Update app.py

app.py

@@ -24,9 +24,8 @@ def remove_duplicates(df: pd.DataFrame) -> pd.DataFrame:
 
 
 def compute_keyword_score(text: str, keywords: List[str]) -> int:
-    """Simple keyword ranking: count of keyword hits (case-insensitive)."""
     text_l = (text or "").lower()
-    return sum(text_l.count(k.lower()) for k in keywords if k.strip())
+    return sum(text_l.count(k.lower()) for k in keywords if k)
 
 
 # ======================================================
@@ -100,7 +99,7 @@ def extract_search_parameters(client, prompt: str) -> Dict[str, str]:
 
 
 # ======================================================
-# Job scraping (Per-board toggles)
+# Job scraping
 # ======================================================
 
 @st.cache_data(ttl=3600)
@@ -125,11 +124,6 @@ def get_indeed_jobs(
         return pd.DataFrame()
 
 
-def get_other_board_stub(board_name: str) -> pd.DataFrame:
-    """Stub for future boards (toggle-safe)."""
-    return pd.DataFrame()
-
-
 # ======================================================
 # Streamlit App
 # ======================================================
@@ -138,102 +132,142 @@ def main():
     st.set_page_config(page_title="Private Job Search", layout="centered")
     st.title("📄 Private Job Search, Rank & Download")
 
-    # --- Inputs ---
+    # --------------------------------------------------
+    # Job description
+    # --------------------------------------------------
     job_prompt = st.text_area(
         "Describe the job you are looking for",
-        placeholder="e.g. Civil Engineer, Water Resources, Transportation in Alberta",
+        placeholder="e.g. Civil Engineer, Water Resources, Transportation",
         height=120
     )
 
     api_key = st.text_input("Groq API Key", type="password")
 
-    # --- Per-job-board toggles ---
+    # --------------------------------------------------
+    # City selection
+    # --------------------------------------------------
+    st.subheader("Location")
+
+    predefined_cities = [
+        "Use AI / Prompt Location",
+        "Calgary, AB",
+        "Edmonton, AB",
+        "Toronto, ON",
+        "Vancouver, BC",
+        "Mississauga, ON",
+        "Brampton, ON",
+        "Ottawa, ON",
+        "Hamilton, ON",
+        "Custom city..."
+    ]
+
+    selected_city = st.selectbox("Select city", predefined_cities)
+
+    custom_city = ""
+    if selected_city == "Custom city...":
+        custom_city = st.text_input(
+            "Enter city (e.g., Red Deer, AB or Surrey, BC)"
+        )
+
+    # --------------------------------------------------
+    # Job boards
+    # --------------------------------------------------
     st.subheader("Job Boards")
-    colb1, colb2, colb3 = st.columns(3)
-    with colb1:
-        use_indeed = st.checkbox("Indeed", value=True)
-    with colb2:
-        use_glassdoor = st.checkbox("Glassdoor (coming soon)", value=False, disabled=True)
-    with colb3:
-        use_linkedin = st.checkbox("LinkedIn (coming soon)", value=False, disabled=True)
-
-    # --- Filters ---
+    use_indeed = st.checkbox("Indeed", value=True)
+
+    # --------------------------------------------------
+    # Filters
+    # --------------------------------------------------
     st.subheader("Filters")
+
     posted_within_days = st.slider(
         "Posted within last (days)",
-        min_value=1, max_value=30, value=7
+        min_value=1,
+        max_value=30,
+        value=7
     )
 
     radius_km = st.slider(
         "Search radius (km)",
-        min_value=5, max_value=100, value=25, step=5
+        min_value=5,
+        max_value=100,
+        value=25,
+        step=5
     )
 
-    # --- Keyword ranking ---
+    # --------------------------------------------------
+    # Keyword ranking
+    # --------------------------------------------------
     keywords_raw = st.text_input(
         "Keyword ranking (comma-separated)",
-        placeholder="water, wastewater, stormwater, EPANET, WNTR"
+        placeholder="water, wastewater, stormwater, EPANET"
     )
     keywords = [k.strip() for k in keywords_raw.split(",") if k.strip()]
 
-    # --- Optional email ---
+    # --------------------------------------------------
+    # Optional email
+    # --------------------------------------------------
     send_email = st.checkbox("📧 Send results by email (optional)")
     email_address = st.text_input("Email address") if send_email else None
 
