            
            gr.Info("Searching Web")
            
            web_results = search(user_prompt["text"])
            
            gr.Info("Extracting relevant Info")
            
            web2 = ' '.join([f"Link: {res['link']}\nText: {res['text']}\n\n" for res in web_results])
            
            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_mistral.text_generation(formatted_prompt, **generate_kwargs)
            output = ""
            
            gr.Info("Generating Answer")
            
            for response in stream:
                if not response.token.text == "</s>":
                    output += response.token.text
                yield output
        else:
            messages = f"<|im_start|>system\nYou 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.<|im_end|>"
    
            for msg in history:
                messages += f"\n<|im_start|>user\n{str(msg[0])}<|im_end|>"
                messages += f"\n<|im_start|>assistant\n{str(msg[1])}<|im_end|>"
        
            messages+=f"\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n"

            stream = client_mixtral.text_generation(messages, **generate_kwargs)
            output = ""
            # Construct the output from the stream of tokens
            for response in stream:
                if not response.token.text == "<|im_end|>":
                    output += response.token.text
                yield output
        update_history(output, user_prompt)
        print(history)
        return
    else:
        if user_prompt["files"]:
            image = user_prompt["files"][-1]
        else:
            for hist in history:
                if type(hist[0])==tuple:
                    image = hist[0][0]
    
        txt = user_prompt["text"]
        img = user_prompt["files"]
        ext_buffer =f"'user\ntext': '{txt}', 'files': '{img}' assistant"
    
        video_extensions = ("avi", "mp4", "mov", "mkv", "flv", "wmv", "mjpeg", "wav", "gif", "webm", "m4v", "3gp")
        image_extensions = Image.registered_extensions()
        image_extensions = tuple([ex for ex, f in image_extensions.items()])
        
        if image.endswith(video_extensions):
            image = sample_frames(image, 12)
            image_tokens = "<image>" * int(len(image))
            prompt = f"<|im_start|>user {image_tokens}\n{user_prompt}<|im_end|><|im_start|>assistant"
          
        elif image.endswith(image_extensions):
            image = Image.open(image).convert("RGB")
            prompt = f"<|im_start|>user <image>\n{user_prompt}<|im_end|><|im_start|>assistant"
    
        final_prompt = f"{system_llava}\n{prompt}"
        
        inputs = processor(prompt, image, return_tensors="pt").to("cuda", torch.float16)
        streamer = TextIteratorStreamer(processor, **{"skip_special_tokens": True})
        generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=2048, do_sample=True)
        generated_text = ""
    
        thread = Thread(target=model.generate, kwargs=generation_kwargs)
        thread.start()
    
        buffer = ""
        for new_text in streamer:
            buffer += new_text
            reply = buffer[len(ext_buffer):]
            yield reply
        update_history(reply, user_prompt)
        return

# Create a chatbot interface
chatbot = gr.Chatbot(
    label="OpenGPT-4o",
    avatar_images=[None, BOT_AVATAR],
    show_copy_button=True,
    likeable=True,
    layout="panel"
)
output = gr.Textbox(label="Prompt")