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| import os | |
| import time | |
| import requests | |
| import random | |
| from threading import Thread | |
| from typing import List, Dict, Union | |
| import subprocess | |
| # 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 | |
| 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 | |
| import json | |
| from gradio_client import Client, file | |
| from groq import Groq | |
| # 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) | |
| model.to("cpu") | |
| # Credit to merve for code of llava interleave qwen | |
| GROQ_API_KEY = os.environ.get("GROQ_API_KEY", None) | |
| client_groq = Groq(api_key=GROQ_API_KEY) | |
| def sample_frames(video_file) : | |
| try: | |
| video = cv2.VideoCapture(video_file) | |
| total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) | |
| num_frames = 12 | |
| 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": "What is Friction? Explain in Detail.", | |
| } | |
| ], | |
| [ | |
| { | |
| "text": "Write me a Python function to generate unique passwords.", | |
| } | |
| ], | |
| [ | |
| { | |
| "text": "What's the latest price of Bitcoin?", | |
| } | |
| ], | |
| [ | |
| { | |
| "text": "Search and give me list of spaces trending on HuggingFace.", | |
| } | |
| ], | |
| [ | |
| { | |
| "text": "Create a Beautiful Picture of Effiel at Night.", | |
| } | |
| ], | |
| [ | |
| { | |
| "text": "Create image of cute cat.", | |
| } | |
| ], | |
| [ | |
| { | |
| "text": "What unusual happens in this video.", | |
| "files": [f"{examples_path}/example_video/accident.gif"], | |
| } | |
| ], | |
| [ | |
| { | |
| "text": "What's name of superhero in this clip", | |
| "files": [f"{examples_path}/example_video/spiderman.gif"], | |
| } | |
| ], | |
| [ | |
| { | |
| "text": "What's written on this paper", | |
| "files": [f"{examples_path}/example_images/paper_with_text.png"], | |
| } | |
| ], | |
| [ | |
| { | |
| "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", ] | |
| } | |
| ] | |
| ] | |
| # Set bot avatar image | |
| BOT_AVATAR = "OpenAI_logo.png" | |
| # Perform a Google search and return the results | |
| 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(query): | |
| term = query | |
| start = 0 | |
| all_results = [] | |
| max_chars_per_page = 8000 | |
| 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": 4, "udm": 14}, | |
| timeout=5, | |
| verify=None, | |
| ) | |
| 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) | |
| 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"}, timeout=5, verify=False) | |
| 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] | |
| all_results.append({"link": link, "text": visible_text}) | |
| except requests.exceptions.RequestException: | |
| all_results.append({"link": link, "text": None}) | |
| return all_results | |
| # def image_gen(prompt): | |
| # client = Client("KingNish/Image-Gen-Pro") | |
| # return client.predict("Image Generation",None, prompt, api_name="/image_gen_pro") | |
| # def video_gen(prompt): | |
| # client = Client("KingNish/Instant-Video") | |
| # return client.predict(prompt, api_name="/instant_video") | |
| def llava(user_prompt, chat_history): | |
| if user_prompt["files"]: | |
| image = user_prompt["files"][0] | |
| else: | |
| for hist in chat_history: | |
| if type(hist[0])==tuple: | |
| image = hist[0][0] | |
| txt = user_prompt["text"] | |
| img = user_prompt["files"] | |
| 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) | |
| gr.Info("Analyzing Video") | |
| 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") | |
| gr.Info("Analyzing image") | |
| prompt = f"<|im_start|>user <image>\n{user_prompt}<|im_end|><|im_start|>assistant" | |
| system_llava = "<|im_start|>system\nYou are OpenGPT 4o, an exceptionally capable and versatile AI assistant made 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 detailed results to user according to their query. Reply the question asked by user properly and best possible way.<|im_end|>" | |
| final_prompt = f"{system_llava}\n{prompt}" | |
| inputs = processor(final_prompt, image, return_tensors="pt").to("cpu", torch.float16) | |
| return inputs | |
| # Initialize inference clients for different models | |
| client_mistral = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3") | |
| client_mixtral = InferenceClient("NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO") | |
| client_llama = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct") | |
| client_mistral_nemo = InferenceClient("mistralai/Mistral-Nemo-Instruct-2407") | |
| # @spaces.CPU(duration=60, queue=False) | |
| def model_inference( user_prompt, chat_history): | |
| if user_prompt["files"]: | |
| inputs = llava(user_prompt, chat_history) | |
