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import os
import streamlit as st
import PyPDF2
from langchain_community.llms import HuggingFaceHub
# Streamlit page configuration
st.set_page_config(page_title="Research Position Application Generator", page_icon="🔬")
# Set Hugging Face API Key
os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets["HF_TOKEN"]
# Initialize LLM
llm = HuggingFaceHub(
repo_id="mistralai/Mistral-7B-Instruct-v0.3",
model_kwargs={"temperature": 0.5}
)
def extract_text_from_pdf(uploaded_file):
"""
Extract text from an uploaded PDF file.
Args:
uploaded_file (UploadedFile): PDF file uploaded by the user
Returns:
str: Extracted text from the PDF
"""
try:
pdf_reader = PyPDF2.PdfReader(uploaded_file)
text = ""
for page in pdf_reader.pages:
text += page.extract_text()
return text
except Exception as e:
st.error(f"Error extracting PDF text: {e}")
return ""
def generate_cold_email(position_details, cv_text):
"""
Generate a professional cold email using the LLM.
Args:
position_details (dict): Details about the research position
cv_text (str): Text extracted from the CV/resume
Returns:
str: Generated cold email
"""
prompt = f"""Write a professional and concise cold email to Professor {position_details['professor_name']}
at {position_details['university']} about the research position in {position_details['research_focus']}.
The email should:
1. Demonstrate knowledge of the professor's research
2. Highlight relevant experience from the CV
3. Express genuine interest in the position
4. Be no more than 250 words
CV Details:
{cv_text}
Research Position Details:
Research Focus: {position_details['research_focus']}
Professor: {position_details['professor_name']}
University: {position_details['university']}
"""
return llm.invoke(prompt)
def generate_cover_letter(position_details, cv_text):
"""
Generate a formal cover letter using the LLM.
Args:
position_details (dict): Details about the research position
cv_text (str): Text extracted from the CV/resume
Returns:
str: Generated cover letter
"""
prompt = f"""Write a professional and formal cover letter for a research position with the following details:
Research Focus: {position_details['research_focus']}
University: {position_details['university']}
The cover letter should:
1. Follow a standard business letter format
2. Clearly state the purpose of the letter
3. Highlight relevant skills and experiences from the CV
4. Demonstrate alignment with the research position
5. Be 300-400 words long
6. Include a strong closing paragraph
CV Details:
{cv_text}
"""
return llm.invoke(prompt)
def main():
"""
Main Streamlit app function
"""
st.title("🔬 Research Position Application Generator")
# Sidebar for position details
st.sidebar.header("Research Position Details")
professor_name = st.sidebar.text_input("Professor's Name")
university = st.sidebar.text_input("University")
research_focus = st.sidebar.text_input("Research Focus")
# CV Upload
st.sidebar.header("Upload CV/Resume")
uploaded_cv = st.sidebar.file_uploader("Choose a PDF file", type="pdf")
# Generate button
if st.sidebar.button("Generate Documents"):
# Validate inputs
if not (professor_name and university and research_focus and uploaded_cv):
st.error("Please fill in all details and upload a CV")
return
# Extract CV text
cv_text = extract_text_from_pdf(uploaded_cv)
# Prepare position details
position_details = {
'professor_name': professor_name,
'university': university,
'research_focus': research_focus
}
# Generate documents
with st.spinner('Generating documents...'):
cold_email = generate_cold_email(position_details, cv_text)
cover_letter = generate_cover_letter(position_details, cv_text)
# Display results
st.header("Generated Documents")
# Cold Email
st.subheader("Cold Email")
st.write(cold_email)
st.download_button(
label="Download Cold Email",
data=cold_email,
file_name="cold_email.txt",
mime="text/plain"
)
# Cover Letter
st.subheader("Cover Letter")
st.write(cover_letter)
st.download_button(
label="Download Cover Letter",
data=cover_letter,
file_name="cover_letter.txt",
mime="text/plain"
)
if __name__ == "__main__":
main() |