Init code add

app.py

@@ -1,7 +1,133 @@
 import gradio as gr
+import os
+import json
+from openai import OpenAI
+from geopy.geocoders import Nominatim
+from folium import Map, GeoJson
+from gradio_folium import Folium
+import cv2
+import numpy as np
+import torch
+from diffusers import ControlNetModel, StableDiffusionControlNetInpaintPipeline
+from PIL import Image
+import io
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Initialize APIs
+openai_client = OpenAI(api_key=os.environ['OPENAI_API_KEY'])
+geolocator = Nominatim(user_agent="geoapi")
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# Function to fetch coordinates
+def get_geo_coordinates(location_name):
+    try:
+        location = geolocator.geocode(location_name)
+        if location:
+            return [location.longitude, location.latitude]
+        return None
+    except Exception as e:
+        print(f"Error fetching coordinates for {location_name}: {e}")
+        return None
+
+# Function to process OpenAI chat response
+def process_openai_response(query):
+    response = openai_client.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=[
+            {"role": "system", "content": "You are a skilled assistant answering geographical and historical questions..."},
+            {"role": "user", "content": query}
+        ],
+        temperature=1,
+        max_tokens=2048,
+        top_p=1,
+        frequency_penalty=0,
+        presence_penalty=0,
+        response_format={"type": "json_object"}
+    )
+    return json.loads(response.choices[0].message.content)
+
+# Generate GeoJSON from OpenAI response
+def generate_geojson(response):
+    feature_type = response['output']['feature_representation']['type']
+    city_names = response['output']['feature_representation']['cities']
+    properties = response['output']['feature_representation']['properties']
+    
+    coordinates = []
+    for city in city_names:
+        coord = get_geo_coordinates(city)
+        if coord:
+            coordinates.append(coord)
+    
+    if feature_type == "Polygon":
+        coordinates.append(coordinates[0])  # Close the polygon
+
+    return {
+        "type": "FeatureCollection",
+        "features": [{
+            "type": "Feature",
+            "properties": properties,
+            "geometry": {
+                "type": feature_type,
+                "coordinates": [coordinates] if feature_type == "Polygon" else coordinates
+            }
+        }]
+    }
+
+# Generate map image
+def save_map_image(geojson_data):
+    m = Map()
+    geo_layer = GeoJson(geojson_data, name="Feature map")
+    geo_layer.add_to(m)
+    bounds = get_bounds(geojson_data)
+    m.fit_bounds(bounds)
+    img_data = m._to_png(5)
+    img = Image.open(io.BytesIO(img_data))
+    img.save('map_image.png')
+    return 'map_image.png'
+
+# ControlNet pipeline setup
+controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16)
+pipeline = StableDiffusionControlNetInpaintPipeline.from_pretrained(
+    "runwayml/stable-diffusion-inpainting", controlnet=controlnet, torch_dtype=torch.float16
+)
+pipeline.enable_model_cpu_offload()
+
+def generate_satellite_image(init_image_path, mask_image_path, prompt):
+    init_image = Image.open(init_image_path)
+    mask_image = Image.open(mask_image_path)
+    control_image = make_inpaint_condition(init_image, mask_image)
+    result = pipeline(prompt=prompt, image=init_image, mask_image=mask_image, control_image=control_image)
+    return result.images[0]
+
+# Gradio UI
+def handle_query(query):
+    # Process OpenAI response
+    response = process_openai_response(query)
+    geojson_data = generate_geojson(response)
+    
+    # Save map image
+    map_image_path = save_map_image(geojson_data)
+    
+    # Generate mask for ControlNet
+    empty_map = cv2.imread("empty_map_image.png")
+    map_image = cv2.imread(map_image_path)
+    difference = cv2.absdiff(cv2.cvtColor(empty_map, cv2.COLOR_BGR2GRAY), cv2.cvtColor(map_image, cv2.COLOR_BGR2GRAY))
+    _, mask = cv2.threshold(difference, 15, 255, cv2.THRESH_BINARY)
+    cv2.imwrite("mask.png", mask)
+    
+    # Generate satellite image
+    satellite_image = generate_satellite_image("map_image.png", "mask.png", response['output']['feature_representation']['properties']['description'])
+    
+    return map_image_path, satellite_image
+
+# Gradio interface
+with gr.Blocks() as demo:
+    with gr.Row():
+        query_input = gr.Textbox(label="Enter Query")
+        submit_btn = gr.Button("Submit")
+    with gr.Row():
+        map_output = gr.Image(label="Map Visualization")
+        satellite_output = gr.Image(label="Generated Satellite Image")
+    
+    submit_btn.click(handle_query, inputs=[query_input], outputs=[map_output, satellite_output])
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,10 @@
+openai       # For interacting with OpenAI API
+gradio       # For creating the Gradio UI
+gradio-folium   # For embedding Folium maps into Gradio
+folium        # For creating maps
+geopy       # For fetching geolocation data
+torch      # For PyTorch (used by Diffusers and ControlNet)
+diffusers     # For the Stable Diffusion inpainting pipeline
+opencv-python-headless # For image processing with OpenCV
+Pillow # For working with images
+numpy  # For numerical operations