Update app.py

app.py

@@ -1,88 +1,129 @@
 import streamlit as st
 import requests
+import json
 import os
-from dotenv import load_dotenv
 
-load_dotenv()
+# CONFIG
+# For Hugging Face Spaces (Same container)
+BACKEND_URL = "http://127.0.0.1:8000/analyze"
 
-# Configuration
-# Defaults to localhost for dev, but can be overridden in production (e.g., Docker)
-BACKEND_URL = os.getenv("BACKEND_URL", "http://127.0.0.1:8000/process-resume")
+st.set_page_config(page_title="Smart ATS", page_icon="🎯", layout="wide")
 
-st.set_page_config(page_title="AI Resume Analyzer", page_icon="📄", layout="centered")
+# CUSTOM CSS
+st.markdown("""
+<style>
+    .metric-card {
+        background-color: #f0f2f6;
+        padding: 20px;
+        border-radius: 10px;
+        border-left: 5px solid #4CAF50;
+    }
+    .skill-tag {
+        display: inline-block;
+        padding: 5px 10px;
+        margin: 2px;
+        border-radius: 12px;
+        font-size: 12px;
+        font-weight: bold;
+    }
+    .match { background-color: #d4edda; color: #155724; border: 1px solid #c3e6cb; }
+    .missing { background-color: #f8d7da; color: #721c24; border: 1px solid #f5c6cb; }
+</style>
+""", unsafe_allow_html=True)
 
-st.title("📄 Intelligent Resume Parser")
-st.markdown("---")
-st.write("Upload a professional resume in PDF format to extract key insights using AI.")
+st.title("🎯 AI Smart Resume Screen")
+st.markdown("Compare resumes against job descriptions to find the perfect fit.")
 
-# Sidebar for status
-with st.sidebar:
-    st.info(f"Connected to Backend: `{BACKEND_URL}`")
+# LAYOUT: Two columns for input
+col1, col2 = st.columns([1, 1])
 
-uploaded_file = st.file_uploader("Upload PDF Resume", type="pdf")
+with col1:
+    st.subheader("1. Job Description (Optional)")
+    jd_input = st.text_area("Paste the Job Description here...", height=200, 
+                           placeholder="e.g. Seeking a Senior Python Developer with AWS experience...")
 
-if uploaded_file:
-    # Basic Frontend Validation
-    if uploaded_file.size > 5 * 1024 * 1024:
-        st.error("File is too large! Please upload a file smaller than 5MB.")
+with col2:
+    st.subheader("2. Candidate Resume")
+    uploaded_file = st.file_uploader("Upload PDF", type="pdf")
+
+# ACTION
+if st.button("Analyze Fit", type="primary"):
+    if not uploaded_file:
+        st.warning("Please upload a resume first.")
     else:
-        if st.button("Analyze Resume", type="primary"):
-            with st.spinner("Processing with AI..."):
-                try:
-                    files = {
-                        "file": (uploaded_file.name, uploaded_file.getvalue(), "application/pdf")
-                    }
+        with st.spinner("Analyzing candidate against requirements..."):
+            try:
+                # Prepare Payload
+                files = {"file": (uploaded_file.name, uploaded_file.getvalue(), "application/pdf")}
+                data = {"job_description": jd_input} if jd_input else {}
+                
+                response = requests.post(BACKEND_URL, files=files, data=data, timeout=60)
+                
+                if response.status_code == 200:
+                    result = response.json()
                     
-                    # Set a timeout to prevent hanging
-                    response = requests.post(BACKEND_URL, files=files, timeout=30)
-
-                    if response.status_code == 200:
-                        data = response.json()
+                    if "error" in result:
+                        st.error(result["error"])
+                    else:
+                        # --- DASHBOARD UI ---
+                        candidate = result.get("candidate", {})
+                        analysis = result.get("match_analysis", {})
                         
