Create app.py

app.py

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+import streamlit as st
+from huggingface_hub import InferenceClient
+from dotenv import load_dotenv
+import os
+import PyPDF2 as pdf
+
+# Load .env file
+load_dotenv()
+api_key = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+# Hugging Face model
+MODEL = "nvidia/Llama-3_1-Nemotron-Ultra-253B-v1"
+
+# Set up page
+st.set_page_config(page_title="JD Matcher by Jishnu Setia", page_icon="📄")
+st.title("📌 Job Description Matcher")
+st.text("Find out if your resume matches the job you're targeting!")
+
+# Input fields
+jd = st.text_area("📝 Paste the Job Description here:")
+uploaded_file = st.file_uploader("📎 Upload Your Resume (PDF only):", type="pdf")
+
+submit = st.button("🚀 Submit")
+
+# Function to read PDF content
+def input_pdf_text(uploaded_file):
+    reader = pdf.PdfReader(uploaded_file)
+    text = ""
+    for page in reader.pages:
+        text += page.extract_text()
+    return text
+
+# Prompt template
+system_prompt = {
+    "role": "system",
+    "content": (
+        "You are a highly experienced ATS (Applicant Tracking System). Evaluate the resume based on the given job description. "
+        "Be strict, accurate, and helpful. Job market is very competitive. Return your response in this format:\n\n"
+        "1. JD Match Percentage: \"%\"\n"
+        "2. Matching Feedback: (e.g., 'Great match!' or 'Needs improvement')\n"
+        "3. Missing Keywords: [list]\n"
+        "4. Tips to Improve the Resume:"
+    )
+}
+
+# When submit is clicked
+if submit:
+    if uploaded_file and jd:
+        with st.spinner("Analyzing your resume..."):
+            resume_text = input_pdf_text(uploaded_file)
+
+            # Prepare context
+            context = [
+                system_prompt,
+                {"role": "user", "content": f"Resume:\n{resume_text}\n\nJob Description:\n{jd}"}
+            ]
+
+            try:
+                client = InferenceClient(
+                    model=MODEL,
+                    provider="nebius",
+                    api_key=api_key
+                )
+
+                completion = client.chat.completions.create(
+                    model=MODEL,
+                    messages=context,
+                    max_tokens=2048,
+                )
+
+                response = completion.choices[0].message.content
+                st.subheader("📊 ATS Evaluation Result")
+                st.markdown(response)
+
+            except Exception as e:
+                st.error(f"❌ Error: {e}")
+    else:
+        st.warning("Please upload a resume and paste a job description!")