Update app.py

app.py

@@ -1,38 +1,85 @@
-# Q&A Chatbot
-from langchain.llms import OpenAI
-
-#from dotenv import load_dotenv
-
-#load_dotenv()  # take environment variables from .env.
-
-import streamlit as st
-import os
-
-
-## Function to load OpenAI model and get respones
-
-def get_openai_response(question):
-    llm=OpenAI(model_name="text-davinci-003",temperature=0.5)
-    response=llm(question)
-    return response
-
-##initialize our streamlit app
-
-st.set_page_config(page_title="Q&A Demo")
-
-st.header("Langchain Application")
-
-input=st.text_input("Input: ",key="input")
-response=get_openai_response(input)
-
-submit=st.button("Ask the question")
-
-## If ask button is clicked
-
-if submit:
-    st.subheader("The Response is")
-    st.write(response)
-
-
-
-
+import streamlit as st
+import PyPDF2
+import re
+
+# Function to extract text from a PDF
+def extract_text_from_pdf(uploaded_file):
+    pdf_reader = PyPDF2.PdfReader(uploaded_file)
+    text = " ".join([page.extract_text() for page in pdf_reader.pages if page.extract_text()])
+    return text
+
+# Function to extract skills from text (reference resumes and user resumes)
+def extract_skills_from_text(text):
+    # A simple way to extract skills based on words in the resume.
+    # This can be more advanced by integrating NER (Named Entity Recognition) or using predefined skill lists.
+    skills = re.findall(r'\b[A-Za-z]+\b', text.lower())  # Extract words
+    skills = set(skills)  # Remove duplicates
+    return skills
+
+# Function to evaluate user's resume based on job description
+def evaluate_resume(candidate_text, job_description_text):
+    # Extract skills dynamically from job description
+    job_description_skills = extract_skills_from_text(job_description_text)
+
+    # Extract skills from candidate's resume
+    candidate_skills = extract_skills_from_text(candidate_text)
+
+    # Calculate matching skills
+    matching_skills = job_description_skills.intersection(candidate_skills)
+    missing_skills = job_description_skills.difference(candidate_skills)
+
+    # Calculate improvement skills (skills present in the JD but not in candidate's resume)
+    improvement_skills = job_description_skills.difference(matching_skills)
+
+    # Calculate matching percentage (based on skills)
+    matching_percentage = (len(matching_skills) / len(job_description_skills)) * 100 if len(job_description_skills) > 0 else 0
+
+    result = {
+        "matching_skills": list(matching_skills),
+        "missing_skills": list(missing_skills),
+        "improvement_skills": list(improvement_skills),
+        "matching_percentage": round(matching_percentage, 2)
+    }
+
+    return result
+
+# Streamlit UI
+st.title("📄 Resume Screening with Job Description Matching")
+st.write("Upload a candidate's resume and provide a job description to check the matching percentage based on skills.")
+
+# Upload job description
+st.subheader("Upload Job Description")
+job_description_file = st.file_uploader("Upload Job Description (PDF)", type=["pdf"])
+
+# Upload candidate resume
+st.subheader("Upload Candidate Resume")
+uploaded_file = st.file_uploader("Upload Candidate Resume (PDF)", type=["pdf"])
+
+if uploaded_file and job_description_file:
+    st.write("🔍 **Processing resume and job description...**")
+
+    # Extract text from candidate resume
+    candidate_text = extract_text_from_pdf(uploaded_file)
+
+    # Extract text from job description
+    job_description_text = extract_text_from_pdf(job_description_file)
+
+    # Evaluate resume based on job description
+    evaluation = evaluate_resume(candidate_text, job_description_text)
+
+    # Display results
+    st.subheader("📌 Evaluation Result:")
+    st.write(f"**🔢 Matching Percentage:** {evaluation['matching_percentage']}%")
+    # st.write(f"**✅ Matching Skills:** {', '.join(evaluation['matching_skills']) or 'None'}")
+    # st.write(f"**❌ Missing Skills:** {', '.join(evaluation['missing_skills']) or 'None'}")
+    st.write(f"**💡 Improvement Skills (Required but Missing):** {', '.join(evaluation['improvement_skills']) or 'None'}")
+
+
+    if evaluation["matching_percentage"] > 70:
+        st.success("✅ Resume is a strong match for the job description!")
+    elif evaluation["matching_percentage"] > 40:
+        st.warning("⚠️ Resume is a partial match for the job description.")
+    else:
+        st.error("❌ Resume does not match well with the job description.")
+else:
+    st.write("Please upload both the job description and candidate resume.")