Spaces:
Sleeping
Sleeping
pr11
#11
by
Mia2024
- opened
- SessionState.py +117 -0
- app.py +100 -57
- requirements.txt +0 -2
- utils.py +0 -51
SessionState.py
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"""Hack to add per-session state to Streamlit.
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Usage
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-----
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>>> import SessionState
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>>>
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>>> session_state = SessionState.get(user_name='', favorite_color='black')
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>>> session_state.user_name
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''
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>>> session_state.user_name = 'Mary'
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>>> session_state.favorite_color
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'black'
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Since you set user_name above, next time your script runs this will be the
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result:
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>>> session_state = get(user_name='', favorite_color='black')
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>>> session_state.user_name
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'Mary'
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"""
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try:
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import streamlit.ReportThread as ReportThread
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from streamlit.server.Server import Server
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except Exception:
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# Streamlit >= 0.65.0
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import streamlit.report_thread as ReportThread
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from streamlit.server.server import Server
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class SessionState(object):
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def __init__(self, **kwargs):
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"""A new SessionState object.
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Parameters
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----------
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**kwargs : any
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Default values for the session state.
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Example
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-------
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>>> session_state = SessionState(user_name='', favorite_color='black')
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>>> session_state.user_name = 'Mary'
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''
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>>> session_state.favorite_color
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'black'
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"""
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for key, val in kwargs.items():
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setattr(self, key, val)
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def get(**kwargs):
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"""Gets a SessionState object for the current session.
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Creates a new object if necessary.
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Parameters
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----------
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**kwargs : any
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Default values you want to add to the session state, if we're creating a
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new one.
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Example
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-------
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>>> session_state = get(user_name='', favorite_color='black')
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>>> session_state.user_name
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''
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>>> session_state.user_name = 'Mary'
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>>> session_state.favorite_color
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'black'
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Since you set user_name above, next time your script runs this will be the
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result:
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>>> session_state = get(user_name='', favorite_color='black')
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>>> session_state.user_name
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'Mary'
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"""
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# Hack to get the session object from Streamlit.
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ctx = ReportThread.get_report_ctx()
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this_session = None
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current_server = Server.get_current()
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if hasattr(current_server, '_session_infos'):
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# Streamlit < 0.56
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session_infos = Server.get_current()._session_infos.values()
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else:
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session_infos = Server.get_current()._session_info_by_id.values()
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for session_info in session_infos:
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s = session_info.session
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if (
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# Streamlit < 0.54.0
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(hasattr(s, '_main_dg') and s._main_dg == ctx.main_dg)
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or
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# Streamlit >= 0.54.0
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(not hasattr(s, '_main_dg') and s.enqueue == ctx.enqueue)
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or
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# Streamlit >= 0.65.2
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(not hasattr(s, '_main_dg') and s._uploaded_file_mgr == ctx.uploaded_file_mgr)
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):
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this_session = s
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if this_session is None:
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raise RuntimeError(
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"Oh noes. Couldn't get your Streamlit Session object. "
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'Are you doing something fancy with threads?')
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# Got the session object! Now let's attach some state into it.
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if not hasattr(this_session, '_custom_session_state'):
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this_session._custom_session_state = SessionState(**kwargs)
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return this_session._custom_session_state
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app.py
CHANGED
@@ -1,58 +1,101 @@
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import streamlit as st
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with
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st.button('Clear', on_click=click_button)
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if st.session_state.clicked:
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# The message and nested widget will remain on the page
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del st.session_state.product
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del st.session_state.gender
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del st.session_state.profession
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del st.session_state.hobby
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del st.session_state.email
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del st.session_state.clicked
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if submitted:
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placeholder.empty()
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# import streamlit as st
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# import time
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# with st.form("my_input"):
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# st.write("Input")
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# # product
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# product=st.text_input("product")
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# # gender
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# gender=st.radio("gender", ["male", "female"])
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# # profession
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# profession=st.text_input("profession")
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# # hobby
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# hobby=st.text_input("hobby")
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# # Every form must have a submit button.
