Create app.py

app.py

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+import os
+import streamlit as st
+from langchain_community.document_loaders import UnstructuredPDFLoader
+from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
+
+# Set HuggingFace API keys
+hf_token = os.getenv("HF_Token")
+os.environ["HUGGINGFACEHUB_API_TOKEN"] =hf_token
+os.environ["HF_TOKEN"]=hf_token
+ 
+llm_skeleton = HuggingFaceEndpoint(
+    repo_id="meta-llama/Llama-3.1-8B-Instruct",
+    provider="novita",
+    temperature=0.7,
+    max_new_tokens=150,
+    task="conversational"
+)
+
+llm = ChatHuggingFace(
+    llm=llm_skeleton,
+    repo_id="meta-llama/Llama-3.2-3B-Instruct",
+    provider="novita",
+    temperature=0.7,
+    max_new_tokens=150,
+    task="conversational"
+)
+
+# Helper function to extract text from uploaded file
+def extract_text(file):
+    try:
+        loader = UnstructuredPDFLoader(file)
+        return loader.load()[0].page_content
+    except Exception as e:
+        st.error(f"Error reading file: {e}")
+        return ""
+
+# Streamlit UI
+st.set_page_config(page_title="Resume & JD Extractor", layout="centered")
+st.title("📄 Resume & Job Description Extractor")
+
+# Upload inputs
+resume_file = st.file_uploader("Upload Resume (PDF)", type=["pdf"])
+jd_file = st.file_uploader("Upload Job Description (PDF or TXT)", type=["pdf", "txt"])
+jd_text = st.text_area("Or paste Job Description text here")
+
+# Extract button
+if st.button("🔍 Extract Data"):
+    if not resume_file and not (jd_file or jd_text):
+        st.warning("Please upload at least one file (Resume or JD) or paste JD text.")
+    
+    # Extract Resume
+    if resume_file:
+        resume_text = extract_text(resume_file)
+        resume_prompt = (
+            "Extract the following from the resume:\n"
+            "1. Name\n2. Education\n3. Experience\n4. Skills\n5. Project Names and Results\n\n"
+            f"Resume:\n{resume_text}"
+        )
+        resume_data = llm.invoke(resume_prompt)
+        st.subheader("📌 Extracted Resume Data")
+        st.markdown(f"<div style='background-color:#f9f9f9;padding:10px;border-radius:8px;'>{resume_data}</div>", unsafe_allow_html=True)
+
+    # Extract JD
+    if jd_file or jd_text:
+        jd_text_extracted = extract_text(jd_file) if jd_file else jd_text
+        jd_prompt = (
+            "Extract the following from the job description:\n"
+            "1. Job ID\n2. Company Name\n3. Role\n4. Experience Required\n5. Skills Required\n"
+            "6. Education Required\n7. Location\n\n"
+            f"Job Description:\n{jd_text_extracted}"
+        )
+        jd_data = llm.invoke(jd_prompt)
+        st.subheader("📌 Extracted Job Description Data")
+        st.markdown(f"<div style='background-color:#f9f9f9;padding:10px;border-radius:8px;'>{jd_data}</div>", unsafe_allow_html=True)