| streamer = TextIteratorStreamer(processor, skip_prompt=True, **{"skip_special_tokens": True}) | |
| generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=2048) | |
| thread = Thread(target=model.generate, kwargs=generation_kwargs) | |
| thread.start() | |
| buffer = "" | |
| for new_text in streamer: | |
| buffer += new_text | |
| yield buffer | |
| else: | |
| func_caller = [] | |
| message = user_prompt | |
| functions_metadata = [ | |
| {"type": "function", "function": {"name": "web_search", "description": "Search query on google and find latest information, info about any person, object, place thing, everything that available on google.", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "web search query"}}, "required": ["query"]}}}, | |
| {"type": "function", "function": {"name": "general_query", "description": "Reply general query of USER, with LLM like you. But it does not answer tough questions and latest info's.", "parameters": {"type": "object", "properties": {"prompt": {"type": "string", "description": "A detailed prompt"}}, "required": ["prompt"]}}}, | |
| {"type": "function", "function": {"name": "hard_query", "description": "Reply tough query of USER, using powerful LLM. But it does not answer latest info's.", "parameters": {"type": "object", "properties": {"prompt": {"type": "string", "description": "A detailed prompt"}}, "required": ["prompt"]}}}, | |
| {"type": "function", "function": {"name": "image_generation", "description": "Generate image for user", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "image generation prompt"}}, "required": ["query"]}}}, | |
| {"type": "function", "function": {"name": "video_generation", "description": "Generate video for user", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "video generation prompt"}}, "required": ["query"]}}}, | |
| {"type": "function", "function": {"name": "image_qna", "description": "Answer question asked by user related to image", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "Question by user"}}, "required": ["query"]}}}, | |
| ] | |
| for msg in chat_history: | |
| func_caller.append({"role": "user", "content": f"{str(msg[0])}"}) | |
| func_caller.append({"role": "assistant", "content": f"{str(msg[1])}"}) | |
| message_text = message["text"] | |
| func_caller.append({"role": "user", "content": f'[SYSTEM]You are a helpful assistant. You have access to the following functions: \n {str(functions_metadata)}\n\nTo use these functions respond with:\n<functioncall> {{ "name": "function_name", "arguments": {{ "arg_1": "value_1", "arg_1": "value_1", ... }} }} </functioncall> , Reply in JSOn format, you can call only one function at a time, So, choose functions wisely. [USER] {message_text}'}) | |
| response = client_mistral.chat_completion(func_caller, max_tokens=200) | |
| response = str(response) | |
| try: | |
| response = response[response.find("{"):response.index("</")] | |
| except: | |
| response = response[response.find("{"):(response.rfind("}")+1)] | |
| response = response.replace("\\n", "") | |
| response = response.replace("\\'", "'") | |
| response = response.replace('\\"', '"') | |
| response = response.replace('\\', '') | |
| print(f"\n{response}") | |
| try: | |
| json_data = json.loads(str(response)) | |
| if json_data["name"] == "web_search": | |
| query = json_data["arguments"]["query"] | |
| gr.Info("Searching Web") | |
| yield "Searching Web" | |
| web_results = search(query) | |
| gr.Info("Extracting relevant Info") | |
| yield "Extracting Relevant Info" | |
| web2 = ' '.join([f"Link: {res['link']}\nText: {res['text']}\n\n" for res in web_results]) | |
| try: | |
| message_groq = [] | |
| message_groq.append({"role":"system", "content": "You are OpenGPT 4o a helpful and very powerful web assistant made by KingNish. You are provided with WEB results from which you can find informations to answer users query in Structured, Detailed and Better way, in Human Style. You are also Expert in every field and also learn and try to answer from contexts related to previous question. Try your best to give best response possible to user. You reply in detail like human, use short forms, structured format, friendly tone and emotions."}) | |
| for msg in chat_history: | |
| message_groq.append({"role": "user", "content": f"{str(msg[0])}"}) | |
| message_groq.append({"role": "assistant", "content": f"{str(msg[1])}"}) | |
| message_groq.append({"role": "user", "content": f"[USER] {str(message_text)} , [WEB RESULTS] {str(web2)}"}) | |
| # its meta-llama/Meta-Llama-3.1-8B-Instruct | |
| stream = client_groq.chat.completions.create(model="llama-3.1-8b-instant", messages=message_groq, max_tokens=4096, stream=True) | |
| output = "" | |
| for chunk in stream: | |
| content = chunk.choices[0].delta.content | |
