Update app.py

app.py

@@ -1,4 +1,5 @@
 import gradio as gr
+import sqlite3
 import re
 import numpy as np
 import pandas as pd
@@ -8,17 +9,22 @@ from sentence_transformers import SentenceTransformer
 from sklearn.metrics.pairwise import cosine_similarity
 import spacy
 from fpdf import FPDF
+import subprocess
 
 # ---------------------------
 # Load SpaCy model
 # ---------------------------
-nlp = spacy.load("en_core_web_sm")
+try:
+    nlp = spacy.load("en_core_web_sm")
+except OSError:
+    subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"])
+    nlp = spacy.load("en_core_web_sm")
 
 # Load sentence-transformers model
 model = SentenceTransformer('all-MiniLM-L6-v2')
 
 # ---------------------------
-# Resume Parsing Functions
+# Resume Parsing
 # ---------------------------
 def extract_text_from_pdf(file):
     try:
@@ -27,8 +33,7 @@ def extract_text_from_pdf(file):
         for page in reader.pages:
             text += page.extract_text() or ""
         return text
-    except Exception as e:
-        print("Error reading PDF:", e)
+    except:
         return ""
 
 def extract_text_from_docx(file):
@@ -36,8 +41,7 @@ def extract_text_from_docx(file):
         doc = Document(file)
         text = "\n".join([p.text for p in doc.paragraphs])
         return text
-    except Exception as e:
-        print("Error reading DOCX:", e)
+    except:
         return ""
 
 def extract_skills(jd_text):
@@ -53,75 +57,61 @@ def split_sections(resume_text):
         if edu: sections["Education"] = edu.group(2).strip()
         if exp: sections["Experience"] = exp.group(2).strip()
         if skills: sections["Skills"] = skills.group(2).strip()
-    except Exception as e:
-        print("Error splitting sections:", e)
+    except:
+        pass
     return sections
 
 def compute_scores(resume_text, jd_text, required_skills):
-    try:
-        present_skills = [kw for kw in required_skills if kw.lower() in resume_text.lower()]
-        keyword_score = len(present_skills)/max(len(required_skills),1)
-        res_vec = model.encode(resume_text)
-        jd_vec  = model.encode(jd_text)
-        semantic_score = cosine_similarity([res_vec],[jd_vec])[0][0]
-        sections = split_sections(resume_text)
-        section_scores = {}
-        for sec, text in sections.items():
-            sec_present = [kw for kw in required_skills if kw.lower() in text.lower()]
-            section_scores[sec] = len(sec_present)/max(len(required_skills),1)
-        final_score = 0.6*keyword_score + 0.4*semantic_score
-        tips = [f"⚠️ Add '{skill}' to improve ATS match" for skill in required_skills if skill.lower() not in resume_text.lower()]
-        return final_score, keyword_score, semantic_score, section_scores, tips
-    except Exception as e:
-        print("Error computing scores:", e)
-        return 0,0,0,{"Education":0,"Experience":0,"Skills":0},[]
+    present_skills = [kw for kw in required_skills if kw.lower() in resume_text.lower()]
+    keyword_score = len(present_skills)/max(len(required_skills),1)
+    res_vec = model.encode(resume_text)
+    jd_vec  = model.encode(jd_text)
+    semantic_score = cosine_similarity([res_vec],[jd_vec])[0][0]
+    sections = split_sections(resume_text)
+    section_scores = {}
+    for sec, text in sections.items():
+        sec_present = [kw for kw in required_skills if kw.lower() in text.lower()]
+        section_scores[sec] = len(sec_present)/max(len(required_skills),1)
+    final_score = 0.6*keyword_score + 0.4*semantic_score
+    tips = [f"⚠️ Add '{skill}' to improve ATS match" for skill in required_skills if skill.lower() not in resume_text.lower()]
+    return final_score, keyword_score, semantic_score, section_scores, tips
 
