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import gradio as gr
from huggingface_hub import InferenceClient
import json
import uuid
from PIL import Image
from bs4 import BeautifulSoup
import requests
import random
from transformers import LlavaProcessor, LlavaForConditionalGeneration, TextIteratorStreamer
from threading import Thread
import re
import time
import torch
import cv2
from gradio_client import Client, file
def extract_text_from_webpage(html_content):
soup = BeautifulSoup(html_content, 'html.parser')
for tag in soup(["script", "style", "header", "footer"]):
tag.extract()
return soup.get_text(strip=True)
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": 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)
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
# Initialize inference clients for different models
client_gemma = 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")
func_caller = []
# Define the main chat function
def respond(message, history):
func_caller = []
user_prompt = message
functions_metadata = [
{"type": "function", "function": {"name": "web_search", "description": "Search query on google", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "web search query"}}, "required": ["query"]}}},
]
for msg in 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> [USER] {message_text}'})
response = client_gemma.chat_completion(func_caller, max_tokens=200)
response = str(response)
try:
response = response[int(response.find("{")):int(response.rindex("</"))]
except:
response = response[int(response.find("{")):(int(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")
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"system\nYou are OpenCHAT 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."
for msg in history:
messages += f"\nuser\n{str(msg[0])}"
messages += f"\nassistant\n{str(msg[1])}"
messages+=f"\nuser\n{message_text}\nweb_result\n{web2}\nassistant\n"
stream = client_mixtral.text_generation(messages, max_new_tokens=2000, do_sample=True, stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
if not response.token.text == "":
output += response.token.text
yield output
else:
messages = f"system\nYou are OpenCHAT 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."
for msg in history:
messages += f"\nuser\n{str(msg[0])}"
messages += f"\nassistant\n{str(msg[1])}"
messages+=f"\nuser\n{message_text}\nassistant\n"
stream = client_llama.text_generation(messages, max_new_tokens=2000, do_sample=True, stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
if not response.token.text == "":
output += response.token.text
yield output
except:
messages = f"system\nYou are OpenCHAT 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."
for msg in history:
messages += f"\nuser\n{str(msg[0])}"
messages += f"\nassistant\n{str(msg[1])}"
messages+=f"\nuser\n{message_text}\nassistant\n"
stream = client_llama.text_generation(messages, max_new_tokens=2000, do_sample=True, stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
if not response.token.text == "":
output += response.token.text
yield output
demo = gr.ChatInterface(
fn=respond,
chatbot=gr.Chatbot(show_copy_button=True, likeable=True, layout="panel"),
description =" ",
textbox=gr.MultimodalTextbox(),
multimodal=True,
concurrency_limit=200,
)
demo.launch() |