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from pyexpat import model
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from transformers import GPT2LMHeadModel, GPT2Tokenizer
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from streamlit_lottie import st_lottie
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import json
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import pandas as pd
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import requests
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import torch
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import tensorflow as tf
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import streamlit as st
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from streamlit_option_menu import option_menu
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logo = "https://www.google.com/url?sa=i&url=https%3A%2F%2Ffr.depositphotos.com%2Fvector-images%2Frobot-logo.html&psig=AOvVaw14rAtmwJQVSpRFXFY6us7z&ust=1647274982461000&source=images&cd=vfe&ved=0CAsQjRxqFwoTCPjhzdO_w_YCFQAAAAAdAAAAABAD"
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st.set_page_config(page_icon = logo, page_title ="Bonsoir !", layout = "wide")
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@st.cache(allow_output_mutation=True)
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def load_tokenizer():
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tokenizer = GPT2Tokenizer.from_pretrained("gpt2-large")
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model = GPT2LMHeadModel.from_pretrained("gpt2-large", pad_token_id=tokenizer.eos_token_id)
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return tokenizer
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@st.cache(allow_output_mutation=True)
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def load_model():
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model = GPT2LMHeadModel.from_pretrained("gpt2-large", pad_token_id=tokenizer.eos_token_id)
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return model
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tokenizer =load_tokenizer()
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model = load_model()
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def reponse(question, temp=0.5, long=40):
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input_ids = tokenizer.encode(question, return_tensors='pt')
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output = model.generate(input_ids, max_length=long, temperature =temp, num_beams=5, no_repeat_ngram_size=2, early_stopping=True)
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rep = tokenizer.decode(output[0], skip_special_tokens=True)
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return rep
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def load_animation(url: str):
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r = requests.get(url)
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if r.status_code != 200 :
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return None
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return r.json()
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url = "https://assets10.lottiefiles.com/packages/lf20_96bovdur.json"
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robot = load_animation(url)
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def contact_message():
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st.header(":mailbox: Let's Get In Touch !")
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name, message = st.columns((1,2))
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with name:
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contact_form = """<form action="https://formsubmit.co/maxime.letutour@gmail.com" method="POST">
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<input type="text" name="name" placeholder = "Ton Nom" required>
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<input type="email" name="email" placeholder = "Ton E-mail" required>
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</form>"""
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st.markdown(contact_form, unsafe_allow_html=True)
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with message :
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contact_form2 = """<form action="https://formsubmit.co/maxime.letutour@gmail.com" method="POST">
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<textarea name="message" placeholder="Ecris moi !"></textarea>
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<button type="submit">Send</button>
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"""
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st.markdown(contact_form2, unsafe_allow_html=True)
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with open("style2.txt") as f:
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st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
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def robot():
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robot = load_animation(url)
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col1, col2, col3 = st.columns((5,1,5))
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with col1:
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st.subheader("Choose the length of my answer")
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long = st.number_input("Be aware that long answers require more time to think !", min_value=10, max_value=250, step =10)
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st.subheader("Ask me something")
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question = st.text_input('Be aware that I speak only english for the moment !',max_chars = 60)
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question = str(question)
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ok = st.button('Ask me')
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with col3:
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st_lottie(robot, speed=1, loop=True, quality = "low",height =300, width = 300)
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if ok:
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rep = reponse(question, long = long)
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rep_style = f'<p style="font-family:Lucida Handwriting; color:#00008B; font-size: 20px;">{rep}</p>'
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st.markdown(rep_style, unsafe_allow_html=True)
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def main():
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st.title("Shall we chat ? Ask me a question")
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with st.sidebar:
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choice = option_menu(
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menu_title = "Ask Me",
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options = ["Question", "Envoie Moi Un Message"],
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icons=["chat","envelope"],
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menu_icon="robot"
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)
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if choice == "Envoie Moi Un Message":
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contact_message()
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elif choice == "Question":
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robot()
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st.sidebar.subheader(":notebook_with_decorative_cover: Par Maxime Le Tutour")
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st.sidebar.write(" :blue_book: [**Mon LinkedIn**](https://www.linkedin.com/in/maxime-le-tutour-95994795/)", unsafe_allow_html =True)
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print("ok")
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if __name__ == '__main__':
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main() |