-                        # Handle case where AI returns an error key
-                        if "error" in data:
-                            st.error(data["error"])
-                        else:
-                            st.success("Extraction Complete!")
+                        # 1. Header (Candidate Info)
+                        st.divider()
+                        c1, c2, c3, c4 = st.columns(4)
+                        c1.markdown(f"**Name:** {candidate.get('name', 'N/A')}")
+                        c2.markdown(f"**Email:** {candidate.get('email', 'N/A')}")
+                        c3.markdown(f"**Phone:** {candidate.get('phone', 'N/A')}")
+                        
+                        # 2. Score Section (Only if JD provided)
+                        if jd_input:
+                            score = analysis.get("score", 0)
+                            verdict = analysis.get("verdict", "N/A")
                             
-                            # Summary Section
-                            st.markdown("### 📝 Professional Summary")
-                            st.info(data.get('summary', 'No summary available.'))
-
-                            # Contact Info
-                            st.markdown("### 📇 Contact Details")
-                            c1, c2, c3 = st.columns(3)
-                            c1.metric("Name", data.get('name', 'N/A'))
-                            c2.metric("Email", data.get('email', 'N/A'))
-                            c3.metric("Phone", data.get('phone', 'N/A'))
+                            # Color coding the score
+                            score_color = "green" if score >= 80 else "orange" if score >= 50 else "red"
+                            
+                            st.markdown(f"""
+                            <div class="metric-card">
+                                <h3 style="margin:0">Match Score: <span style="color:{score_color}">{score}%</span></h3>
+                                <p style="margin:0"><strong>Verdict:</strong> {verdict}</p>
+                                <p style="margin-top:10px"><em>"{analysis.get('reasoning', '')}"</em></p>
+                            </div>
+                            """, unsafe_allow_html=True)
+                            
+                            st.markdown("### 🧩 Skill Gap Analysis")
+                            k1, k2 = st.columns(2)
+                            
+                            with k1:
+                                st.success("✅ Matching Skills")
+                                matches = analysis.get("matching_skills", [])
+                                if matches:
+                                    html = "".join([f'<span class="skill-tag match">{s}</span>' for s in matches])
+                                    st.markdown(html, unsafe_allow_html=True)
+                                else:
+                                    st.write("No direct skill matches found.")
 
-                            # Skills Section
-                            st.markdown("### 🛠 Technical Skills")
-                            skills = data.get('skills', [])
-                            if skills and isinstance(skills, list):
-                                # CSS styling for tags
-                                st.markdown(
-                                    f"""
-                                    <div style="display: flex; flex-wrap: wrap; gap: 10px;">
-                                        {''.join([f'<span style="background-color: #e0f2f1; color: #00695c; padding: 5px 10px; border-radius: 15px; font-size: 14px;">{skill}</span>' for skill in skills])}
-                                    </div>
-                                    """,
-                                    unsafe_allow_html=True
-                                )
-                            else:
-                                st.write("No specific skills detected.")
+                            with k2:
+                                st.error("⚠️ Missing / To Improve")
+                                missing = analysis.get("missing_skills", [])
+                                if missing:
+                                    html = "".join([f'<span class="skill-tag missing">{s}</span>' for s in missing])
+                                    st.markdown(html, unsafe_allow_html=True)
+                                else:
+                                    st.write("No major skills missing!")
+                                    
+                        else:
+                            # Standard Extraction View (No JD)
+                            st.info("💡 Paste a Job Description on the left to see a Match Score!")
+                            st.markdown("### 🛠 Skills Detected")
+                            st.write(", ".join(candidate.get("skills", [])))
 
-                            with st.expander("View Raw JSON Data"):
-                                st.json(data)
-                                
-                    elif response.status_code == 413:
-                        st.error("The file is too large for the server to process.")
-                    else:
-                        st.error(f"Server Error: {response.status_code} - {response.text}")
+                        with st.expander("View Raw JSON Data"):
+                            st.json(result)
 
-                except requests.exceptions.ConnectionError:
-                    st.error("🚨 Connection Failed: Could not reach the backend server.")
-                except requests.exceptions.Timeout:
-                    st.error("🚨 Request Timed Out: The AI took too long to respond.")
-                except Exception as e:
-                    st.error(f"An unexpected error occurred: {e}")
+                else:
+                    st.error(f"Server Error: {response.text}")
+                    
+            except Exception as e:
+                st.error(f"Connection Error: {e}")