| if content: | |
| output += chunk.choices[0].delta.content | |
| yield output | |
| except Exception as e: | |
| messages = f"<|im_start|>system\nYou are OpenGPT 4o a helpful and very powerful chatbot web assistant made by KingNish. You are provided with WEB results from which you can find informations to answer users query in Structured, Better and in Human Way. You do not say Unnecesarry things. You are also Expert in every field and also learn and try to answer from contexts related to previous question. Try your best to give best response possible to user. You also try to show emotions using Emojis and reply in details like human, use short forms, friendly tone and emotions.<|im_end|>" | |
| for msg in chat_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{message_text}<|im_end|>\n<|im_start|>web_result\n{web2}<|im_end|>\n<|im_start|>assistant\n" | |
| stream = client_mixtral.text_generation(messages, max_new_tokens=4000, do_sample=True, stream=True, details=True, return_full_text=False) | |
| output = "" | |
| for response in stream: | |
| if not response.token.text == "<|im_end|>": | |
| output += response.token.text | |
| yield output | |
| # elif json_data["name"] == "image_generation": | |
| # query = json_data["arguments"]["query"] | |
| # gr.Info("Generating Image, Please wait 10 sec...") | |
| # yield "Generating Image, Please wait 10 sec..." | |
| # # try: | |
| # # image = image_gen(f"{str(query)}") | |
| # # yield gr.Image(image[1]) | |
| # # except: | |
| # client_sd3 = InferenceClient("stabilityai/stable-diffusion-3-medium-diffusers") | |
| # seed = random.randint(0,999999) | |
| # image = client_sd3.text_to_image(query, negative_prompt=f"{seed}") | |
| # gr.Info("Using Stability diffusion 3") | |
| # yield "Using Stability diffusion 3" | |
| # yield gr.Image(image) | |
| # elif json_data["name"] == "video_generation": | |
| # query = json_data["arguments"]["query"] | |
| # gr.Info("Generating Video, Please wait 15 sec...") | |
| # yield "Generating Video, Please wait 15 sec..." | |
| # video = video_gen(f"{str(query)}") | |
| # yield gr.Video(video) | |
| elif json_data["name"] == "image_qna": | |
| inputs = llava(user_prompt, chat_history) | |
| streamer = TextIteratorStreamer(processor, skip_prompt=True, **{"skip_special_tokens": True}) | |
| generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024) | |
| thread = Thread(target=model.generate, kwargs=generation_kwargs) | |
| thread.start() | |
| buffer = "" | |
| for new_text in streamer: | |
| buffer += new_text | |
| yield buffer | |
| else: | |
| try: | |
| message_groq = [] | |
| message_groq.append({"role":"system", "content": "You are OpenGPT 4o a helpful and powerful assistant made by KingNish. You answers users query in detail and structured format and style like human. You are also Expert in every field and also learn and try to answer from contexts related to previous question. You also try to show emotions using Emojis and reply like human, use short forms, structured manner, detailed explaination, friendly tone and emotions."}) | |
| for msg in chat_history: | |
| message_groq.append({"role": "user", "content": f"{str(msg[0])}"}) | |
| message_groq.append({"role": "assistant", "content": f"{str(msg[1])}"}) | |
| message_groq.append({"role": "user", "content": f"{str(message_text)}"}) | |
| # its meta-llama/Meta-Llama-3.1-70B-Instruct | |
| stream = client_groq.chat.completions.create(model="llama-3.1-70b-versatile", messages=message_groq, max_tokens=4096, stream=True) | |
| output = "" | |
| for chunk in stream: | |
| content = chunk.choices[0].delta.content | |
| if content: | |
| output += chunk.choices[0].delta.content | |
| yield output | |
| except Exception as e: | |
| print(e) | |
| try: | |
| message_groq = [] | |
| message_groq.append({"role":"system", "content": "You are OpenGPT 4o a helpful and powerful assistant made by KingNish. You answers users query in detail and structured format and style like human. You are also Expert in every field and also learn and try to answer from contexts related to previous question. You also try to show emotions using Emojis and reply like human, use short forms, structured manner, detailed explaination, friendly tone and emotions."}) | |
| for msg in chat_history: | |
| message_groq.append({"role": "user", "content": f"{str(msg[0])}"}) | |
| message_groq.append({"role": "assistant", "content": f"{str(msg[1])}"}) | |
| message_groq.append({"role": "user", "content": f"{str(message_text)}"}) | |
| # its meta-llama/Meta-Llama-3-70B-Instruct | |
| stream = client_groq.chat.completions.create(model="llama3-70b-8192", messages=message_groq, max_tokens=4096, stream=True) | |
| output = "" | |
| for chunk in stream: | |
