Create app.py

app.py

@@ -0,0 +1,54 @@
+import streamlit as st
+from langchain_core.prompts import PromptTemplate
+from langchain_community.chat_models import ChatHuggingFace
+
+# Setup LLM
+llm_model = ChatHuggingFace(
+    repo_id="HuggingFaceH4/zephyr-7b-beta",
+    task="conversational",
+    temperature=0.7,
+    max_new_tokens=300,
+    model="huggingface",
+    provider="hf-inference"
+)
+
+# Prompt template
+prompt = PromptTemplate.from_template("""
+You are an expert career advisor specializing in analyzing job descriptions and providing actionable insights to help job seekers tailor their resumes and skills for maximum impact.
+
+Given the Job Description below, extract and present the following sections **using markdown formatting**:
+
+**1. Key Technical Skills**  
+List the main technical skills required using bullet points.
+
+**2. Important Soft Skills**  
+List the soft skills emphasized by the employer using bullet points.
+
+**3. Suggested Mini Projects**  
+Recommend mini projects or learning paths to strengthen the candidate’s profile, using bullet points.
+
+**4. Resume Improvement Tips**  
+Provide practical and specific tips to improve the candidate's resume, using bullet points.
+
+Use **bold headings** for each section and markdown bullet points (`- `). Write in a professional yet friendly tone. Be concise, clear, and focused on actionable advice.
+
+Job Description:  
+\"\"\"{job_description}\"\"\"
+
+""")
+
+# Chain
+chain = prompt | llm_model
+
+# Streamlit UI
+st.set_page_config(page_title="JD Analyzer AI")
+st.title("📄 JD Analyzer AI")
+st.markdown("Paste any job description below and get insights to improve your profile.")
+
+jd_input = st.text_area("Paste Job Description:", height=300)
+
+if st.button("Analyze"):
+    with st.spinner("Analyzing the JD..."):
+        output = chain.invoke({"job_description": jd_input})
+        st.markdown("### 🔍 Output")
+        st.markdown(output)