Create app.py

app.py

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+import streamlit as st
+from streamlit_javascript import st_javascript
+import requests
+import pandas as pd
+import os
+
+# Load API key from environment variable
+API_KEY = os.getenv("GOOGLE_PLACES_API_KEY")
+
+st.title("📍 AI-Powered Restaurant & Museum Finder")
+
+# Fetch user's location
+location = st_javascript("navigator.geolocation.getCurrentPosition(position => position.coords);")
+
+if location:
+    lat, lon = location["latitude"], location["longitude"]
+    st.success(f"Detected Location: {lat}, {lon}")
+
+    def fetch_places(lat, lon, place_type="restaurant"):
+        radius = 3000  # 3km range
+        url = f"https://maps.googleapis.com/maps/api/place/nearbysearch/json?location={lat},{lon}&radius={radius}&type={place_type}&key={API_KEY}"
+        response = requests.get(url)
+        return response.json().get("results", [])
+
+    # Select category
+    place_type = st.selectbox("Choose a Category", ["restaurant", "museum"])
+
+    places = fetch_places(lat, lon, place_type)
+
+    if places:
+        def rank_places(places):
+            return sorted(places, key=lambda p: (p.get("rating", 0) * 0.7) + (p.get("user_ratings_total", 0) * 0.3), reverse=True)
+
+        ranked_places = rank_places(places)
+        df = pd.DataFrame([
+            {"Name": p["name"], "Rating": p.get("rating", "N/A"), "Reviews": p.get("user_ratings_total", 0), "Address": p.get("vicinity", "Unknown")}
+            for p in ranked_places[:10]
+        ])
+        st.table(df)
+
+        # Map Visualization
+        st.map(pd.DataFrame({"lat": [p["geometry"]["location"]["lat"] for p in ranked_places[:10]],
+                             "lon": [p["geometry"]["location"]["lng"] for p in ranked_places[:10]]}))
+    else:
+        st.warning(f"No {place_type}s found nearby.")
+else:
+    st.error("Unable to detect location. Please enable location services.")