 # ---------------------------
 # CSV & PDF Export
 # ---------------------------
 def export_csv(df, filename="ats_report.csv"):
-    try:
-        df.to_csv(filename, index=False)
-    except Exception as e:
-        print("Error exporting CSV:", e)
+    df.to_csv(filename, index=False)
     return filename
 
 def export_pdf(df, filename="ats_report.pdf"):
-    try:
-        pdf = FPDF()
-        pdf.add_page()
-        pdf.set_font("Arial", size=12)
-        pdf.cell(200, 10, txt="ATS Resume Screening Report", ln=True, align="C")
-        pdf.ln(10)
-        for i, row in df.iterrows():
-            pdf.cell(200, 10, txt=f"JD {i+1}: {row['JD']}", ln=True)
-            pdf.cell(200, 10, txt=f"Final Score: {row['Final Score']}", ln=True)
-            pdf.cell(200, 10, txt=f"Keyword Score: {row['Keyword Score']}", ln=True)
-            pdf.cell(200, 10, txt=f"Semantic Score: {row['Semantic Score']}", ln=True)
-            pdf.cell(200, 10, txt="Section Scores:", ln=True)
-            pdf.multi_cell(0, 10, row["Section Scores"])
-            pdf.cell(200, 10, txt="Tips:", ln=True)
-            pdf.multi_cell(0, 10, row["Tips"])
-            pdf.ln(5)
-        pdf.output(filename)
-    except Exception as e:
-        print("Error exporting PDF:", e)
+    pdf = FPDF()
+    pdf.add_page()
+    pdf.set_font("Arial", size=12)
+    pdf.cell(200, 10, txt="ATS Resume Screening Report", ln=True, align="C")
+    pdf.ln(10)
+    for i, row in df.iterrows():
+        pdf.cell(200, 10, txt=f"JD {i+1}: {row['JD']}", ln=True)
+        pdf.cell(200, 10, txt=f"Final Score: {row['Final Score']}", ln=True)
+        pdf.cell(200, 10, txt=f"Keyword Score: {row['Keyword Score']}", ln=True)
+        pdf.cell(200, 10, txt=f"Semantic Score: {row['Semantic Score']}", ln=True)
+        pdf.cell(200, 10, txt="Section Scores:", ln=True)
+        pdf.multi_cell(0, 10, row["Section Scores"])
+        pdf.cell(200, 10, txt="Tips:", ln=True)
+        pdf.multi_cell(0, 10, row["Tips"])
+        pdf.ln(5)
+    pdf.output(filename)
     return filename
 
 # ---------------------------
-# AI Resume Rewriter
+# AI Resume Rewriter (Simple)
 # ---------------------------
 def ai_resume_rewriter(resume_text, jd_text):
-    try:
-        required_skills = extract_skills(jd_text)
-        missing_skills = [skill for skill in required_skills if skill.lower() not in resume_text.lower()]
-        rewritten = resume_text
-        if missing_skills:
-            rewritten += "\n\n### Suggested Skills to Add:\n" + "\n".join([f"- {s}" for s in missing_skills])
-        return rewritten
-    except Exception as e:
-        print("Error in AI rewriter:", e)
-        return resume_text
+    required_skills = extract_skills(jd_text)
+    rewritten = resume_text
+    for skill in required_skills:
+        if skill.lower() not in resume_text.lower():
+            rewritten += f"\n- Experience with {skill}"
+    return rewritten
 
