Create app.py

app.py

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+import os
+import gradio as gr
+import numpy as np
+import faiss
+from sentence_transformers import SentenceTransformer
+from groq import Groq
+
+# Initialize LLM and embeddings
+client = Groq()
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+EMBED_DIM = 384
+faiss_index = faiss.IndexFlatIP(EMBED_DIM)
+candidates = []
+
+# ---------------- HELPERS ----------------
+def embed_text(text: str):
+    vec = embedder.encode([text])
+    vec = vec / np.linalg.norm(vec)
+    return vec.astype("float32")
+
+def llm_generate(prompt: str):
+    completion = client.chat.completions.create(
+        model="llama-3.1-8b-instant",
+        messages=[{"role": "user", "content": prompt}],
+        temperature=0.3
+    )
+    return completion.choices[0].message.content
+
+# ---------------- FUNCTIONALITY ----------------
+
+def add_candidate(name, skills, experience):
+    profile_text = f"Name: {name}\nSkills: {skills}\nExperience: {experience}"
+    vec = embed_text(profile_text)
+    faiss_index.add(vec)
+    candidates.append(profile_text)
+    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.
+Rules:
+- Professional tone
+- No exaggeration
+- No emojis
+- Max 120 words
+
+Candidate 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.
+
+Job Description:
+{job_desc}"""
+    return llm_generate(prompt)
+
+def recommend_candidates(job_query):
+    if len(candidates) == 0:
+        return "No candidates available.", "", ""
+    
+    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]):
+        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.
+
+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])
+
+demo.launch()