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import streamlit as st
from huggingface_hub import InferenceClient
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import os
from PyPDF2 import PdfReader
import docx
def extract_cv_text(file):
"""Extract text from PDF or DOCX CV files."""
if file is None:
return "No CV uploaded"
file_ext = os.path.splitext(file.name)[1].lower()
if file_ext == '.pdf':
reader = PdfReader(file)
text = ""
for page in reader.pages:
text += page.extract_text()
return text
elif file_ext == '.docx':
doc = docx.Document(file)
text = ""
for paragraph in doc.paragraphs:
text += paragraph.text + "\n"
return text
else:
return "Unsupported file format. Please upload PDF or DOCX files."
# Replace 'your_huggingface_token' with your actual Hugging Face access token
access_token = os.getenv('API_KEY')
# Initialize the tokenizer and model with the Hugging Face access token
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it", use_auth_token=access_token)
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2b-it",
torch_dtype=torch.bfloat16,
use_auth_token=access_token
)
model.eval() # Set the model to evaluation mode
# Initialize the inference client (if needed for other API-based tasks)
client = InferenceClient(token=access_token)
def conversation_predict(input_text):
"""Generate a response for single-turn input using the model."""
# Tokenize the input text
input_ids = tokenizer(f"""Job Description:
{input_text}
Instructions: Write a concise and professional email expressing interest in the position.
Highlight relevant experience and skills from the CV that match the job requirements.
Keep the tone professional and enthusiastic.
Email:""", return_tensors="pt").input_ids
# Generate a response with the model
outputs = model.generate(input_ids, max_new_tokens=2048)
# Decode and return the generated response
return tokenizer.decode(outputs[0], skip_special_tokens=True)
def respond(
message: str,
history: list[tuple[str, str]],
system_message: str,
cv_file,
max_tokens: int,
temperature: float,
top_p: float,
):
"""Generate a response for a multi-turn chat conversation."""
# Extract CV text and update system message
cv_text = extract_cv_text(cv_file) if cv_file else "No CV provided"
updated_system_message = f"""Task: Write a professional job application email.
CV Summary:
{cv_text}
{system_message}"""
messages = [{"role": "system", "content": updated_system_message}]
for user_input, assistant_reply in history:
if user_input:
messages.append({"role": "user", "content": user_input})
if assistant_reply:
messages.append({"role": "assistant", "content": assistant_reply})
messages.append({"role": "user", "content": message})
response = ""
for message_chunk in client.chat_completion(
messages=messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message_chunk["choices"][0]["delta"].get("content", "")
response += token
yield response
# Streamlit UI
st.title("Job Application Email Generator")
# Instructions text area
system_message = st.text_area("System message",
"Instructions: Write a concise and professional email expressing interest in the position.",
height=150)
# CV file upload
cv_file = st.file_uploader("Upload CV (PDF or DOCX)", type=["pdf", "docx"])
# Sliders for max tokens, temperature, and top-p
max_tokens = st.slider("Max new tokens", min_value=1, max_value=2048, value=512, step=1)
temperature = st.slider("Temperature", min_value=0.1, max_value=4.0, value=0.7, step=0.1)
top_p = st.slider("Top-p (nucleus sampling)", min_value=0.1, max_value=1.0, value=0.95, step=0.05)
# Input message field
message = st.text_input("Job Description", "")
# Button to generate response
if st.button("Generate Email"):
if message:
response = conversation_predict(message)
st.write("Generated Email:")
st.write(response)
else:
st.warning("Please enter a job description.")
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