Create model2.py

pages/model2.py

@@ -0,0 +1,108 @@
+import os
+import zipfile
+import tempfile
+import fitz  
+import streamlit as st
+from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
+
+os.environ["HF_TOKEN"]=os.getenv('HF_Token')
+os.environ["HUGGINGFACEHUB_API_KEY"]=os.getenv('HF_Token')
+
+
+base_llm = HuggingFaceEndpoint(
+    repo_id="meta-llama/Llama-3.1-8B-Instruct",
+    provider="novita",
+    temperature=0.7,
+    max_new_tokens=150,
+    task="conversational"
+)
+
+llm = ChatHuggingFace(
+    llm=base_llm,
+    repo_id="meta-llama/Llama-3.2-3B-Instruct",
+    provider="novita",
+    temperature=0.7,
+    max_new_tokens=200,
+    task="conversational"
+)
+
+def extract_text(file_bytes):
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
+        tmp.write(file_bytes)
+        text = ""
+        doc = fitz.open(tmp.name)
+        for page in doc:
+            text += page.get_text()
+        return text
+
+
+def resume_prompt(text):
+    return (
+        "Extract the following from this resume:\n"
+        "1. Full Name\n2. Education Background\n3. Total Years of Experience\n4. Technical & Soft Skills\n"
+        "5. Key Projects and Outcomes\n\nResume Content:\n" + text
+    )
+
+
+def jd_prompt(text):
+    return (
+        "Extract this information from the job description:\n"
+        "1. Job ID\n2. Company Name\n3. Designation\n4. Required Experience\n"
+        "5. Key Skills\n6. Education\n7. Location\n\nJD Text:\n" + text
+    )
+
+
+def match_prompt(jd_text, all_resumes, top_n=3):
+    return (
+        f"You are an expert recruitment AI. Based on the following job description and candidate summaries, rank the top {top_n} suitable profiles.\n"
+        "Evaluate based on: Skills match, Experience, Education, and Project Relevance.\n"
+        f"\nJob Description:\n{jd_text}\n\nCandidates:\n{all_resumes}\n\n"
+        f"List the Top {top_n} candidates like this:\n"
+        "1. Candidate Name - Matching Reason\n2. Candidate Name - Matching Reason\n..."
+    )
+
+st.set_page_config(page_title="AI Resume Screener", layout="centered")
+st.title("🧠 Smart Resume Screener + JD Extractor")
+st.markdown("Upload a ZIP of resumes and a Job Description to discover top-fit candidates, powered by AI.")
+
+zip_file = st.file_uploader("📁 Upload ZIP of Resumes (PDFs Only)", type="zip")
+jd_file = st.file_uploader("📄 Upload Job Description (PDF/TXT)", type=["pdf", "txt"])
+jd_text_manual = st.text_area("✍️ Or Paste JD Text Directly Below")
+top_n = st.slider("🎯 Number of Top Candidates", 1, 10, 3)
+
+if st.button("🚀 Find Best Matches"):
+    if not zip_file or not (jd_file or jd_text_manual.strip()):
+        st.warning("Please upload both a ZIP file and a JD file/text.")
+        st.stop()
+
+    jd_text = ""
+    if jd_file:
+        jd_text = extract_text(jd_file.read()) if jd_file.name.endswith(".pdf") else jd_file.read().decode("utf-8")
+    else:
+        jd_text = jd_text_manual.strip()
+
+    st.subheader("📌 Extracted JD Information")
+    jd_response = llm.invoke(jd_prompt(jd_text))
+    st.markdown(jd_response.content)
+
+    resume_summaries = ""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        with zipfile.ZipFile(zip_file, "r") as archive:
+            pdfs = [f for f in archive.namelist() if f.endswith(".pdf")]
+            if not pdfs:
+                st.error("No PDFs found in the ZIP file.")
+                st.stop()
+            st.success(f"✅ Found {len(pdfs)} resumes. Extracting details...")
+
+            for file in pdfs:
+                with archive.open(file) as pdf:
+                    text = extract_text(pdf.read())
+                    summary = llm.invoke(resume_prompt(text)).content
+                    resume_summaries += f"\n\n[File: {file}]\n{summary}"
+
+    with st.spinner("🔎 Matching candidates to job description..."):
+        final_prompt = match_prompt(jd_text, resume_summaries, top_n)
+        match_result = llm.invoke(final_prompt)
+
+    st.subheader("✅ Top Matched Candidates")
+    st.markdown(match_result.content)