Update app.py

app.py

@@ -5,9 +5,15 @@ import faiss
 from sentence_transformers import SentenceTransformer
 from groq import Groq
 
-# Initialize LLM and embeddings
-client = Groq()
+# ---------------- CONFIG ----------------
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+if not GROQ_API_KEY:
+    raise ValueError("GROQ_API_KEY not found. Please set it in Hugging Face Secrets.")
+
+# ---------------- INIT ----------------
+client = Groq(api_key=GROQ_API_KEY)
 embedder = SentenceTransformer("all-MiniLM-L6-v2")
+
 EMBED_DIM = 384
 faiss_index = faiss.IndexFlatIP(EMBED_DIM)
 candidates = []
@@ -26,94 +32,158 @@ def llm_generate(prompt: str):
     )
     return completion.choices[0].message.content
 
-# ---------------- FUNCTIONALITY ----------------
-
+# ---------------- CORE FEATURES ----------------
 def add_candidate(name, skills, experience):
-    profile_text = f"Name: {name}\nSkills: {skills}\nExperience: {experience}"
-    vec = embed_text(profile_text)
+    if not name or not skills:
+        return "⚠️ Please fill in required fields."
+
+    profile = f"Name: {name}\nSkills: {skills}\nExperience: {experience}"
+    vec = embed_text(profile)
     faiss_index.add(vec)
-    candidates.append(profile_text)
-    return f"Candidate '{name}' added successfully!"
+    candidates.append(profile)
+
+    return f"✅ Candidate **{name}** added successfully."
 
 def generate_bio(raw_data):
-    prompt = f"""You are a professional HR recruiter.
-Write a concise, formal candidate bio using the data below.
+    prompt = f"""
+You are a professional HR recruiter.
+Write a concise, formal candidate bio.
+
 Rules:
 - Professional tone
-- No exaggeration
 - No emojis
+- No exaggeration
 - Max 120 words
 
 Candidate Data:
-{raw_data}"""
+{raw_data}
+"""
     return llm_generate(prompt)
 
 def rewrite_job(job_desc):
-    prompt = f"""Rewrite the job description below to comply with professional recruitment standards.
-Ensure it is inclusive, clear, and well-structured.
+    prompt = f"""
+Rewrite the job description below to meet modern recruitment standards.
+Make it inclusive, clear, and well-structured.
 
 Job Description:
-{job_desc}"""
+{job_desc}
+"""
     return llm_generate(prompt)
 
 def recommend_candidates(job_query):
-    if len(candidates) == 0:
-        return "No candidates available.", "", ""
-    
+    if not candidates:
+        return "No candidates available.", "", "⚠️ Add candidates first."
+
     job_vec = embed_text(job_query)
     scores, ids = faiss_index.search(job_vec, k=min(3, len(candidates)))
-    
-    top_candidates = []
-    explanations = []
-    
-    for rank, idx in enumerate(ids[0]):
+
+    ranked = []
+    reasons = []
+
+    for i, idx in enumerate(ids[0]):
         candidate = candidates[idx]
-        explain_prompt = f"""Explain why the candidate below is a good match for the job.
-Base reasoning only on skills and experience.
-Keep it concise and professional.
+        explain_prompt = f"""
+Explain why this candidate matches the job.
+Focus only on skills and experience.
 
