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import gradio as gr |
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from huggingface_hub import InferenceClient |
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import json |
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from PIL import Image |
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from bs4 import BeautifulSoup |
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import requests |
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def extract_text_from_webpage(html_content): |
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soup = BeautifulSoup(html_content, 'html.parser') |
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for tag in soup(["script", "style", "header", "footer"]): |
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tag.extract() |
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return soup.get_text(strip=True) |
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def search(query): |
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term = query |
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all_results = [] |
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max_chars_per_page = 8000 |
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with requests.Session() as session: |
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resp = session.get( |
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url="https://www.google.com/search", |
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headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0"}, |
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params={"q": term, "num": 3}, |
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timeout=5 |
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) |
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resp.raise_for_status() |
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soup = BeautifulSoup(resp.text, "html.parser") |
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result_block = soup.find_all("div", attrs={"class": "g"}) |
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for result in result_block: |
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link = result.find("a", href=True) |
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link = link["href"] |
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try: |
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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) |
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webpage.raise_for_status() |
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visible_text = extract_text_from_webpage(webpage.text) |
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if len(visible_text) > max_chars_per_page: |
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visible_text = visible_text[:max_chars_per_page] |
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all_results.append({"link": link, "text": visible_text}) |
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except requests.exceptions.RequestException: |
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all_results.append({"link": link, "text": None}) |
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return all_results |
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client_gemma = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3") |
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client_mixtral = InferenceClient("NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO") |
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client_llama = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct") |
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def respond(message, history): |
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func_caller = [] |
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user_prompt = message |
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functions_metadata = [ |
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{"type": "function", "function": {"name": "web_search", "description": "Search query on google", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "web search query"}}, "required": ["query"]}}}, |
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] |
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for msg in history: |
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func_caller.append({"role": "user", "content": f"{str(msg[0])}"}) |
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func_caller.append({"role": "assistant", "content": f"{str(msg[1])}"}) |
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message_text = message |
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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}'}) |
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response = client_gemma.chat_completion(func_caller, max_tokens=200) |
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response = str(response) |
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try: |
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response = response[int(response.find("{")):int(response.rindex("</"))] |
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except: |
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response = response[int(response.find("{")):(int(response.rfind("}"))+1)] |
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response = response.replace("\\n", "") |
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response = response.replace("\\'", "'") |
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response = response.replace('\\"', '"') |
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response = response.replace('\\', '') |
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print(f"\n{response}") |
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try: |
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json_data = json.loads(str(response)) |
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if json_data["name"] == "web_search": |
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query = json_data["arguments"]["query"] |
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web_results = search(query) |
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web2 = ' '.join([f"Link: {res['link']}\nText: {res['text']}\n\n" for res in web_results]) |
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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." |
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for msg in history: |
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messages += f"\nuser\n{str(msg[0])}" |
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messages += f"\nassistant\n{str(msg[1])}" |
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messages+=f"\nuser\n{message_text}\nweb_result\n{web2}\nassistant\n" |
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stream = client_mixtral.text_generation(messages, max_new_tokens=2000, do_sample=True, stream=True, details=True, return_full_text=False) |
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output = "" |
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for response in stream: |
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if not response.token.text == "": |
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output += response.token.text |
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yield output |
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else: |
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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." |
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for msg in history: |
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messages += f"\nuser\n{str(msg[0])}" |
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messages += f"\nassistant\n{str(msg[1])}" |
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messages+=f"\nuser\n{message_text}\nassistant\n" |
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stream = client_llama.text_generation(messages, max_new_tokens=2000, do_sample=True, stream=True, details=True, return_full_text=False) |
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output = "" |
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for response in stream: |
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if not response.token.text == "": |
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output += response.token.text |
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yield output |
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except: |
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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." |
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for msg in history: |
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messages += f"\nuser\n{str(msg[0])}" |
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messages += f"\nassistant\n{str(msg[1])}" |
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messages+=f"\nuser\n{message_text}\nassistant\n" |
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stream = client_llama.text_generation(messages, max_new_tokens=2000, do_sample=True, stream=True, details=True, return_full_text=False) |
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output = "" |
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for response in stream: |
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if not response.token.text == "": |
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output += response.token.text |
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yield output |
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demo = gr.ChatInterface( |
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fn=respond, |
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chatbot=gr.Chatbot(show_copy_button=True, likeable=True, layout="panel"), |
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description="Ask anything and get responses based on web searches and AI models.", |
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textbox=gr.MultimodalTextbox(), |
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multimodal=True, |
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concurrency_limit=200, |
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) |
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demo.launch() |
