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app.py

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+import os
+import time
+import json
+import pandas as pd
+import streamlit as st
+import docx
+import fitz as pymupdf
+from dotenv import load_dotenv
+import google.generativeai as genai
+from prompt import extract_skill, prompt_first_chunks
+
+# Load environment variables
+load_dotenv()
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+
+genai.configure(api_key=GOOGLE_API_KEY)
+model = genai.GenerativeModel('gemini-1.0-pro')
+
+def extract_skills_from_job_description(job_description):
+    """Extract skills from the provided job description."""
+    jd_prompt = extract_skill.format(job_description=job_description)
+    response = model.generate_content(jd_prompt, generation_config=genai.types.GenerationConfig(temperature=0.0))
+    if response._result.candidates[0].content:
+        generated_response = response._result.candidates[0].content.parts[0].text.replace("```json\n", "").rstrip("\n").replace("```", "")
+        json_array = json.loads(generated_response)
+        elements_to_prepend = ['Name', 'Address', 'EmailId', 'Mobile_number']
+        return elements_to_prepend + json_array
+    else:
+        st.error("Error with Generative AI Model.")
+        return []
+
+def process_resume(file, jd_skills):
+    """Extract skills and ratings from a single resume file."""
+    text = ""
+    if file.name.endswith('.pdf'):
+        # Process PDF files
+        document = pymupdf.open(stream=file.read(), filetype="pdf")
+        for page_num in range(len(document)):
+            page = document.load_page(page_num)
+            text += page.get_text()
+        document.close()
+    elif file.name.endswith('.docx'):
+        # Process DOCX files
+        document = docx.Document(file)
+        for paragraph in document.paragraphs:
+            text += paragraph.text + "\n"
+        for table in document.tables:
+            for row in table.rows:
+                for cell in row.cells:
+                    text += cell.text + "\n"
+    
+    # Generate response from model
+    resume_prompt = prompt_first_chunks.format(resume=text, jd_skill=jd_skills)
+    response = model.generate_content(resume_prompt, generation_config=genai.types.GenerationConfig(temperature=0.0))
+    try:
+        json_array = json.loads(response._result.candidates[0].content.parts[0].text)
+        return pd.DataFrame([json_array], columns=jd_skills)
+    except Exception as e:
+        st.error(f"Error processing file {file.name}: {e}")
+        return pd.DataFrame()
+
+def main():
+    st.title("Resume Filtering Based on Job Description")
+
+    # Upload resumes
+    uploaded_files = st.file_uploader("Upload Resumes (PDF/DOCX)", type=["pdf", "docx"], accept_multiple_files=True)
+
+    # Input job description
+    job_description = st.text_area("Enter Job Description")
+
+    # Process resumes and display results
+    if st.button("Process Resumes"):
+        if not uploaded_files or not job_description:
+            st.warning("Please upload resumes and provide a job description.")
+            return
+
+        jd_skills = extract_skills_from_job_description(job_description)
+        if not jd_skills:
+            return
+
+        all_data = pd.DataFrame(columns=jd_skills)
+
+        for file in uploaded_files:
+            resume_data = process_resume(file, jd_skills)
+            if not resume_data.empty:
+                resume_data["resume_path"] = file.name
+                all_data = pd.concat([all_data, resume_data], ignore_index=True)
+
+        if not all_data.empty:
+            # Calculate total skill ratings
+            skills_columns = all_data.columns[4:-1]
+            all_data['total_skill_rating'] = round((all_data[skills_columns].sum(axis=1) / len(skills_columns)) * 100, 2)
+
+            # Display dataframe in Streamlit
+            st.write("### Processed Resume Data:", all_data)
+
+            # Save to CSV
+            csv_path = "processed_resumes.csv"
+            all_data.to_csv(csv_path, index=False)
+            st.success(f"Data saved to {csv_path}")
+            st.download_button(label="Download CSV", data=all_data.to_csv(index=False), file_name="processed_resumes.csv", mime="text/csv")
+
+if __name__ == "__main__":
+    main()

prompt.py

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+from langchain_core.prompts import PromptTemplate
+
+extract_skill = PromptTemplate.from_template("""`You are an experienced HR recruiter, your role involves receiving a ```{job_description}``` for a specific position.
+Your primary responsibility is to compile a list of essential requirements and skills mentioned in the job description.
+Kindly provide the main points in the form of a string list.
+Please give the results as JSON array""")
+
+
+
+
+prompt_first_chunks = PromptTemplate.from_template("""  
+  Candidate Resume information is below.
+----------------------------------
+{resume}
+----------------------------------
+Given the provided resume cotext and no prior knowledge,
+You are an experienced HR recruiter proficient in resume scanning. Your task is to verify if the candidate possesses a specific skill listed in jd_skills based on the resume above. Note that the skill may not be directly listed; you may need to analyze the resume to find the skill.
+Jd_skills: {jd_skill}
+Your response should be as a JSON array with the required Jd_skills as the key and its corresponding answer as the value. 
+Ensure that only the skill listed in jd_skills is matched, and do not assign 1 or 0 values to the name, address, email ID, and mobile number fields.
+The format should be: 
+{{
+  "Name": "full name of candidate in string",
+  "Address": "full address in string",
+  "EmailId": "emailid in string",
+  "Mobile_number":"Candidate mobile number in string",
+  {jd_skill}: 1  (if skill present) or 0 (if skill not present)
+}}
+            """)
+

requirements.txt

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+langchain
+openai
+PyMuPDF
+twilio
+flask
+python-docx
+llama_index
+google-generativeai
+llama-index-llms-azure-openai
+langchain-openai