 # ---------------------------
 # Feedback Generator
@@ -143,42 +133,32 @@ certification_mapping = {
 }
 
 def generate_feedback(resume_text, jd_text):
-    try:
-        required_skills = extract_skills(jd_text)
-        resume_lower = resume_text.lower()
-        missing_skills = [skill for skill in required_skills if skill.lower() not in resume_lower]
-        skill_suggestions = [f"{s}: {', '.join(skill_course_mapping[s])}" for s in missing_skills if s in skill_course_mapping]
-        cert_suggestions = [f"Consider certification: {certification_mapping[s]}" for s in missing_skills if s in certification_mapping]
-        resume_tips = []
-        if "Education" not in resume_text:
-            resume_tips.append("Include an Education section if missing.")
-        if "Experience" not in resume_text:
-            resume_tips.append("Include an Experience section with quantified achievements.")
-        if "Skills" not in resume_text:
-            resume_tips.append("Add a Skills section highlighting relevant skills.")
-        if len(resume_text.split()) < 200:
-            resume_tips.append("Consider adding more details to increase resume length and content richness.")
-        feedback_text = "### Missing Skills:\n" + ("\n".join(missing_skills) if missing_skills else "None")
-        feedback_text += "\n\n### Suggested Courses:\n" + ("\n".join(skill_suggestions) if skill_suggestions else "No suggestions")
-        feedback_text += "\n\n### Suggested Certifications:\n" + ("\n".join(cert_suggestions) if cert_suggestions else "No suggestions")
-        feedback_text += "\n\n### Resume Optimization Tips:\n" + ("\n".join(resume_tips) if resume_tips else "Your resume looks well-structured.")
-        return feedback_text
-    except Exception as e:
-        print("Error generating feedback:", e)
-        return "Feedback unavailable."
+    required_skills = extract_skills(jd_text)
+    resume_lower = resume_text.lower()
+    missing_skills = [skill for skill in required_skills if skill.lower() not in resume_lower]
+    skill_suggestions = [f"{s}: {', '.join(skill_course_mapping[s])}" for s in missing_skills if s in skill_course_mapping]
+    cert_suggestions = [f"Consider certification: {certification_mapping[s]}" for s in missing_skills if s in certification_mapping]
+    resume_tips = []
+    if "Education" not in resume_text: resume_tips.append("Include an Education section if missing.")
+    if "Experience" not in resume_text: resume_tips.append("Include an Experience section with quantified achievements.")
+    if "Skills" not in resume_text: resume_tips.append("Add a Skills section highlighting relevant skills.")
+    if len(resume_text.split()) < 200: resume_tips.append("Consider adding more details to increase resume length and content richness.")
+    feedback_text = "### Missing Skills:\n" + ("\n".join(missing_skills) if missing_skills else "None")
+    feedback_text += "\n\n### Suggested Courses:\n" + ("\n".join(skill_suggestions) if skill_suggestions else "No suggestions")
+    feedback_text += "\n\n### Suggested Certifications:\n" + ("\n".join(cert_suggestions) if cert_suggestions else "No suggestions")
+    feedback_text += "\n\n### Resume Optimization Tips:\n" + ("\n".join(resume_tips) if resume_tips else "Your resume looks well-structured.")
+    return feedback_text
 
 # ---------------------------
 # Multi-JD Analysis
 # ---------------------------
 def analyze_multi_jd(resume_file, jd_texts):
-    file_ext = resume_file.name.split('.')[-1].lower()
-    if file_ext == "pdf":
+    if resume_file.name.endswith(".pdf"):
         resume_text = extract_text_from_pdf(resume_file)
-    elif file_ext == "docx":
+    elif resume_file.name.endswith(".docx"):
         resume_text = extract_text_from_docx(resume_file)
     else:
         resume_text = ""
-
     jd_list = [jd.strip() for jd in jd_texts.split("\n\n") if jd.strip()]
     results = []
     for jd in jd_list:
@@ -197,27 +177,30 @@ def analyze_multi_jd(resume_file, jd_texts):
     df = pd.DataFrame(results)
     export_csv(df)
     export_pdf(df)
-    feedback = generate_feedback(resume_text, jd_texts)
-    rewritten_resume = ai_resume_rewriter(resume_text, jd_texts)
-    return "ats_report.csv", "ats_report.pdf", feedback, rewritten_resume
+    return df, generate_feedback(resume_text, jd_texts), ai_resume_rewriter(resume_text, jd_texts)
 
 # ---------------------------
 # Gradio Interface
 # ---------------------------
+def analyze_gradio(resume_file, jd_text):
+    df, feedback, rewritten_resume = analyze_multi_jd(resume_file, jd_text)
+    return "ats_report.csv", "ats_report.pdf", feedback, rewritten_resume
+
 iface = gr.Interface(
-    fn=analyze_multi_jd,
+    fn=analyze_gradio,
     inputs=[
         gr.File(label="Upload Resume (PDF/DOCX)"),
-        gr.Textbox(label="Paste Job Description(s) (Separate multiple JDs with double line breaks)", lines=10)
+        gr.Textbox(label="Paste Job Description(s) (Separate multiple JDs with double newline)")
     ],
     outputs=[
         gr.File(label="Download CSV Report"),
         gr.File(label="Download PDF Report"),
-        gr.Textbox(label="Personalized Feedback", lines=15),
-        gr.Textbox(label="AI Suggested Resume Revisions", lines=15)
+        gr.Textbox(label="AI Feedback"),
+        gr.Textbox(label="AI Rewritten Resume")
     ],
-    title="AI-Powered Resume Screening System",
-    description="Upload your resume, paste job descriptions, and get ATS scoring, personalized feedback, and AI suggestions."
+    title="AI Resume Screening & Optimization",
+    description="Upload resume and job description(s) to get ATS scores, section scores, AI feedback, and rewritten resume."
 )
 
-iface.launch()
+if __name__ == "__main__":
+    iface.launch()