Update app.py

app.py

@@ -1,16 +1,18 @@
 import gradio as gr
 from langchain_community.document_loaders.pdf import PyPDFLoader
 from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.vectorstores.faiss import FAISS
+from langchain_groq import ChatGroq
 from langchain_core.prompts import PromptTemplate
 from langchain_core.output_parsers import StrOutputParser
-from langchain_groq import ChatGroq
 from sklearn.metrics.pairwise import cosine_similarity
 from dotenv import load_dotenv
 from fpdf import FPDF
+from wordcloud import WordCloud
+import matplotlib.pyplot as plt
 import numpy as np
 import tempfile
 import os
+import io
 
 load_dotenv()
 groq_api_key = os.getenv("GROQ_API_KEY")
@@ -28,11 +30,11 @@ def extract_skills(text):
     skills_list = ["Python", "SQL", "Machine Learning", "Deep Learning", "NLP", "Data Visualization", "Cloud", "TensorFlow", "PyTorch", "Statistics", "Java", "C++", "HTML", "CSS", "JavaScript"]
     return [skill for skill in skills_list if skill.lower() in text.lower()]
 
-def calculate_match(user_skills, job_skills):
-    common = set(user_skills) & set(job_skills)
-    match_percent = (len(common) / len(job_skills)) * 100 if job_skills else 0
-    missing_skills = list(set(job_skills) - set(user_skills))
-    return round(match_percent, 2), missing_skills
+def generate_learning_resources(missing_skills):
+    suggestions = []
+    for skill in missing_skills:
+        suggestions.append(f"For {skill}: Search for '{skill} tutorial' on YouTube or Coursera.")
+    return "\n".join(suggestions)
 
 def generate_skill_gap_report(user_skills, job_skills, missing_skills, match_percent):
     llm = ChatGroq(model="llama3-8b-8192", temperature=0.2)
@@ -67,79 +69,98 @@ def create_pdf(report_text):
     pdf.output(output_path)
     return output_path
 
-def process_skill_gap(resume_pdf, jd_pdf):
-    if resume_pdf is None or jd_pdf is None:
-        return 0, "❌ Please upload both Resume and Job Description PDFs.", "", "", "", None
+def generate_wordcloud(missing_skills):
+    text = " ".join(missing_skills)
+    wordcloud = WordCloud(width=600, height=400, background_color='black', colormap='Set3').generate(text)
+    buffer = io.BytesIO()
+    plt.figure(figsize=(6,4))
+    plt.imshow(wordcloud, interpolation='bilinear')
+    plt.axis('off')
+    plt.tight_layout()
+    plt.savefig(buffer, format='png')
+    plt.close()
+    buffer.seek(0)
+    return buffer
+
+def process_skill_gap(resume_pdf, jd_pdfs):
+    if resume_pdf is None or jd_pdfs is None:
+        return 0, "❌ Please upload Resume and at least one Job Description PDF.", "", "", "", None, None, ""
 
     resume_text = extract_text_from_pdf(resume_pdf)
-    jd_text = extract_text_from_pdf(jd_pdf)
-
     user_skills = extract_skills(resume_text)
-    job_skills = extract_skills(jd_text)
 
-    match_percent, missing_skills = calculate_match(user_skills, job_skills)
+    best_match_percent = 0
+    best_missing_skills = []
+    best_job_skills = []
+    best_similarity = 0
+    best_report = ""
 
-    embed_model = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
-    vectors = embed_model.embed_documents([resume_text, jd_text])
-    similarity_score = cosine_similarity([vectors[0]], [vectors[1]])[0][0]
-    similarity_percent = round(similarity_score * 100, 2)
+    for jd_pdf in jd_pdfs:
+        jd_text = extract_text_from_pdf(jd_pdf)
+        job_skills = extract_skills(jd_text)
 
-    report = generate_skill_gap_report(user_skills, job_skills, missing_skills, match_percent)
+        common = set(user_skills) & set(job_skills)
+        match_percent = (len(common) / len(job_skills)) * 100 if job_skills else 0
+        missing_skills = list(set(job_skills) - set(user_skills))
 
-    pdf_path = create_pdf(report)
+        embed_model = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+        vectors = embed_model.embed_documents([resume_text, jd_text])
+        similarity_score = cosine_similarity([vectors[0]], [vectors[1]])[0][0]
+        similarity_percent = round(similarity_score * 100, 2)
 
