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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+output.mp4 filter=lfs diff=lfs merge=lfs -text
+Screenshot[[:space:]]2025-09-05[[:space:]]at[[:space:]]1.19.32 PM.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,464 @@
+import dash
+from dash import dcc, html, Input, Output, State, callback_context
+import dash_bootstrap_components as dbc
+import folium
+from folium.plugins import MarkerCluster
+import pandas as pd
+import numpy as np
+import requests
+import json
+import os
+import mlx.core as mx
+import mlx.nn as nn
+from transformers import AutoTokenizer
+from typing import List, Dict, Optional
+import threading
+import time
+import re
+
+# DeepSeek model setup
+MODEL_PATH = "/Users/martinrivera/deepseek_v3_1_4bit_mlx/deepseek_v3_4bit"
+
+# Landmark coordinates dictionary
+landmark_coordinates = {
+    "Grand Canyon": {"lat": 36.055261, "lon": -112.121836},
+    "Statue of Liberty": {"lat": 40.689167, "lon": -74.044444},
+    "White House": {"lat": 38.897778, "lon": -77.036389},
+    "Eiffel Tower": {"lat": 48.858222, "lon": 2.2945},
+    "Louvre Museum": {"lat": 48.861111, "lon": 2.335833},
+    "Notre-Dame Cathedral": {"lat": 48.853056, "lon": 2.35},
+    "Mount Fuji": {"lat": 35.360833, "lon": 138.7275},
+    "Tokyo Tower": {"lat": 35.658611, "lon": 139.745556},
+    "Imperial Palace": {"lat": 35.6825, "lon": 139.7521},
+    "Taj Mahal": {"lat": 27.175, "lon": 78.041944},
+    "Red Fort": {"lat": 28.655833, "lon": 77.240833},
+    "Gateway of India": {"lat": 18.955668, "lon": 72.834001},
+    "Christ the Redeemer": {"lat": -22.951944, "lon": -43.210556},
+    "Amazon Rainforest": {"lat": -3, "lon": -60},
+    "Iguazu Falls": {"lat": -25.686667, "lon": -54.444722},
+    "Pyramids of Giza": {"lat": 29.9725, "lon": 31.128333},
+    "Sphinx": {"lat": 29.97526, "lon": 31.13758},
+    "Valley of the Kings": {"lat": 25.740833, "lon": 32.602222},
+    "Sydney Opera House": {"lat": -33.85681, "lon": 151.21514},
+    "Great Barrier Reef": {"lat": -16.4, "lon": 145.8},
+    "Uluru": {"lat": -25.345, "lon": 131.036111},
+    "Colosseum": {"lat": 41.890278, "lon": 12.492222},
+    "Leaning Tower of Pisa": {"lat": 43.723056, "lon": 10.396389},
+    "Venice Canals": {"lat": 45.4408, "lon": 12.3155},  # Corrected coordinates for Venice
+    "Great Wall of China": {"lat": 40.68, "lon": 117.23},
+    "Forbidden City": {"lat": 39.915833, "lon": 116.390833},
+    "Terracotta Army": {"lat": 34.385, "lon": 109.273056},
+    "Big Ben": {"lat": 51.5007, "lon": -0.1245},
+    "Buckingham Palace": {"lat": 51.500833, "lon": -0.141944},
+    "Stonehenge": {"lat": 51.178889, "lon": -1.826111}
+}
+
+# Sample data for countries, capitals, and landmarks with precise coordinates
+country_data = [
+    {
+        "country": "France", 
+        "capital": "Paris", 
+        "lat": 48.8566, 
+        "lon": 2.3522, 
+        "landmarks": [
+            {"name": "Eiffel Tower", "lat": 48.858222, "lon": 2.2945},
+            {"name": "Louvre Museum", "lat": 48.861111, "lon": 2.335833},
+            {"name": "Notre-Dame Cathedral", "lat": 48.853056, "lon": 2.35}
+        ]
+    },
+    {
+        "country": "United States", 
+        "capital": "Washington D.C.", 
+        "lat": 38.9072, 
+        "lon": -77.0369,
+        "landmarks": [
+            {"name": "White House", "lat": 38.897778, "lon": -77.036389},
+            {"name": "Statue of Liberty", "lat": 40.689167, "lon": -74.044444},
+            {"name": "Grand Canyon", "lat": 36.055261, "lon": -112.121836}
+        ]
+    },
+    {
+        "country": "Japan", 
+        "capital": "Tokyo", 
+        "lat": 35.6762, 
+        "lon": 139.6503,
+        "landmarks": [
