Update app.py

app.py

@@ -1,73 +1,69 @@
-# Import necessary libraries
+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
 from convert import ExtractPDFText
-from ATS_score import calculateATSscore
+from ATS_score import calculateATSscore, skill_gap_analysis
 from model import modelFeedback
-import streamlit as st
 import time
 
 # Streamlit app title
 st.title("Resume Screening Assistance")
 
-# Initialize session state variables
-if "resume_data" not in st.session_state:
-    st.session_state.resume_data = []
-
-if "jobdescription" not in st.session_state:
-    st.session_state.jobdescription = ""
-
-# Upload Resumes (Multiple PDFs)
-st.session_state.jobdescription = st.text_area("Paste the job description below:")
-
-# Input Job Description
-pdf_files = st.file_uploader("Upload your resumes (PDF only):", type="pdf", accept_multiple_files=True)
-
-if pdf_files:
-    st.session_state.resume_data = [] 
-    for pdf in pdf_files:
-        extracted_text = ExtractPDFText(pdf)
-        st.session_state.resume_data.append({"name": pdf.name, "text": extracted_text})
-    st.success(f"{len(pdf_files)} resumes uploaded and processed successfully!")
+# Define required skills for the job
+required_skills = ["Python", "Machine Learning", "Cloud (AWS/Azure)", "Data Analysis", "React", "Docker"]
 
-# Submit button
-submit = st.button("Submit")
+# Job Description Input
+job_description = st.text_area("Paste the job description below:")
 
+# Upload Resumes
+uploaded_files = st.file_uploader("Upload your resumes (PDF only):", type="pdf", accept_multiple_files=True)
 
-# Define start function
-def start():
-    if st.session_state.resume_data and st.session_state.jobdescription:
-        st.subheader("Top Matches:")
+if uploaded_files and job_description:
+    resumes_data = []
+    for file in uploaded_files:
+        text = ExtractPDFText(file)
+        ats_score = calculateATSscore(text, job_description)
+        skill_analysis = skill_gap_analysis(text, required_skills)
+        feedback = modelFeedback(ats_score, text, skill_analysis['missing'])
+        resumes_data.append({
+            "name": file.name,
+            "ATS Score": ats_score,
+            "Skills Present": skill_analysis['present'],
+            "Missing Skills": skill_analysis['missing'],
+            "Feedback": feedback
+        })
 
-        # Calculate ATS scores and store them in the resumes
-        for resume in st.session_state.resume_data:
-            with st.spinner(f"Analyzing {resume['name']}..."):
-                # Calculate ATS score
-                ATS_score = calculateATSscore(resume["text"], st.session_state.jobdescription)
-                
-                if ATS_score is None:
-                    st.warning(f"Warning: Unable to calculate ATS score for {resume['name']}.")
-                    continue  # Skip this resume if ATS score is None
-                
-                resume["ATS_score"] = ATS_score  # Add ATS score to resume data
-                
-                # Generate feedback from model
-                resume["model_feedback"] = modelFeedback(ATS_score, resume["text"])  # Store model feedback in the resume data
-                
-                time.sleep(1)  # Optional: Simulate processing time
-        
-        # Sort resumes based on ATS score in descending order
-        sorted_resumes = sorted(st.session_state.resume_data, key=lambda x: x["ATS_score"], reverse=True)
-        
-        # Display the results
-        for rank, resume in enumerate(sorted_resumes, 1):
-            st.write(f"##### Resume: {resume['name']}")
-            st.write(f"**ATS Score:** {int(resume['ATS_score']*100)}%")
-            st.write(f"**Ranking:** {rank}")
-            st.write(f"**Summary:** {resume['model_feedback']}")  # Display the individual feedback for each resume
-            st.write("---")
-    else:
-        st.info("Please upload resumes and provide a job description.")
+    # Sort resumes by ATS score
+    sorted_resumes = sorted(resumes_data, key=lambda x: x["ATS Score"], reverse=True)
+    
+    # Display results
+    st.subheader("Top Matches:")
+    for idx, resume in enumerate(sorted_resumes, 1):
+        st.write(f"### {idx}. {resume['name']}")
+        st.write(f"**ATS Score:** {resume['ATS Score']*100:.0f}%")
+        st.write("**Missing Skills:**", ", ".join(resume["Missing Skills"]) if resume["Missing Skills"] else "None")
+        st.write("**Feedback:**", resume["Feedback"])
+        st.write("---")
 
+    # Skill Gap Analysis Table
+    st.subheader("Skill Gap Analysis")
+    for resume in sorted_resumes:
+        st.write(f"**{resume['name']}**")
+        skill_data = {
+            "Required Skills": required_skills,
+            "Present in Resume": ["✅" if skill in resume["Skills Present"] else "❌" for skill in required_skills],
+            "Missing": [skill if skill in resume["Missing Skills"] else "—" for skill in required_skills]
+        }
+        df = pd.DataFrame(skill_data)
+        st.table(df)
 
-# Process when submit button is clicked
-if submit:
-    start()
+    # Visualize ATS Scores
+    st.subheader("ATS Score Distribution")
+    names = [r["name"] for r in sorted_resumes]
+    scores = [r["ATS Score"]*100 for r in sorted_resumes]
+    plt.figure(figsize=(8, 5))
+    plt.bar(names, scores)
+    plt.title("ATS Scores by Resume")
+    plt.ylabel("ATS Score (%)")
+    plt.xticks(rotation=45)
+    st.pyplot(plt)