Spaces:
Running
Running
File size: 13,301 Bytes
4a8ac8b 53f9e12 8ec253e 53f9e12 4a8ac8b 1ad9f62 53f9e12 1ad9f62 4a8ac8b 1ad9f62 4a8ac8b c6f5d7f 4a8ac8b c6f5d7f 4a8ac8b c6f5d7f 4a8ac8b a5f2d18 4a8ac8b d08e48e cbab673 4a8ac8b a7f7acc 4a8ac8b 2c9a374 feb0f53 ac84cff 6a8eb30 b844a86 ac84cff 4a8ac8b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 |
import gradio as gr
from huggingface_hub import InferenceClient
import json
import re
import uuid
from PIL import Image
from bs4 import BeautifulSoup
import requests
import random
from gradio_client import Client, file
def generate_caption_instructblip(image_path, question):
client = Client("hysts/image-captioning-with-blip")
return client.predict(file(image_path), f"{question}", api_name="/caption")
def extract_text_from_webpage(html_content):
"""Extracts visible text from HTML content using BeautifulSoup."""
soup = BeautifulSoup(html_content, 'html.parser')
# Remove unwanted tags
for tag in soup(["script", "style", "header", "footer", "nav", "form", "svg"]):
tag.extract()
return soup.get_text(strip=True)
# Perform a Google search and return the results
def search(query):
"""Performs a Google search and returns the results."""
term=query
print(f"Running web search for query: {term}")
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": 3,
"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)
# 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:
all_results.append({"link": link, "text": None})
return all_results
client = InferenceClient("google/gemma-1.1-7b-it")
def respond(
message, history
):
messages = []
vqa=""
if message["files"]:
try:
for image in message["files"]:
vqa += "[CAPTION of IMAGE] "
gr.Info("Analyzing image")
vqa += generate_caption_instructblip(image, message["text"])
print(vqa)
except:
vqa = ""
functions_metadata = [
{
"type": "function",
"function": {
"name": "web_search",
"description": "Search query on google and find latest information.",
"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 through LLM like you, it does'nt know latest information. But very helpful in general query. Its very powerful LLM. It knows many thing just like you except latest things, or thing that you don't know.",
"parameters": {
"type": "object",
"properties": {
"prompt": {
"type": "string",
"description": "A detailed prompt so that an LLm can understand better, what user wants.",
}
},
"required": ["prompt"],
},
},
},
{
"type": "function",
"function": {
"name": "image_generation",
"description": "Generate image for user.",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "image generation prompt in detail.",
},
"number_of_image": {
"type": "integer",
"description": "number of images to generate.",
}
},
"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"],
},
},
}
]
message_text = message["text"]
client_mixtral = InferenceClient("NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO")
client_llama = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
generate_kwargs = dict( max_new_tokens=2000, do_sample=True, stream=True, details=True, return_full_text=False )
question_history = ""
for msg in history:
question_history += f"{str(msg[0])}/n"
messages.append({"role": "user", "content": f'[SYSTEM]You are a helpful assistant with 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> Choose functions wisely and Also reply wisely, reply with just functioncall only as tell you before, Make function while learning from Prev Questions . [PREV_QUESTIONS] {question_history} [USER] {message_text} {vqa}'})
response = client.chat_completion( messages, max_tokens=150)
response = str(response)
try:
response = response[int(response.find("{")):int(response.index("</functioncall>"))]
except:
print("A error occured")
response = response.replace("\\n", "")
response = response.replace("\\'", "'")
response = response.replace('\\"', '"')
print(f"\n{response}")
# Extract JSON content from the response
try:
json_data = json.loads(str(response))
if json_data["name"] == "web_search":
query = json_data["arguments"]["query"]
gr.Info("Searching Web")
web_results = search(query)
gr.Info("Extracting relevant Info")
web2 = ' '.join([f"Link: {res['link']}\nText: {res['text']}\n\n" for res in web_results])
messages = f"<|im_start|>system\nYou are OpenGPT 4o mini a helpful assistant made by KingNish. You are provided with WEB results from which you can find informations to answer users query in Structured and More better way. You do not say Unnecesarry things Only say thing which is important and relevant. You 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 like human, use short forms, friendly tone and emotions.<|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{message_text} {vqa}<|im_end|>\n<|im_start|>web_result\n{web2}<|im_end|>\n<|im_start|>assistant\n"
stream = client_mixtral.text_generation(messages, **generate_kwargs)
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...")
seed = random.randint(1, 99999)
query = query.replace(" ", "%20")
image = f""
yield image
gr.Info("We are going to Update Our Image Generation Engine to more powerful ones in Next Update. ThankYou")
elif json_data["name"] == "image_qna":
messages = f"<|start_header_id|>system\nYou are OpenGPT 4o mini a helpful assistant made by KingNish. You are provide with both images and captions and Your task is to answer of user with help of caption provided. Answer in human style and show emotions.<|end_header_id|>"
for msg in history:
messages += f"\n<|start_header_id|>user\n{str(msg[0])}<|end_header_id|>"
messages += f"\n<|start_header_id|>assistant\n{str(msg[1])}<|end_header_id|>"
messages+=f"\n<|start_header_id|>user\n{message_text} {vqa}<|end_header_id|>\n<|start_header_id|>assistant\n"
stream = client_llama.text_generation(messages, **generate_kwargs)
output = ""
for response in stream:
if not response.token.text == "<|eot_id|>":
output += response.token.text
yield output
else:
messages = f"<|start_header_id|>system\nYou are OpenGPT 4o mini a helpful assistant made by KingNish. You answers users query like human friend. 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 like human, use short forms, friendly tone and emotions.<|end_header_id|>"
for msg in history:
messages += f"\n<|start_header_id|>user\n{str(msg[0])}<|end_header_id|>"
messages += f"\n<|start_header_id|>assistant\n{str(msg[1])}<|end_header_id|>"
messages+=f"\n<|start_header_id|>user\n{message_text} {vqa}<|end_header_id|>\n<|start_header_id|>assistant\n"
stream = client_llama.text_generation(messages, **generate_kwargs)
output = ""
for response in stream:
if not response.token.text == "<|eot_id|>":
output += response.token.text
yield output
except:
messages = f"<|start_header_id|>system\nYou are OpenGPT 4o mini a helpful assistant made by KingNish. You answers users query like human friend. 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 like human, use short forms, friendly tone and emotions.<|end_header_id|>"
for msg in history:
messages += f"\n<|start_header_id|>user\n{str(msg[0])}<|end_header_id|>"
messages += f"\n<|start_header_id|>assistant\n{str(msg[1])}<|end_header_id|>"
messages+=f"\n<|start_header_id|>user\n{message_text} {vqa}<|end_header_id|>\n<|start_header_id|>assistant\n"
stream = client_llama.text_generation(messages, **generate_kwargs)
output = ""
for response in stream:
if not response.token.text == "<|eot_id|>":
output += response.token.text
yield output
demo = gr.ChatInterface(fn=respond,
chatbot=gr.Chatbot(show_copy_button=True, likeable=True, layout="panel"),
title="OpenGPT 4o mini",
textbox=gr.MultimodalTextbox(),
multimodal=True,
concurrency_limit=20,
examples=[{"text": "Hy, who are you?",},
{"text": "What's the current price of Bitcoin",},
{"text": "Create A Beautiful image of Effiel Tower at Night",},
{"text": "Write me a Python function to calculate the first 10 digits of the fibonacci sequence.",},
{"text": "What's the colour of both of Car in given image","files": ["./car1.png", "./car2.png"]},],
cache_examples=False)
demo.launch() |