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# col1, col2=st.columns(2)
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# # Place a button in each column
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# with col1:
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# submitted = st.form_submit_button("Submit")
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# with col2:
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# clear = st.form_submit_button("Clear")
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# with st.form("my_output"):
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# if submitted:
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# st.write("product", product)
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# st.write("gender", gender)
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# st.write("profession", profession)
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# st.write("hobby", hobby)
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# # Clear the user inputs
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# if clear:
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# st.experimental_rerun()
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# _LOREM_IPSUM = """
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# Lorem ipsum dolor sit amet, **consectetur adipiscing** elit, sed do eiusmod tempor
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# incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis
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# nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
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# """
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# def stream_data():
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# for word in _LOREM_IPSUM.split(" "):
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# yield word + " "
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# time.sleep(0.02)
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# for word in _LOREM_IPSUM.split(" "):
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# yield word + " "
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# time.sleep(0.02)
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# if st.button("Stream data"):
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# st.write_stream(stream_data)
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import streamlit as st
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import SessionState
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# Initialize session state
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session_state = SessionState.get(product="", gender="", profession="", hobby="")
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# Create a form for user inputs
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with st.form("my_input"):
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st.write("Input")
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# product
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product = st.text_input("product", value=session_state.product)
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# gender
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gender = st.radio("gender", ["male", "female"], index=["male", "female"].index(session_state.gender) if session_state.gender else 0)
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# profession
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profession = st.text_input("profession", value=session_state.profession)
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# hobby
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hobby = st.text_input("hobby", value=session_state.hobby)
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# Every form must have a submit button.
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submitted = st.form_submit_button("Submit")
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clear = st.form_submit_button("Clear")
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# Display the user inputs
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with st.form("my_output"):
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if submitted and not clear:
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# Save inputs to session state
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session_state.product = product
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session_state.gender = gender
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session_state.profession = profession
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session_state.hobby = hobby
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st.write("product", product)
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st.write("gender", gender)
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st.write("profession", profession)
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st.write("hobby", hobby)
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# Clear the user inputs
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if clear:
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# Clear session state
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session_state.product = ""
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session_state.gender = ""
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session_state.profession = ""
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session_state.hobby = ""
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st.experimental_rerun()
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requirements.txt
DELETED
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torch
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transformers>=4.24.0
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utils.py
DELETED
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from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
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import re
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# Load saved model and tokenizer
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model_checkpoint = "24NLPGroupO/EmailGeneration"
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tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, truncation=True)
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model = AutoModelForCausalLM.from_pretrained(model_checkpoint)
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# Set up the generation pipeline
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generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
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def clean_generated_text(text):
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#Basic cleaning
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text = re.sub(r'^(Re:|Fwd:)', '', text) # Remove reply and forward marks
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text = re.sub(r'Best regards,.*$', '', text, flags=re.DOTALL) # Remove signature
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text = re.sub(r'PHONE.*$', '', text, flags=re.DOTALL) # Remove phone numbers
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text = re.sub(r'Email:.*$', '', text, flags=re.DOTALL) # Remove email addresses
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text = re.sub(r'Cc:.*$', '', text, flags=re.DOTALL) # Remove CC list
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text = re.sub(r'\* Attachments:.*', '', text, flags=re.S) # Remove Attachments
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text = re.sub(r'©️ .*$', '', text, flags=re.DOTALL) # Remove copyright and ownership statements
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text = re.sub(r'URL', '', text) # Remove URLs
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text = re.sub(r'NUMBER', '10', text) # Replace 'NUMBER' with a real number
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text = re.sub(r'CURRENCYNUMBER', 'USD 100', text) # Replace 'CURRENCYNUMBER' with a real value
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text = re.sub(r'About Us.*', '', text, flags=re.DOTALL) # Remove 'About Us' and all following text
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text = re.sub(r'\d+ [^\s]+ St\.?,?.*?\d{5}', '', text) # Remove street
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text = re.sub(r'\d+ [^\s]+ Ave\.?,?.*?\d{5}', '', text) # Remove avenues
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text = re.sub(r'\d+ [^\s]+ Rd\.?,?.*?\d{5}', '', text) # Remove roads
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text = re.sub(r'\d+ [^\s]+ Ln\.?,?.*?\d{5}', '', text) # Remove lanes
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text = re.sub(r'\d+ [^\s]+ Blvd\.?,?.*?\d{5}', '', text) # Remove boulevards
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text = re.sub(r'\d+ [^\s]+ Dr\.?,?.*?\d{5}', '', text) # Remove drives
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text = re.sub(r'\d+ [^\s]+ Ct\.?,?.*?\d{5}', '', text) # Remove courts
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return text.strip()
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def generate_email(product, gender, profession, hobby):
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input_text = f"{product} {gender} {profession} {hobby}"
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result = generator(
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input_text, # The starting text that guides the model on what to generate
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max_length=256, # Set a suitable maximum length
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top_k=40, # Consider more top options words
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top_p=0.6, # Control the probability range for word choices
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temperature=0.4, # Control the randomness of generation
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repetition_penalty=1.5, # Reduce content repetition
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num_return_sequences=2, # Generate three texts
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do_sample=True
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)
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# Clean each generated text
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cleaned_texts = [clean_generated_text(seq['generated_text']) for seq in result]
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# Choose the best text based on length and clarity
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best_text = max(cleaned_texts, key=len)
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return best_text
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