Update app.py

app.py

@@ -1,5 +1,4 @@
 import gradio as gr
-import sqlite3
 import re
 import numpy as np
 import pandas as pd
@@ -9,86 +8,17 @@ from sentence_transformers import SentenceTransformer
 from sklearn.metrics.pairwise import cosine_similarity
 import spacy
 from fpdf import FPDF
-import hashlib
-import subprocess
 
 # ---------------------------
 # Load SpaCy model
 # ---------------------------
-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")
+nlp = spacy.load("en_core_web_sm")
 
 # Load sentence-transformers model
 model = SentenceTransformer('all-MiniLM-L6-v2')
 
 # ---------------------------
-# SQLite DB setup
-# ---------------------------
-conn = sqlite3.connect('resumes.db', check_same_thread=False)
-cursor = conn.cursor()
-
-cursor.execute("""
-CREATE TABLE IF NOT EXISTS users (
-    id INTEGER PRIMARY KEY,
-    username TEXT UNIQUE,
-    password_hash TEXT
-)
-""")
-
-cursor.execute("""
-CREATE TABLE IF NOT EXISTS analyses (
-    id INTEGER PRIMARY KEY,
-    user_id INTEGER,
-    resume_text TEXT,
-    jd_text TEXT,
-    final_score REAL,
-    keyword_score REAL,
-    semantic_score REAL,
-    section_scores TEXT,
-    tips TEXT,
-    date TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
-    FOREIGN KEY(user_id) REFERENCES users(id)
-)
-""")
-conn.commit()
-
-# ---------------------------
-# Authentication Functions
-# ---------------------------
-def hash_password(password):
-    return hashlib.sha256(password.encode()).hexdigest()
-
-def is_valid_username(username):
-    return re.match(r'^\w+$', username) is not None
-
-def signup(username, password):
-    username = username.strip()
-    password = password.strip()
-    if not is_valid_username(username):
-        return "❌ Invalid username. Use only letters, numbers, underscore."
-    try:
-        cursor.execute("INSERT INTO users (username, password_hash) VALUES (?,?)",
-                       (username, hash_password(password)))
-        conn.commit()
-        return "✅ Signup successful! Please login."
-    except sqlite3.IntegrityError:
-        return "❌ Username already exists. Try a different one."
-
-def login(username, password):
-    username = username.strip()
-    password = password.strip()
-    cursor.execute("SELECT id, password_hash FROM users WHERE username=?", (username,))
-    row = cursor.fetchone()
-    if row and row[1] == hash_password(password):
-        return f"✅ Login successful! User ID: {row[0]}", row[0]
-    else:
-        return "❌ Invalid username or password", None
-
-# ---------------------------
-# Resume Parsing
+# Resume Parsing Functions
 # ---------------------------
 def extract_text_from_pdf(file):
     try:
@@ -97,7 +27,8 @@ def extract_text_from_pdf(file):
         for page in reader.pages:
             text += page.extract_text() or ""
         return text
-    except:
+    except Exception as e:
+        print("Error reading PDF:", e)
         return ""
 
 def extract_text_from_docx(file):
@@ -105,7 +36,8 @@ def extract_text_from_docx(file):
         doc = Document(file)
         text = "\n".join([p.text for p in doc.paragraphs])
         return text
-    except:
+    except Exception as e:
+        print("Error reading DOCX:", e)
         return ""
 
 def extract_skills(jd_text):
@@ -121,8 +53,8 @@ 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:
-        pass
+    except Exception as e:
+        print("Error splitting sections:", e)
     return sections
 
 def compute_scores(resume_text, jd_text, required_skills):
@@ -140,7 +72,8 @@ def compute_scores(resume_text, jd_text, required_skills):
         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:
+    except Exception as e:
+        print("Error computing scores:", e)
         return 0,0,0,{"Education":0,"Experience":0,"Skills":0},[]
 
 # ---------------------------
@@ -149,8 +82,8 @@ def compute_scores(resume_text, jd_text, required_skills):
 def export_csv(df, filename="ats_report.csv"):
     try:
         df.to_csv(filename, index=False)
-    except:
-        pass
+    except Exception as e:
+        print("Error exporting CSV:", e)
     return filename
 
 def export_pdf(df, filename="ats_report.pdf"):
@@ -171,22 +104,23 @@ def export_pdf(df, filename="ats_report.pdf"):
             pdf.multi_cell(0, 10, row["Tips"])
             pdf.ln(5)
         pdf.output(filename)
-    except:
-        pass
+    except Exception as e:
+        print("Error exporting PDF:", e)
     return filename
 
