Update pages/model.py

pages/model.py

@@ -0,0 +1,92 @@
+import streamlit as st
+import os
+from langchain_community.chat_models import ChatHuggingFace
+from langchain_huggingface import HuggingFaceEndpoint
+from langchain_community.document_loaders import UnstructuredPDFLoader
+
+
+os.environ["HF_TOKEN"]=os.getenv('HF_Token')
+os.environ["HUGGINGFACEHUB_API_KEY"]=os.getenv('HF_Token')
+
+llm_config = HuggingFaceEndpoint(
+    repo_id="meta-llama/Llama-3.1-8B-Instruct",
+    provider="nebius",
+    temperature=0.5,
+    max_new_tokens=300,
+    task="conversational"
+)
+llm = ChatHuggingFace(
+    llm=llm_config,
+    repo_id="meta-llama/Llama-3.1-8B-Instruct",
+    provider="nebius"
+)
+
+
+st.set_page_config(page_title="Smart Resume-JD Extractor", layout="centered")
+st.markdown("""
+    <div style="text-align:center; padding: 30px; background-color: #f3f4f6; border-radius: 12px;">
+        <h1 style="color:#2c3e50;">📄 PrescriptoHire: Resume & JD Extractor</h1>
+        <p style="color:#555; font-size: 16px;">AI-powered tool to extract & compare details from resumes and job descriptions</p>
+    </div>
+""", unsafe_allow_html=True)
+
+
+resume_file = st.file_uploader("Upload Candidate Resume (PDF)", type=["pdf"])
+jd_file = st.file_uploader("Upload Job Description (PDF)", type=["pdf"])
+
+
+st.markdown("<p style='text-align:center; margin:10px 0;'>-- OR --</p>", unsafe_allow_html=True)
+jd_text_input = st.text_area("Paste Job Description Text")
+
+
+if st.button("🔍 Extract & Compare"):
+    if not resume_file:
+        st.error("Please upload a resume PDF to proceed.")
+    else:
+        # Extract Resume Text
+        resume_text = UnstructuredPDFLoader(resume_file).load()[0].page_content
+
+        resume_prompt = f"""
+        Extract the following from the resume:
+        1. Name
+        2. Contact Info
+        3. Education
+        4. Experience
+        5. Skills
+        6. Projects and Achievements
+
+        Resume Text:
+        {resume_text}
+        """
+        with st.spinner("Extracting from Resume..."):
+            resume_output = llm.invoke(resume_prompt)
+        st.success("Resume data extracted!")
+        st.subheader("🧾 Resume Summary")
+        st.write(resume_output)
+
+    # Process JD (file or text)
+    if jd_file or jd_text_input:
+        if jd_file:
+            jd_text = UnstructuredPDFLoader(jd_file).load()[0].page_content
+        else:
+            jd_text = jd_text_input
+
+        jd_prompt = f"""
+        Extract the following from the job description:
+        1. Job Title
+        2. Company Name
+        3. Experience Required
+        4. Key Responsibilities
+        5. Skills & Qualifications
+        6. Location
+
+        Job Description:
+        {jd_text}
+        """
+        with st.spinner("Extracting from Job Description..."):
+            jd_output = llm.invoke(jd_prompt)
+        st.success("Job description data extracted!")
+        st.subheader("📌 Job Description Summary")
+        st.write(jd_output)
+    else:
+        st.warning("Please upload or paste a job description.")