| content = chunk.choices[0].delta.content | |
| if content: | |
| output += chunk.choices[0].delta.content | |
| yield output | |
| except Exception as e: | |
| print(e) | |
| message_groq = [] | |
| message_groq.append({"role":"system", "content": "You are OpenGPT 4o a helpful and powerful assistant made by KingNish. You answers users query in detail and structured format and style like human. You are also Expert in every field and also learn and try to answer from contexts related to previous question. You also try to show emotions using Emojis and reply like human, use short forms, structured manner, detailed explaination, friendly tone and emotions."}) | |
| for msg in chat_history: | |
| message_groq.append({"role": "user", "content": f"{str(msg[0])}"}) | |
| message_groq.append({"role": "assistant", "content": f"{str(msg[1])}"}) | |
| message_groq.append({"role": "user", "content": f"{str(message_text)}"}) | |
| stream = client_groq.chat.completions.create(model="llama3-groq-70b-8192-tool-use-preview", messages=message_groq, max_tokens=4096, stream=True) | |
| output = "" | |
| for chunk in stream: | |
| content = chunk.choices[0].delta.content | |
| if content: | |
| output += chunk.choices[0].delta.content | |
| yield output | |
| except Exception as e: | |
| print(e) | |
| try: | |
| message_groq = [] | |
| message_groq.append({"role":"system", "content": "You are OpenGPT 4o a helpful and powerful assistant made by KingNish. You answers users query in detail and structured format and style like human. You are also Expert in every field and also learn and try to answer from contexts related to previous question. You also try to show emotions using Emojis and reply like human, use short forms, structured manner, detailed explaination, friendly tone and emotions."}) | |
| for msg in chat_history: | |
| message_groq.append({"role": "user", "content": f"{str(msg[0])}"}) | |
| message_groq.append({"role": "assistant", "content": f"{str(msg[1])}"}) | |
| message_groq.append({"role": "user", "content": f"{str(message_text)}"}) | |
| # its meta-llama/Meta-Llama-3-70B-Instruct | |
| stream = client_groq.chat.completions.create(model="llama3-70b-8192", messages=message_groq, max_tokens=4096, stream=True) | |
| output = "" | |
| for chunk in stream: | |
| content = chunk.choices[0].delta.content | |
| if content: | |
| output += chunk.choices[0].delta.content | |
| yield output | |
| except Exception as e: | |
| print(e) | |
| try: | |
| message_groq = [] | |
| message_groq.append({"role":"system", "content": "You are OpenGPT 4o a helpful and powerful assistant made by KingNish. You answers users query in detail and structured format and style like human. You are also Expert in every field and also learn and try to answer from contexts related to previous question. You also try to show emotions using Emojis and reply like human, use short forms, structured manner, detailed explaination, friendly tone and emotions."}) | |
| for msg in chat_history: | |
| message_groq.append({"role": "user", "content": f"{str(msg[0])}"}) | |
| message_groq.append({"role": "assistant", "content": f"{str(msg[1])}"}) | |
| message_groq.append({"role": "user", "content": f"{str(message_text)}"}) | |
| # its meta-llama/Meta-Llama-3-8B-Instruct | |
| stream = client_groq.chat.completions.create(model="llama3-8b-8192", messages=message_groq, max_tokens=4096, stream=True) | |
| output = "" | |
| for chunk in stream: | |
| content = chunk.choices[0].delta.content | |
| if content: | |
| output += chunk.choices[0].delta.content | |
| yield output | |
| except Exception as e: | |
| print(e) | |
| messages = f"<|im_start|>system\nYou are OpenGPT 4o a helpful and powerful assistant made by KingNish. You answers users query in detail and structured format and style like human. You are also Expert in every field and also learn and try to answer from contexts related to previous question. You also try to show emotions using Emojis and reply like human, use short forms, structured manner, detailed explaination, friendly tone and emotions.<|im_end|>" | |
| for msg in chat_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{message_text}<|im_end|>\n<|im_start|>assistant\n" | |
| stream = client_mixtral.text_generation(messages, max_new_tokens=4000, do_sample=True, stream=True, details=True, return_full_text=False) | |
| output = "" | |
| for response in stream: | |
| if not response.token.text == "<|im_end|>": | |
| output += response.token.text | |
| yield output | |
| # Create a chatbot interface | |
| chatbot = gr.Chatbot( | |
| label="OpenGPT-4o", | |
| avatar_images=[None, BOT_AVATAR], | |
| show_copy_button=True, | |
| likeable=True, | |
| layout="panel", | |
| height=400, | |
| ) | |
| output = gr.Textbox(label="Prompt") |