 # ---------------------------
-# AI Resume Rewriter (Simple)
+# AI Resume Rewriter
 # ---------------------------
 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
-        for skill in required_skills:
-            if skill.lower() not in resume_text.lower():
-                rewritten += f"\n- Experience with {skill}"
+        if missing_skills:
+            rewritten += "\n\n### Suggested Skills to Add:\n" + "\n".join([f"- {s}" for s in missing_skills])
         return rewritten
-    except:
+    except Exception as e:
+        print("Error in AI rewriter:", e)
         return resume_text
 
 # ---------------------------
@@ -229,73 +163,52 @@ def generate_feedback(resume_text, jd_text):
         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:
+    except Exception as e:
+        print("Error generating feedback:", e)
         return "Feedback unavailable."
 
 # ---------------------------
 # Multi-JD Analysis
 # ---------------------------
-def analyze_multi_jd(user_id, resume_file, jd_texts):
-    try:
-        if resume_file.name.endswith(".pdf"):
-            resume_text = extract_text_from_pdf(resume_file)
-        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:
-            required_skills = extract_skills(jd)
-            final_score, keyword_score, semantic_score, section_scores, tips = compute_scores(resume_text, jd, required_skills)
-            section_scores_str = "\n".join([f"{k}: {v:.2%}" for k,v in section_scores.items()])
-            tips_str = "\n".join(tips) if tips else "No suggestions"
-            results.append({
-                "JD": jd[:50]+"..." if len(jd)>50 else jd,
-                "Final Score": f"{final_score:.2%}",
-                "Keyword Score": f"{keyword_score:.2%}",
-                "Semantic Score": f"{semantic_score:.2%}",
-                "Section Scores": section_scores_str,
-                "Tips": tips_str
-            })
-            cursor.execute("""
-            INSERT INTO analyses (user_id, resume_text, jd_text, final_score, keyword_score, semantic_score, section_scores, tips)
-            VALUES (?,?,?,?,?,?,?,?)""",
-                           (user_id, resume_text, jd, final_score, keyword_score, semantic_score, str(section_scores), tips_str))
-            conn.commit()
-        df = pd.DataFrame(results)
-        export_csv(df)
-        export_pdf(df)
-        return df
-    except:
-        return pd.DataFrame()
+def analyze_multi_jd(resume_file, jd_texts):
+    file_ext = resume_file.name.split('.')[-1].lower()
+    if file_ext == "pdf":
+        resume_text = extract_text_from_pdf(resume_file)
+    elif file_ext == "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:
+        required_skills = extract_skills(jd)
+        final_score, keyword_score, semantic_score, section_scores, tips = compute_scores(resume_text, jd, required_skills)
+        section_scores_str = "\n".join([f"{k}: {v:.2%}" for k,v in section_scores.items()])
+        tips_str = "\n".join(tips) if tips else "No suggestions"
+        results.append({
+            "JD": jd[:50]+"..." if len(jd)>50 else jd,
+            "Final Score": f"{final_score:.2%}",
+            "Keyword Score": f"{keyword_score:.2%}",
+            "Semantic Score": f"{semantic_score:.2%}",
+            "Section Scores": section_scores_str,
+            "Tips": tips_str
+        })
+    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
 
 # ---------------------------
 # Gradio Interface
 # ---------------------------
-def analyze_gradio(resume_file, jd_text, username, password):
-    login_msg, user_id = login(username, password)
-    if not user_id:
-        return None, None, login_msg, ""
-    df = analyze_multi_jd(user_id, resume_file, jd_text)
-    try:
-        if resume_file.name.endswith(".pdf"):
-            resume_text = extract_text_from_pdf(resume_file)
-        elif resume_file.name.endswith(".docx"):
-            resume_text = extract_text_from_docx(resume_file)
-    except:
-        resume_text = ""
-    feedback = generate_feedback(resume_text, jd_text)
-    rewritten_resume = ai_resume_rewriter(resume_text, jd_text)
-    return "ats_report.csv", "ats_report.pdf", feedback, rewritten_resume
-
 iface = gr.Interface(
-    fn=analyze_gradio,
+    fn=analyze_multi_jd,
     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="Username"),
-        gr.Textbox(label="Password", type="password")
+        gr.Textbox(label="Paste Job Description(s) (Separate multiple JDs with double line breaks)", lines=10)
     ],
     outputs=[
         gr.File(label="Download CSV Report"),