app.py

app.py

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+import streamlit as st
+import geopandas as gpd
+import shapely
+from shapely.geometry import Polygon
+from io import BytesIO
+from transformers import pipeline, RagTokenizer, RagRetriever, RagSequenceForGeneration
+import os
+import zipfile
+import json
+import xml.etree.ElementTree as ET
+from shapely.ops import transform
+from pyproj import Proj, transform as proj_transform
+
+# Load pre-trained Hugging Face RAG model
+def load_rag_model():
+    tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
+    retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq")
+    model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq")
+    return tokenizer, retriever, model
+
+tokenizer, retriever, model = load_rag_model()
+
+# Function to load shapefile (SHP, DBF, etc.)
+def load_shapefile(uploaded_file):
+    if uploaded_file is not None:
+        if uploaded_file.name.endswith('.zip'):
+            with zipfile.ZipFile(uploaded_file, 'r') as zip_ref:
+                zip_ref.extractall("extracted_files")
+            shp_file = [f for f in os.listdir("extracted_files") if f.endswith(".shp")][0]
+            shapefile_path = os.path.join("extracted_files", shp_file)
+        else:
+            shapefile_path = uploaded_file
+        return gpd.read_file(shapefile_path)
+    return None
+
+# Function to get land summary based on a KML or KMZ file
+def get_land_summary_by_kml(kml_file):
+    tree = ET.parse(kml_file)
+    root = tree.getroot()
+    ns = {'kml': 'http://www.opengis.net/kml/2.2'}
+    coordinates = []
+    for coord in root.findall('.//kml:coordinates', ns):
+        coords = coord.text.strip().split()
+        coordinates.extend([(float(x.split(',')[0]), float(x.split(',')[1])) for x in coords])
+    
+    # Create a polygon from coordinates
+    poly = Polygon(coordinates)
+    return poly.area
+
+# Function to summarize floodland areas, acreage, and usable land
+def summarize_land_data(shapefile_data):
+    # Example: Getting floodland areas, acreage, and usable land from shapefile data
+    total_area = shapefile_data['geometry'].area.sum()  # Sum of all area
+    usable_land = shapefile_data[shapefile_data['use_type'] == 'Usable Land']  # Filter usable land
+    usable_land_area = usable_land['geometry'].area.sum()  # Area of usable land
+    return total_area, usable_land_area
+
+# Function to generate a response using RAG model
+def generate_rag_response(query):
+    inputs = tokenizer(query, return_tensors="pt")
+    retriever_outputs = retriever(inputs['input_ids'], return_tensors="pt")
+    generated_ids = model.generate(input_ids=inputs['input_ids'], 
+                                   context_input_ids=retriever_outputs['context_input_ids'], 
+                                   context_attention_mask=retriever_outputs['context_attention_mask'])
+    response = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+    return response
+
+# Streamlit app interface
+def main():
+    st.title("Geospatial Data Summary and Chatbot")
+
+    # Buttons for interaction
+    summary_button = st.button("Get Land Summary")
+    chatbot_button = st.button("Chat with the Bot")
+
+    # Upload file option
+    uploaded_file = st.file_uploader("Upload SHP/DBF/ZIP file", type=["zip", "shp", "dbf"])
+    kml_file = st.file_uploader("Upload KML or KMZ boundary file", type=["kml", "kmz"])
+
+    if summary_button:
+        if uploaded_file is not None:
+            # Load the shapefile
+            shapefile_data = load_shapefile(uploaded_file)
+            if shapefile_data is not None:
+                total_area, usable_land_area = summarize_land_data(shapefile_data)
+                st.write(f"Total floodland area: {total_area:.2f} sq meters")
+                st.write(f"Usable land area: {usable_land_area:.2f} sq meters")
+        elif kml_file is not None:
+            # Process KML for land summary
+            land_area = get_land_summary_by_kml(kml_file)
+            st.write(f"Floodland area in KML boundary: {land_area:.2f} sq meters")
+        else:
+            st.write("Please upload either a shapefile or KML/KMZ file.")
+
+    elif chatbot_button:
+        if uploaded_file is not None or kml_file is not None:
+            query = st.text_input("Ask the bot a question about the data:")
+            if query:
+                answer = generate_rag_response(query)
+                st.write(answer)
+        else:
+            st.write("Please upload a file before interacting with the chatbot.")
+
+if __name__ == "__main__":
+    main()