Merge pull request #554 from Niraj1608/ATS

✨[FEATURE] Resume Application Tracking System(ATS) Using Google Gemini Pro Vision LIM Model #440

Resume Application Tracking System(ATS)/README.md

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+
+
+# ATS Resume Expert
+
+ATS Resume Expert is a Streamlit-based web application that uses Google's Generative AI (Gemini) model to analyze resumes in PDF format against specific job descriptions. The application evaluates the resume content, providing insights and match percentages to help users understand how well their resume aligns with job requirements.
+
+## Features
+- **Resume Analysis**: Upload a PDF resume, and the AI evaluates it based on a provided job description.
+- **Job Match Scoring**: The AI provides a match percentage between the resume and job description, highlighting strengths, weaknesses, missing keywords, and more.
+- **Streamlit UI**: User-friendly interface with text input for job description and resume upload capability.
+
+## Getting Started
+
+### Prerequisites
+1. **Python**: Make sure you have Python 3.7+ installed.
+2. **Google API Key**: This project requires access to Google Generative AI's Gemini model. Obtain an API key and configure it in the environment.
+
+### Installation
+1. Clone this repository:
+   ```bash
+   git clone https://github.com/your-username/ATS-Resume-Expert.git
+   cd ATS-Resume-Expert
+   ```
+2. Install the required packages:
+   ```bash
+   pip install -r requirements.txt
+   ```
+   Here is a sample `requirements.txt`:
+   ```
+   streamlit
+   dotenv
+   pdf2image
+   pillow
+   google-generativeai
+   ```
+
+3. Install **poppler** (required for `pdf2image`):
+   - **Windows**: [Download Poppler for Windows](http://blog.alivate.com.au/poppler-windows/), extract, and add `poppler/bin` to your PATH.
+   - **Linux**: Run `sudo apt install poppler-utils`.
+   - **macOS**: Run `brew install poppler`.
+
+4. Create a `.env` file in the project root with your Google API key:
+   ```
+   GOOGLE_API_KEY=your_google_api_key
+   ```
+
+### Running the App
+1. Start the Streamlit app:
+   ```bash
+   streamlit run app.py
+   ```
+
+2. Open the provided local URL to access the ATS Resume Expert app.
+
+## Usage
+1. **Job Description**: Enter the job description in the text area.
+2. **Resume Upload**: Upload a PDF version of the resume.
+3. **Analyze Resume**:
+   - Click **Tell Me About the Resume** to get an evaluation of the resume based on job requirements.
+   - Click **Percentage Match** to receive a match score along with suggestions for improvement.
+
+## File Structure
+- **app.py**: Main application code.
+- **README.md**: Documentation for the app.
+- **requirements.txt**: List of required Python libraries.
+- **.env**: Environment file for API keys (not included in repository).
+
+## Troubleshooting
+1. **Poppler Installation**: Ensure Poppler is installed and accessible in your PATH if you encounter PDF processing errors.
+2. **API Errors**: Check your Google API key and usage limits if there are issues with the AI model responses.
+
+

Resume Application Tracking System(ATS)/app.py

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+import base64
+import streamlit as st
+import os
+import io
+from PIL import Image 
+import pdf2image
+import google.generativeai as genai
+
+genai.configure(api_key=os.getenv("API_KEY"))
+
+def get_gemini_response(input,pdf_cotent,prompt):
+    model=genai.GenerativeModel('gemini-pro-vision')
+    response=model.generate_content([input,pdf_content[0],prompt])
+    return response.text
+
+def input_pdf_setup(uploaded_file):
+    if uploaded_file is not None:
+        ## Convert the PDF to image
+        images=pdf2image.convert_from_bytes(uploaded_file.read())
+
+        first_page=images[0]
+
+        # Convert to bytes
+        img_byte_arr = io.BytesIO()
+        first_page.save(img_byte_arr, format='JPEG')
+        img_byte_arr = img_byte_arr.getvalue()
+
+        pdf_parts = [
+            {
+                "mime_type": "image/jpeg",
+                "data": base64.b64encode(img_byte_arr).decode()  # encode to base64
+            }
+        ]
+        return pdf_parts
+    else:
+        raise FileNotFoundError("No file uploaded")
+
+## Streamlit App
+
+st.set_page_config(page_title="ATS Resume EXpert")
+st.header("ATS Tracking System")
+input_text=st.text_area("Job Description: ",key="input")
+uploaded_file=st.file_uploader("Upload your resume(PDF)...",type=["pdf"])
+
+
+if uploaded_file is not None:
+    st.write("PDF Uploaded Successfully")
+
+
+submit1 = st.button("Tell Me About the Resume")
+
+#submit2 = st.button("How Can I Improvise my Skills")
+
+submit3 = st.button("Percentage match")
+
+input_prompt1 = """
+ You are an experienced Technical Human Resource Manager,your task is to review the provided resume against the job description. 
+  Please share your professional evaluation on whether the candidate's profile aligns with the role. 
+ Highlight the strengths and weaknesses of the applicant in relation to the specified job requirements.
+"""
+
+input_prompt3 = """
+You are an skilled ATS (Applicant Tracking System) scanner with a deep understanding of data science and ATS functionality, 
+your task is to evaluate the resume against the provided job description. give me the percentage of match if the resume matches
+the job description. First the output should come as percentage and then keywords missing and last final thoughts.
+"""
+
+if submit1:
+    if uploaded_file is not None:
+        pdf_content=input_pdf_setup(uploaded_file)
+        response=get_gemini_response(input_prompt1,pdf_content,input_text)
+        st.subheader("The Repsonse is")
+        st.write(response)
+    else:
+        st.write("Please uplaod the resume")
+
+elif submit3:
+    if uploaded_file is not None:
+        pdf_content=input_pdf_setup(uploaded_file)
+        response=get_gemini_response(input_prompt3,pdf_content,input_text)
+        st.subheader("The Repsonse is")
+        st.write(response)
+    else:
+        st.write("Please uplaod the resume")
+
+
+
+
+
+
+
+
+

Resume Application Tracking System(ATS)/requirements.txt

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+streamlit
+google-generativeai
+python-dotenv
+langchain
+PyPDF2
+faiss-cpu
+langchain_google_genai