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LICENSE

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+MIT License
+
+Copyright (c) 2025 Ashish Singh Mehra
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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----
-title: Resume And CoverLetter Generator
-emoji: 💻
-colorFrom: yellow
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.42.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Resume-Cover-Letter-Generator

app.py

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+import streamlit as st
+from embeddings import generate_embeddings
+from generator import generate_resume, generate_cover_letter
+from parser import extract_text_from_pdf
+from vector_store import store_resume_embedding, search_similar_resumes
+
+# Streamlit App
+st.title("Resume and Cover Letter Generator")
+
+# Upload Resume File
+uploaded_file = st.file_uploader("Upload your resume (PDF)", type="pdf")
+if uploaded_file:
+    # Extract text from the uploaded PDF
+    text = extract_text_from_pdf(uploaded_file.read())
+    # Generate embeddings for the uploaded resume
+    vector = generate_embeddings(text)
+    # Store the resume embedding for future searches
+    store_resume_embedding(uploaded_file.name, vector, text)
+    st.success("Resume uploaded successfully!")
+
+# Form to generate documents
+with st.form("generate_documents"):
+    name = st.text_input("Your Name")
+    job_description = st.text_area("Job Description")
+    submit_button = st.form_submit_button("Generate Resume and Cover Letter")
+    
+    if submit_button:
+        if not name or not job_description:
+            st.error("Please fill in both fields.")
+        else:
+            # Search for similar resumes based on job description
+            similar_resumes = search_similar_resumes(job_description)
+            # Generate resume and cover letter
+            resume = generate_resume(name, job_description, similar_resumes)
+            cover_letter = generate_cover_letter(name, job_description, similar_resumes)
+            
+            # Display the generated resume and cover letter
+            st.subheader("Generated Resume")
+            st.write(resume)
+            st.subheader("Generated Cover Letter")
+            st.write(cover_letter)

ats_checker.py

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+import re
+from collections import Counter
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+def extract_keywords(text, top_n=10):
+    words = re.findall(r'\b\w+\b', text.lower())
+    common_words = set(["the", "and", "is", "to", "in", "for", "on", "with"])  # Stopwords
+    filtered_words = [word for word in words if word not in common_words]
+    return [word for word, _ in Counter(filtered_words).most_common(top_n)]
+
+def compute_ats_score(resume_text, job_desc):
+    resume_keywords = extract_keywords(resume_text)
+    job_keywords = extract_keywords(job_desc)
+    
+    match_score = len(set(resume_keywords) & set(job_keywords)) / len(set(job_keywords))
+    return f"ATS Score: {match_score:.2f}"

embeddings.py

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+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+
+def generate_embeddings(text):
+    return model.encode([text])[0]  # Convert text to vector

generator.py

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+import google.generativeai as genai
+
+# Configure Gemini API
+genai.configure(api_key="AIzaSyCQkmt7LZKp_BHTd05DtIkJEFQD3V1Usqw")
+
+# Initialize Gemini model
+llm = genai.GenerativeModel("gemini-pro")
+
+def generate_resume(name, job_desc, similar_resumes):
+    prompt = f"Generate a professional resume for {name} applying for:\n{job_desc}\n\nReference these resumes:\n{similar_resumes}"
+    response = llm.generate_content(prompt)
+    return response.text
+
+def generate_cover_letter(name, job_desc, similar_resumes):
+    prompt = f"Write a tailored cover letter for {name} based on:\n{job_desc}\n\nReference resumes:\n{similar_resumes}"
+    response = llm.generate_content(prompt)
+    return response.text
+

parser.py

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+import fitz
+
+def extract_text_from_pdf(file_data):
+  pdf_doc = fitz.open(stream=file_data, filetype='pdf')
+  text = " ".join([page.get_text("text") for page in pdf_doc])
+  return text
+

requirements.txt

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+langchain
+transformers
+faiss-cpu
+pinecone-client
+fastapi
+uvicorn
+pydantic
+python-multipart
+pdfplumber
+python-docx
+pymupdf
+sentence-transformers
+langchain_community
+google-generativeai

vector_store.py

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+import faiss
+import numpy as np
+from embeddings import generate_embeddings
+
+
+
+dimension = 384
+index = faiss.IndexFlatL2(dimension)
+resume_db = {}
+
+def store_resume_embedding(filename, vector, text):
+    global resume_db
+    index.add(np.array([vector]))
+    resume_db[len(resume_db)] = {"filename": filename, "text": text}
+
+def search_similar_resumes(job_description):
+    job_vector = generate_embeddings(job_description)
+    D, I = index.search(np.array([job_vector]), k=3)
+    return [resume_db[i]["text"] for i in I[0] if i in resume_db]