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import pandas as pd
from api_logic import fetch_keyword_ideas, rank_and_filter_keywords, KeywordRequestModel
# -----------------------------
# Display functions
# -----------------------------
def display_keyword_results(data: dict):
"""
Display keyword ideas returned from fetch_keyword_ideas()
Parameters
----------
data : dict
{
"results": [...],
"aggregateMetricResults": {...}
}
"""
# When no results are found
if not data or not data.get("results"):
st.warning("No keyword ideas found.")
return
results = data["results"]
st.subheader("📌 Keyword Ideas Overview")
# ------- 1 Display main metrics as a table -------
df_main = pd.DataFrame([
{
"Keyword": r["keyword"],
"Avg Monthly Searches": r["avg_monthly_searches"],
"Competition": r["competition"],
"Competition Index": r["competition_index"],
"Low Bid (micros)": r["low_top_of_page_bid_micros"],
"High Bid (micros)": r["high_top_of_page_bid_micros"],
#"Avg CPC (micros)": r["average_cpc_micros"],
## Added Features
"Log CPC": r["log_cpc"],
"Normalized CPC": r["log_cpc_norm_0_100"],
}
for r in results
])
st.dataframe(df_main, use_container_width=True)
# ------- 2 Monthly search volumes (inside expanders) -------
st.subheader("📅 Monthly Search Volumes")
for r in results:
with st.expander(f"{r['keyword']} - Monthly Trends"):
mv = r.get("monthly_search_volumes", [])
if mv:
df_mv = pd.DataFrame(mv)
df_mv = df_mv.rename(columns={
"month": "Month",
"year": "Year",
"monthly_searches": "Monthly Searches"
})
st.dataframe(df_mv, use_container_width=True)
else:
st.write("No monthly search volume data available.")
# ------- 3 Aggregate metrics (device-based searches) -------
st.subheader("📱 Aggregate Metric Results")
agg = data.get("aggregateMetricResults", {})
device_searches = agg.get("device_searches", [])
if device_searches:
df_device = pd.DataFrame(device_searches)
st.dataframe(df_device, use_container_width=True)
else:
st.write("No aggregate device metrics.")
# -----------------------------
# Streamlit UI
# -----------------------------
def main():
st.title("🔍 Google Ads Keyword Ideas ToolV007")
with st.expander("🛠️ Keyword Planner Demo – Development Progress (click to view)"):
st.markdown("""
1. Obtain Google Ads API authentication token
2. Implement `fetch_keyword_ideas` in `api_logic.py`
This function communicates with the Google Ads API and retrieves keyword ideas.
3. Implement methodology to sort data **⚠️(currently in progress)⚠️** requires 1 day more.
- Methodology is taken from **["Rule Book For GKP With Code"]**
3.1 Volume Search Section
3.2 Competition Score Section
3.3 CPC Score Section **(Completed)**
3.4 Intent Section
3.5 Trend Section
4. Implement `app.py`
Integrate the results into the Streamlit UI and deploy
**Hugging Face Link will be shared soon **
""")
st.markdown("Enter keyword(s) and your Google Ads settings to fetch keyword ideas.")
# Input UI
customer_id = st.text_input("Customer ID", value="")
keywords = st.text_input("Keywords (comma separated)", value="")
geo_target = st.text_input("Geo Target ID (optional)", value="2840")
language = st.text_input("Language ID (optional)", value="1000")
st.write("---")
if st.button("🚀 Get Keyword Ideas"):
if not customer_id or not keywords:
st.error("Please enter Customer ID and keywords.")
return
keyword_list = [k.strip() for k in keywords.split(",")]
# Build KeywordRequestModel
request_model = KeywordRequestModel(
customer_id=customer_id,
keywords=keyword_list,
geo_target=geo_target if geo_target else None,
language=language if language else None,
)
with st.spinner("Fetching keyword ideas..."):
response = fetch_keyword_ideas(request_model)
# Filter top 20 items
top20 = rank_and_filter_keywords(response, limit=20)
# Build display-friendly response structure
filtered_response = {
"results": top20,
"aggregateMetricResults": response.get("aggregateMetricResults")
}
st.success("✔ Completed")
display_keyword_results(filtered_response)
# Run application
if __name__ == "__main__":
main()
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