 Candidate:
 {candidate}
 
-Job:
-{job_query}"""
-        explanation = llm_generate(explain_prompt)
-        top_candidates.append(f"Rank {rank+1}: {candidate}")
-        explanations.append(explanation)
-    
-    return "\n\n".join(top_candidates), "\n\n".join(explanations), "Done"
-
-# ---------------- GRADIO INTERFACE ----------------
-
-with gr.Blocks() as demo:
-    gr.Markdown("# AI-Powered Recruitment MVP")
-
-    with gr.Tab("Add Candidate"):
-        name_input = gr.Textbox(label="Candidate Name")
-        skills_input = gr.Textbox(label="Skills")
-        exp_input = gr.Textbox(label="Experience")
-        add_btn = gr.Button("Add Candidate")
-        add_output = gr.Textbox(label="Status")
-        add_btn.click(add_candidate, inputs=[name_input, skills_input, exp_input], outputs=add_output)
-
-    with gr.Tab("Candidate Bio Generator"):
-        raw_input = gr.Textbox(label="Candidate Raw Data", lines=5)
-        bio_btn = gr.Button("Generate Bio")
-        bio_output = gr.Textbox(label="Candidate Bio", lines=5)
-        bio_btn.click(generate_bio, inputs=raw_input, outputs=bio_output)
-
-    with gr.Tab("Job Description Filter"):
-        job_input = gr.Textbox(label="Job Description", lines=5)
-        rewrite_btn = gr.Button("Rewrite Job")
-        rewrite_output = gr.Textbox(label="Rewritten Job Description", lines=5)
-        rewrite_btn.click(rewrite_job, inputs=job_input, outputs=rewrite_output)
-
-    with gr.Tab("Candidate Recommendation"):
-        query_input = gr.Textbox(label="Job Requirements", lines=5)
-        rec_btn = gr.Button("Recommend Candidates")
-        rec_candidates = gr.Textbox(label="Top Candidates", lines=5)
-        rec_explanation = gr.Textbox(label="Recommendation Explanation", lines=10)
-        rec_status = gr.Textbox(label="Status")
-        rec_btn.click(recommend_candidates, inputs=query_input, outputs=[rec_candidates, rec_explanation, rec_status])
+Job Requirements:
+{job_query}
+"""
+        ranked.append(f"### Rank {i+1}\n{candidate}")
+        reasons.append(llm_generate(explain_prompt))
+
+    return "\n\n".join(ranked), "\n\n".join(reasons), "✅ Recommendations generated"
+
+# ---------------- UI ----------------
+theme = gr.themes.Soft(
+    primary_hue="blue",
+    secondary_hue="slate",
+    neutral_hue="gray",
+)
+
+with gr.Blocks(theme=theme) as demo:
+
+    gr.Markdown("""
+    # 🤖 AI Recruitment Assistant  
+    **Smart hiring tools for modern recruiters**
+    """)
+
+    gr.Markdown("---")
+
+    with gr.Tabs():
+
+        with gr.Tab("➕ Add Candidate"):
+            gr.Markdown("### Candidate Profile Entry")
+
+            with gr.Row():
+                with gr.Column():
+                    name = gr.Textbox(label="Candidate Name *", placeholder="e.g. Ali Khan")
+                    skills = gr.Textbox(label="Key Skills *", placeholder="Python, React, SQL, ML")
+                    experience = gr.Textbox(label="Experience Summary", lines=4)
+
+                    add_btn = gr.Button("Add Candidate", variant="primary")
+                    status = gr.Markdown()
+
+                with gr.Column():
+                    gr.Markdown("""
+                    #### 💡 Tips
+                    - Be specific with skills  
+                    - Mention years & domain  
+                    - Clean data = better matching  
+                    """)
+
+            add_btn.click(add_candidate, [name, skills, experience], status)
+
+        with gr.Tab("🧾 Candidate Bio Generator"):
+            raw_data = gr.Textbox(lines=6, placeholder="Paste candidate CV data...")
+            bio_btn = gr.Button("Generate Bio", variant="primary")
+            bio_output = gr.Textbox(lines=6, label="Generated Bio")
+
+            bio_btn.click(generate_bio, raw_data, bio_output)
+
+        with gr.Tab("📄 Job Description Optimizer"):
+            job_desc = gr.Textbox(lines=6, placeholder="Paste job description...")
+            rewrite_btn = gr.Button("Rewrite Professionally", variant="primary")
+            rewrite_output = gr.Textbox(lines=6)
+
+            rewrite_btn.click(rewrite_job, job_desc, rewrite_output)
+
+        with gr.Tab("🎯 Candidate Recommendation"):
+            job_query = gr.Textbox(lines=5, placeholder="Enter job requirements...")
+            rec_btn = gr.Button("Find Best Matches", variant="primary")
+
+            with gr.Row():
+                rec_candidates = gr.Markdown()
+                rec_explanation = gr.Textbox(lines=10)
+
+            rec_status = gr.Markdown()
+
+            rec_btn.click(
+                recommend_candidates,
+                job_query,
+                [rec_candidates, rec_explanation, rec_status]
+            )
+
+        with gr.Tab("📘 About This App"):
+            gr.Markdown("""
+            ## What is this application?
+            An AI-powered recruitment assistant that automates:
+            - Candidate shortlisting
+            - Bio generation
+            - Job optimization
+            - Intelligent matching
+
+            ## Example
+            Job: *Python developer with ML experience*  
+            Output: Ranked candidates with explanations
+
+            ## Tech Stack
+            Groq • FAISS • Sentence Transformers • Gradio
+            """)
+
+    gr.Markdown("--- Built for Hugging Face Spaces 🚀")
 
 demo.launch()