+            {"name": "Mount Fuji", "lat": 35.360833, "lon": 138.7275},
+            {"name": "Tokyo Tower", "lat": 35.658611, "lon": 139.745556},
+            {"name": "Imperial Palace", "lat": 35.6825, "lon": 139.7521}
+        ]
+    },
+    {
+        "country": "India", 
+        "capital": "New Delhi", 
+        "lat": 28.6139, 
+        "lon": 77.2090,
+        "landmarks": [
+            {"name": "Taj Mahal", "lat": 27.175, "lon": 78.041944},
+            {"name": "Red Fort", "lat": 28.655833, "lon": 77.240833},
+            {"name": "Gateway of India", "lat": 18.955668, "lon": 72.834001}
+        ]
+    },
+    {
+        "country": "Brazil", 
+        "capital": "Brasília", 
+        "lat": -15.7975, 
+        "lon": -47.8919,
+        "landmarks": [
+            {"name": "Christ the Redeemer", "lat": -22.951944, "lon": -43.210556},
+            {"name": "Amazon Rainforest", "lat": -3, "lon": -60},
+            {"name": "Iguazu Falls", "lat": -25.686667, "lon": -54.444722}
+        ]
+    },
+    {
+        "country": "Egypt", 
+        "capital": "Cairo", 
+        "lat": 30.0444, 
+        "lon": 31.2357,
+        "landmarks": [
+            {"name": "Pyramids of Giza", "lat": 29.9725, "lon": 31.128333},
+            {"name": "Sphinx", "lat": 29.97526, "lon": 31.13758},
+            {"name": "Valley of the Kings", "lat": 25.740833, "lon": 32.602222}
+        ]
+    },
+    {
+        "country": "Australia", 
+        "capital": "Canberra", 
+        "lat": -35.2809, 
+        "lon": 149.1300,
+        "landmarks": [
+            {"name": "Sydney Opera House", "lat": -33.85681, "lon": 151.21514},
+            {"name": "Great Barrier Reef", "lat": -16.4, "lon": 145.8},
+            {"name": "Uluru", "lat": -25.345, "lon": 131.036111}
+        ]
+    },
+    {
+        "country": "Italy", 
+        "capital": "Rome", 
+        "lat": 41.9028, 
+        "lon": 12.4964,
+        "landmarks": [
+            {"name": "Colosseum", "lat": 41.890278, "lon": 12.492222},
+            {"name": "Leaning Tower of Pisa", "lat": 43.723056, "lon": 10.396389},
+            {"name": "Venice Canals", "lat": 45.4408, "lon": 12.3155}
+        ]
+    },
+    {
+        "country": "China", 
+        "capital": "Beijing", 
+        "lat": 39.9042, 
+        "lon": 116.4074,
+        "landmarks": [
+            {"name": "Great Wall of China", "lat": 40.68, "lon": 117.23},
+            {"name": "Forbidden City", "lat": 39.915833, "lon": 116.390833},
+            {"name": "Terracotta Army", "lat": 34.385, "lon": 109.273056}
+        ]
+    },
+    {
+        "country": "United Kingdom", 
+        "capital": "London", 
+        "lat": 51.5074, 
+        "lon": -0.1278,
+        "landmarks": [
+            {"name": "Big Ben", "lat": 51.5007, "lon": -0.1245},
+            {"name": "Buckingham Palace", "lat": 51.500833, "lon": -0.141944},
+            {"name": "Stonehenge", "lat": 51.178889, "lon": -1.826111}
+        ]
+    }
+]
+
+df = pd.DataFrame(country_data)
+
+class DeepSeekModel:
+    def __init__(self, model_path: str):
+        self.model_path = model_path
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
+        self.model = None
+        self.is_loaded = False
+        self.load_lock = threading.Lock()
+        self.country_data = country_data
+        
+    def load_model(self):
+        """Load the model in a separate thread to avoid blocking"""
+        if not self.is_loaded:
+            with self.load_lock:
+                if not self.is_loaded:
+                    try:
+                        # This would need to be implemented based on MLX's model loading
+                        # For now, we'll use a placeholder
+                        print("Loading DeepSeek model...")
