Update app.py

app.py

@@ -1,5 +1,7 @@
 import streamlit as st
 import requests
+import pdfplumber
+import docx
 from sentence_transformers import SentenceTransformer
 import faiss
 import numpy as np
@@ -24,6 +26,23 @@ groq_client = Groq(api_key=st.secrets.get("GROQ_API_KEY", None))
 # -----------------------------
 # FUNCTIONS
 # -----------------------------
+def extract_text_from_resume(file):
+    """Extract text from PDF or DOCX file"""
+    if file.name.endswith(".pdf"):
+        text = ""
+        with pdfplumber.open(file) as pdf:
+            for page in pdf.pages:
+                text += page.extract_text() or ""
+        return text
+
+    elif file.name.endswith(".docx"):
+        doc = docx.Document(file)
+        text = "\n".join([p.text for p in doc.paragraphs])
+        return text
+
+    else:
+        st.error("Unsupported file type. Please upload PDF or DOCX.")
+        return ""
 
 def fetch_jobs():
     resp = requests.get(REMOTEOK_URL)
@@ -36,16 +55,13 @@ def embed_texts(texts):
     return model.encode(texts, convert_to_numpy=True, normalize_embeddings=True)
 
 def match_jobs(resume_text, jobs, top_k=5):
-    # prepare job descriptions
     job_texts = [f"{job.get('position','')} {job.get('company','')} {job.get('description','')}" for job in jobs]
     
-    # embeddings
     resume_vec = embed_texts([resume_text])
     job_vecs = embed_texts(job_texts)
 
-    # FAISS index
     dim = job_vecs.shape[1]
-    index = faiss.IndexFlatIP(dim)  # cosine similarity (normalized)
+    index = faiss.IndexFlatIP(dim)
     index.add(job_vecs)
 
     scores, idx = index.search(resume_vec, top_k)
@@ -75,21 +91,22 @@ def generate_resume(resume_text, job):
 # -----------------------------
 st.title("MATCHHIVE - AI Job Matcher")
 
-resume_file = st.file_uploader("Upload your resume (txt/pdf/docx)", type=["txt"])
+resume_file = st.file_uploader("Upload your resume (PDF or DOCX)", type=["pdf", "docx"])
 if resume_file:
-    resume_text = resume_file.read().decode("utf-8", errors="ignore")
+    resume_text = extract_text_from_resume(resume_file)
 
-    st.subheader("Fetching jobs...")
-    jobs = fetch_jobs()
+    if resume_text.strip():
+        st.subheader("Fetching jobs...")
+        jobs = fetch_jobs()
 
-    st.subheader("Best Matches")
-    matches = match_jobs(resume_text, jobs, top_k=5)
+        st.subheader("Best Matches")
+        matches = match_jobs(resume_text, jobs, top_k=5)
 
-    for job, score in matches:
-        st.markdown(f"**{job['position']}** at *{job['company']}*  \n"
-                    f"[View Job Posting]({job['url']})  \n"
-                    f"**Match Score:** {score:.2f}")
+        for job, score in matches:
+            st.markdown(f"**{job['position']}** at *{job['company']}*  \n"
+                        f"[View Job Posting]({job['url']})  \n"
+                        f"**Match Score:** {score:.2f}")
 
-        if st.button(f"Generate Resume for {job['position']}", key=job['id']):
-            tailored_resume = generate_resume(resume_text, job)
-            st.text_area("Tailored Resume", tailored_resume, height=300)
+            if st.button(f"Generate Resume for {job['position']}", key=job['id']):
+                tailored_resume = generate_resume(resume_text, job)
+                st.text_area("Tailored Resume", tailored_resume, height=300)