neew code

app.py

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+import geopandas as gpd
+import sqlite3
+import pandas as pd
+import torch
+import faiss
+import numpy as np
+import os
+from shapely.geometry import shape
+from sentence_transformers import SentenceTransformer
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import streamlit as st
+
+# Set the environment variables for GPU usage in Hugging Face
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"  # Hugging Face uses GPU 0 by default
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+# Set device to GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+st.write(f"Using device: {device}")
+
+# Step 1: Load and Process Floodland Data
+conn = sqlite3.connect('NY.db')
+cursor = conn.cursor()
+
+# Load shapefile
+gdf = gpd.read_file('S_FLD_HAZ_AR.shp')
+
+# Validate geometries
+gdf['geometry'] = gdf['geometry'].apply(lambda geom: geom if geom.is_valid else None)
+gdf = gdf.dropna(subset=['geometry'])
+
+# Convert CRS to UTM Zone 18N (New York)
+gdf = gdf.to_crs(epsg=32618)
+
+# Calculate acreage (1 square meter = 0.000247105 acres)
+gdf['acreage'] = gdf.geometry.area * 0.000247105
+
+# Define flood-prone zones and calculate usable area
+flood_prone_zones = ['A', 'AE', 'AH', 'AO', 'VE']
+gdf['usable_area'] = gdf.apply(lambda row: row['acreage'] if row['FLD_ZONE'] not in flood_prone_zones else 0, axis=1)
+
+# Convert geometry to WKT format
+gdf['wkt_geometry'] = gdf['geometry'].apply(lambda geom: geom.wkt)
+
+# Step 2: Load Embedding Model (Sentence-Transformer)
+embedder = SentenceTransformer('all-MiniLM-L6-v2')
+
+# Convert floodland descriptions into text
+gdf['text'] = gdf.apply(
+    lambda row: f"Flood Zone: {row['FLD_ZONE']}, Subtype: {row['ZONE_SUBTY']}, Acreage: {row['acreage']:.2f} acres, Usable Area: {row['usable_area']:.2f} acres",
+    axis=1
+)
+
+# Generate text embeddings
+embeddings = embedder.encode(gdf['text'].tolist(), show_progress_bar=True)
+
+# Create FAISS index
+d = embeddings.shape[1]
+index = faiss.IndexFlatL2(d)
+index.add(embeddings)
+
+# Store embeddings in DataFrame
+gdf['embedding'] = list(embeddings)
+
+# Step 3: Load LLaMA Model for Summarization
+llama_model_name = "meta-llama/Llama-2-7b-chat-hf"
+tokenizer = AutoTokenizer.from_pretrained(llama_model_name)
+model = AutoModelForCausalLM.from_pretrained(llama_model_name, torch_dtype=torch.float16, device_map="auto")
+
+# Function to Generate Summary using LLaMA
+def llama_summarize(text, total_acreage, usable_acreage, location_data, max_length=250):
+    input_text = f"""
+    **Total Land Area**: {total_acreage:.2f} acres
+    **Usable Area**: {usable_acreage:.2f} acres
+    **Flood-prone Zones**:
+     {location_data}
+    
+    Summarization in sentence
+    """
+
+    inputs = tokenizer(input_text, return_tensors="pt").to(device)
+    
+    # Calculate max_new_tokens based on input size
+    input_length = inputs['input_ids'].shape[1]
+    max_new_tokens = max_length - input_length
+    if max_new_tokens <= 0:
+        max_new_tokens = 200  # Ensure at least a few tokens are generated
+    
+    with torch.no_grad():
+        output_tokens = model.generate(
+            **inputs, 
+            max_new_tokens=max_new_tokens,  # Use max_new_tokens to control the generated length
+            temperature=0.7, 
+            top_k=50, 
+            top_p=0.9, 
+            repetition_penalty=1.2
+        )
+    
+    summary = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
+    return summary
+
+# Step 4: RAG Summarization Function
+def rag_summarize(query, gdf, index, k=5):
+    query = query.lower().strip()
+    query_embedding = embedder.encode([query])[0]
+
+    # Retrieve top-k relevant documents
+    distances, indices = index.search(np.array([query_embedding]), k)
+    retrieved_docs = gdf.iloc[indices[0]]
+
+    # Aggregate data
+    total_acreage = retrieved_docs['acreage'].sum()
+    usable_acreage = retrieved_docs['usable_area'].sum()
+    location_data = "\n".join([
+        f"- **Flood Zone**: {row['FLD_ZONE']}, **Subtype**: {row['ZONE_SUBTY']}, "
+        f"**Acreage**: {row['acreage']:.2f}, **Usable Area**: {row['usable_area']:.2f} acres"
+        for _, row in retrieved_docs.iterrows()
+    ])
+
+    # Use LLaMA for summarization
+    summary = llama_summarize(query, total_acreage, usable_acreage, location_data)
+    
+    return summary
+
+# Streamlit Interface
+st.title("🌊 Floodland Summary Bot (Powered by LLaMA-2)")
+
+# Input for location
+user_input = st.text_input("Enter a location (e.g., New York)", "")
+
+# When the user inputs a query, display the summary
+if user_input:
+    query = user_input.lower().strip()
+    summary = rag_summarize(query, gdf, index)
+    st.write(summary)