Create Src/app.py

Src/app.py

@@ -0,0 +1,56 @@
+import streamlit as st
+from pdf_parser import parse_resume
+
+st.set_page_config(page_title="Resume Parser", layout="centered")
+st.title("Resume Parser")
+
+uploaded_file = st.file_uploader("Upload your resume (PDF)", type=["pdf"])
+if uploaded_file:
+    with open("temp_resume.pdf", "wb") as f:
+        f.write(uploaded_file.getbuffer())
+    
+    try:
+        result = parse_resume("temp_resume.pdf")
+    except Exception as e:
+        st.error(f"Error parsing resume: {str(e)}")
+        st.stop()
+
+    # Prepare structured output
+    structured_output = {
+        "name": result.get("name", {}).get("value"),
+        "email": result.get("email", {}).get("value"),
+        "phone": result.get("phone", {}).get("value"),
+        "linkedin": result.get("linkedin", {}).get("value"),
+        "skills": result.get("skills", {}).get("value") or [],
+        "education": result.get("education", {}).get("value"),
+        "experience": result.get("experience", {}).get("value"),
+        "projects": result.get("projects", {}).get("value"),
+        "certifications": result.get("certifications", {}).get("value"),
+    }
+
+    # Display results
+    st.subheader("Parsed Resume Data")
+    st.json(structured_output)
+
+    st.subheader("Detailed Analysis")
+    fields = [
+        ("Personal Info", ["name", "email", "phone", "linkedin"]),
+        ("Skills", ["skills"]),
+        ("Professional History", ["experience", "projects"]),
+        ("Education & Certifications", ["education", "certifications"])
+    ]
+
+    for category, field_keys in fields:
+        st.markdown(f"### {category}")
+        cols = st.columns(2)
+        for i, field in enumerate(field_keys):
+            data = result.get(field, {})
+            value = data.get("value", "NIL")
+            conf = data.get("confidence", 0)
+            
+            if value == "NIL" or conf == 0:
+                cols[i%2].error(f"{field.title()}: Not detected")
+            elif conf < 0.7:
+                cols[i%2].warning(f"{field.title()}: {value} (Confidence: {conf:.0%})")
+            else:
+                cols[i%2].success(f"{field.title()}: {value} (Confidence: {conf:.0%})")