-    skill_text = f"✅ Skill Match: {match_percent}%"
-    missing_text = f"❌ Missing Skills: {', '.join(missing_skills) if missing_skills else 'None'}"
-    similarity_text = f"🔎 Resume vs JD Similarity Score: {similarity_percent}%"
+        if match_percent > best_match_percent:
+            best_match_percent = match_percent
+            best_missing_skills = missing_skills
+            best_job_skills = job_skills
+            best_similarity = similarity_percent
+            best_report = generate_skill_gap_report(user_skills, job_skills, missing_skills, match_percent)
 
-    return match_percent, skill_text, missing_text, similarity_text, report, pdf_path
+    resources = generate_learning_resources(best_missing_skills)
+    pdf_path = create_pdf(best_report)
+    wordcloud_img = generate_wordcloud(best_missing_skills) if best_missing_skills else None
+
+    skill_text = f"✅ Skill Match: {best_match_percent}%"
+    missing_text = f"❌ Missing Skills: {', '.join(best_missing_skills) if best_missing_skills else 'None'}"
+    similarity_text = f"🔎 Best JD Similarity Score: {best_similarity}%"
+
+    return best_match_percent, skill_text, missing_text, similarity_text, best_report, pdf_path, wordcloud_img, resources
 
 with gr.Blocks() as demo:
     gr.HTML("""
-            <style>
-            body {
-                background-color: #121212;
-                color: #ffffff;
-            }
-            .gradio-container {
-                font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-                color: #ffffff;
-            }
-            h1, h2, h3, h4, h5, h6, p, label, textarea, input, .gr-textbox, .gr-button {
-                color: #ffffff !important;
-                background-color: #1e1e1e !important;
-                border-color: #333333 !important;
-            }
-            .gr-button {
-                background-color: #4CAF50 !important;
-                color: white !important;
-            }
-            </style>
-            """)
-
-
-    gr.Markdown("# 🧠 AI Skill Gap Checker")
-    gr.Markdown("Upload your **Resume PDF** and **Job Description PDF** below. The AI will analyze your skill match and generate an improvement report. 📄")
+    <style>
+    body { background-color: #121212; color: #ffffff; }
+    .gradio-container { font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; color: #ffffff; }
+    h1, h2, h3, p, label, textarea, input, .gr-textbox, .gr-button {
+        color: #ffffff !important;
+        background-color: #1e1e1e !important;
+        border-color: #333333 !important;
+    }
+    .gr-button { background-color: #4CAF50 !important; color: white !important; }
+    </style>
+    """)
+
+    gr.Markdown("# 🧠 AI Skill Gap Checker - Advanced Version")
+    gr.Markdown("Upload your **Resume PDF** and **Multiple Job Description PDFs**. The AI will compare and suggest learning resources.")
 
     with gr.Row():
         resume_file = gr.File(label="📄 Upload Resume (PDF)", type="binary")
-        jd_file = gr.File(label="📄 Upload Job Description (PDF)", type="binary")
+        jd_files = gr.File(label="📄 Upload Job Description PDFs (Multiple Allowed)", type="binary", file_types=[".pdf"], file_count="multiple")
 
     submit_btn = gr.Button("🚀 Analyze Skill Gap")
 
-    gr.Markdown("## 🎯 Match Percentage")
     match_slider = gr.Slider(minimum=0, maximum=100, step=1, label="Job Match Percentage (%)", interactive=False)
-
     skill_match_text = gr.Textbox(label="Skill Match Details", interactive=False)
     missing_skills_text = gr.Textbox(label="Missing Skills", interactive=False)
-    similarity_text = gr.Textbox(label="Resume-JD Similarity Score", interactive=False)
+    similarity_text = gr.Textbox(label="Best JD Similarity Score", interactive=False)
     report_output = gr.Textbox(label="AI-Generated Skill Gap Report", lines=12, interactive=False)
     download_pdf = gr.File(label="📥 Download Skill Gap Report (PDF)")
+    wordcloud_output = gr.Image(label="📊 Missing Skills Word Cloud")
+    learning_resources = gr.Textbox(label="📚 AI Learning Resource Recommendations", interactive=False)
 
     submit_btn.click(
         fn=process_skill_gap,
-        inputs=[resume_file, jd_file],
-        outputs=[match_slider, skill_match_text, missing_skills_text, similarity_text, report_output, download_pdf]
+        inputs=[resume_file, jd_files],
+        outputs=[match_slider, skill_match_text, missing_skills_text, similarity_text, report_output, download_pdf, wordcloud_output, learning_resources]
     )
 
 demo.launch()