Spaces:
Sleeping
Sleeping
File size: 4,238 Bytes
2223de7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 |
import os
from langchain_text_splitters import CharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.schema import HumanMessage
from langchain.document_loaders import UnstructuredFileLoader
from langchain_community.vectorstores import Chroma
from langchain_groq import ChatGroq
import gradio as gr
# Initialize ChromaDB and Groq API
DB_DIR = "chroma_db"
COLLECTION_NAME = "resume_collection" # Changed to reflect purpose
embedding_function = HuggingFaceEmbeddings()
GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
llm = ChatGroq(api_key=GROQ_API_KEY, model_name="llama-3.1-8b-instant")
# Store resume content globally
stored_resume = None
def load_and_split_document(file_path):
"""Loads a document and splits it into chunks."""
loader = UnstructuredFileLoader(file_path)
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50) # Increased chunk size
chunks = text_splitter.split_documents(documents)
return chunks
def upload_resume(file):
"""Processes and stores the resume."""
try:
global stored_resume
uploaded_file_path = file.name
# Load and store resume
chunks = load_and_split_document(uploaded_file_path)
stored_resume = "\n".join([chunk.page_content for chunk in chunks])
return "Resume successfully uploaded and stored!"
except Exception as e:
return f"Error processing resume: {str(e)}"
def generate_cold_email(job_description, hiring_manager_name, company_name):
"""Generates a cold email based on the job description and stored resume."""
try:
if not stored_resume:
return "Please upload your resume first!"
# Craft prompt for skill extraction and email generation
prompt = f"""
Task: Generate a personalized cold email for a job application.
Resume:
{stored_resume}
Job Description:
{job_description}
First, analyze the job description and resume to identify matching skills and relevant experiences.
Then, create a compelling cold email to {hiring_manager_name} at {company_name} that:
1. Shows enthusiasm for the role and company
2. Highlights 2-3 most relevant skills/experiences that match the job requirements
3. Demonstrates knowledge of the company
4. Includes a clear call to action
5. Maintains a professional yet engaging tone
6. Keeps the email concise (max 200 words)
Format the email with proper salutation and signature.
"""
messages = [HumanMessage(content=prompt)]
response = llm.invoke(messages)
return response.content
except Exception as e:
return f"Error generating email: {str(e)}"
# Define the Gradio UI
with gr.Blocks() as demo:
gr.Markdown("# 📧 AI Cold Email Generator")
with gr.Tab("Upload Resume"):
resume_input = gr.File(label="Upload Your Resume (PDF)")
resume_upload_button = gr.Button("Upload Resume")
resume_status = gr.Textbox(label="Upload Status", interactive=False)
with gr.Tab("Generate Email"):
job_description = gr.Textbox(
label="Job Description",
placeholder="Paste the job description here...",
lines=5
)
with gr.Row():
hiring_manager = gr.Textbox(
label="Hiring Manager's Name",
placeholder="e.g., John Smith"
)
company = gr.Textbox(
label="Company Name",
placeholder="e.g., Tech Corp"
)
generate_button = gr.Button("Generate Cold Email")
email_output = gr.Textbox(
label="Generated Email",
interactive=False,
lines=10
)
# Event handlers
resume_upload_button.click(
upload_resume,
inputs=[resume_input],
outputs=[resume_status]
)
generate_button.click(
generate_cold_email,
inputs=[job_description, hiring_manager, company],
outputs=[email_output]
)
# Launch the app
demo.launch()
|