-    # --- Action ---
-    if st.button("🔍 Search Jobs", disabled=not job_prompt or not api_key):
+    # --------------------------------------------------
+    # Action
+    # --------------------------------------------------
+    if st.button(
+        "🔍 Search Jobs",
+        disabled=not job_prompt or not api_key
+    ):
         client = groq.Client(api_key=api_key)
 
         with st.spinner("Understanding your request..."):
             params = extract_search_parameters(client, job_prompt)
 
-        all_jobs = []
-
-        with st.spinner("Searching job boards..."):
-            if use_indeed:
-                indeed_df = get_indeed_jobs(
-                    params["search_term"],
-                    params["location"],
-                    radius_km,
-                    posted_within_days
-                )
-                if not indeed_df.empty:
-                    indeed_df["source"] = "Indeed"
-                    all_jobs.append(indeed_df)
-
-            # Future boards (toggle-safe)
-            if use_glassdoor:
-                all_jobs.append(get_other_board_stub("Glassdoor"))
-            if use_linkedin:
-                all_jobs.append(get_other_board_stub("LinkedIn"))
-
-        if not all_jobs:
-            st.warning("No jobs found.")
+        # Resolve final location
+        if selected_city == "Use AI / Prompt Location":
+            location = params.get("location", "Canada")
+        elif selected_city == "Custom city...":
+            location = custom_city if custom_city else params.get("location", "Canada")
+        else:
+            location = selected_city
+
+        if not use_indeed:
+            st.warning("No job boards selected.")
             return
 
-        jobs_df = pd.concat(all_jobs, ignore_index=True)
-        jobs_df.fillna("", inplace=True)
-        jobs_df = remove_duplicates(jobs_df)
-
-        # --- Keyword ranking ---
-        if keywords:
-            jobs_df["keyword_score"] = jobs_df.apply(
-                lambda r: compute_keyword_score(
-                    f"{r.get('title','')} {r.get('description','')}",
-                    keywords
-                ),
-                axis=1
+        with st.spinner("Searching jobs..."):
+            jobs_df = get_indeed_jobs(
+                params["search_term"],
+                location,
+                radius_km,
+                posted_within_days
             )
-        else:
-            jobs_df["keyword_score"] = 0
 
-        # Sort by keyword score (desc) then date if available
-        sort_cols = ["keyword_score"]
-        jobs_df = jobs_df.sort_values(sort_cols, ascending=[False])
+            if jobs_df.empty:
+                st.warning("No jobs found.")
+                return
 
-        st.success(f"✅ Found {len(jobs_df)} jobs")
+            jobs_df.fillna("", inplace=True)
+            jobs_df = remove_duplicates(jobs_df)
+
+        # Keyword ranking
+        jobs_df["keyword_score"] = jobs_df.apply(
+            lambda r: compute_keyword_score(
+                f"{r.get('title','')} {r.get('description','')}",
+                keywords
+            ),
+            axis=1
+        )
 
-        # --- Download always available ---
+        jobs_df = jobs_df.sort_values(
+            by="keyword_score",
+            ascending=False
+        )
+
+        st.success(f"✅ Found {len(jobs_df)} jobs for **{location}**")
+
+        # --------------------------------------------------
+        # Download
+        # --------------------------------------------------
         csv_data = jobs_df.to_csv(index=False).encode("utf-8")
         st.download_button(
             label="⬇️ Download Jobs (CSV)",
@@ -242,7 +276,9 @@ def main():
             mime="text/csv"
         )
 
-        # --- Optional email ---
+        # --------------------------------------------------
+        # Optional email
+        # --------------------------------------------------
         if send_email:
             if not email_address:
                 st.warning("Please enter an email address.")
@@ -256,15 +292,20 @@ def main():
                     except Exception as e:
                         st.error(f"Failed to send email: {e}")
 
-        # --- Preview ---
-        st.subheader("Preview (Top Results)")
+        # --------------------------------------------------
+        # Preview
+        # --------------------------------------------------
+        st.subheader("Preview (Top 20 Results)")
         preview_cols = [
             c for c in [
-                "source", "title", "company", "location",
+                "title", "company", "location",
                 "keyword_score", "date_posted", "job_url"
             ] if c in jobs_df.columns
         ]
-        st.dataframe(jobs_df[preview_cols].head(20), use_container_width=True)
+        st.dataframe(
+            jobs_df[preview_cols].head(20),
+            use_container_width=True
+        )
 
 
 if __name__ == "__main__":