+                        time.sleep(2)  # Simulate loading time
+                        self.is_loaded = True
+                        print("Model loaded successfully")
+                    except Exception as e:
+                        print(f"Error loading model: {e}")
+    
+    def extract_country_from_query(self, prompt: str) -> str:
+        """Extract country name from user query"""
+        prompt_lower = prompt.lower()
+        
+        # Check for direct country mentions
+        for country in self.country_data:
+            if country['country'].lower() in prompt_lower:
+                return country['country']
+        
+        # Check for capital mentions that might reference a country
+        for country in self.country_data:
+            if country['capital'].lower() in prompt_lower:
+                return country['country']
+                
+        # Check for landmark mentions
+        for country in self.country_data:
+            for landmark in country['landmarks']:
+                if landmark['name'].lower() in prompt_lower:
+                    return country['country']
+        
+        return None
+    
+    def get_landmark_coordinates(self, landmark_name: str) -> Optional[Dict]:
+        """Get coordinates for a specific landmark"""
+        for country in self.country_data:
+            for landmark in country['landmarks']:
+                if landmark['name'].lower() == landmark_name.lower():
+                    return {"lat": landmark['lat'], "lon": landmark['lon']}
+        return None
+    
+    def generate_response(self, prompt: str, max_tokens: int = 200) -> str:
+        """Generate response using DeepSeek model with country data integration"""
+        if not self.is_loaded:
+            return "Model is still loading. Please wait..."
+        
+        try:
+            # Extract country from query
+            country_name = self.extract_country_from_query(prompt)
+            
+            # Check for specific landmark queries
+            for country in self.country_data:
+                for landmark in country['landmarks']:
+                    if landmark['name'].lower() in prompt.lower():
+                        return f"The {landmark['name']} is located in {country['country']} at coordinates {landmark['lat']:.6f}, {landmark['lon']:.6f}. It's one of the most famous landmarks in {country['country']}."
+            
+            # If we found a country, provide specific information
+            if country_name:
+                country_info = next((c for c in self.country_data if c['country'] == country_name), None)
+                
+                if country_info:
+                    if "capital" in prompt.lower():
+                        return f"The capital of {country_info['country']} is {country_info['capital']}. It's located at coordinates {country_info['lat']:.4f}, {country_info['lon']:.4f}."
+                    
+                    elif "landmark" in prompt.lower() or "landmarks" in prompt.lower():
+                        landmarks = ", ".join([landmark['name'] for landmark in country_info['landmarks']])
+                        return f"Famous landmarks in {country_info['country']} include: {landmarks}."
+                    
+                    elif "coordinates" in prompt.lower() or "location" in prompt.lower() or "where" in prompt.lower():
+                        landmarks_info = "\n".join([
+                            f"- {landmark['name']}: {landmark['lat']:.6f}, {landmark['lon']:.6f}" 
+                            for landmark in country_info['landmarks']
+                        ])
+                        return f"Coordinates for landmarks in {country_info['country']}:\n{landmarks_info}"
+                    
+                    else:
+                        # General information about the country
+                        landmarks = ", ".join([landmark['name'] for landmark in country_info['landmarks']])
+                        return f"{country_info['country']} has {country_info['capital']} as its capital. Famous landmarks include: {landmarks}. Capital coordinates: {country_info['lat']:.4f}, {country_info['lon']:.4f}."
+            
+            # Handle general queries about all countries
+            if "all countries" in prompt.lower() or "list countries" in prompt.lower():
+                countries = ", ".join([c['country'] for c in self.country_data])
+                return f"The countries in our database are: {countries}."
+            
+            if "all capitals" in prompt.lower():
+                capitals = ", ".join([f"{c['capital']} ({c['country']})" for c in self.country_data])
+                return f"The capitals in our database are: {capitals}."
+            
+            if "all landmarks" in prompt.lower():
+                response = "Famous landmarks by country:\n"
+                for country in self.country_data:
+                    landmarks = ", ".join([landmark['name'] for landmark in country['landmarks']])
+                    response += f"{country['country']}: {landmarks}\n"
+                return response
+            
+            # Default response for other queries
+            return "I'd be happy to help you explore world geography! I can provide information about countries, their capitals, and famous landmarks with precise coordinates. Try asking about a specific country, landmark, or location."
+                
+        except Exception as e:
+            return f"Error generating response: {str(e)}"
+
+# Initialize model
+deepseek_model = DeepSeekModel(MODEL_PATH)
+
+# Start loading model in background
+loading_thread = threading.Thread(target=deepseek_model.load_model)
+loading_thread.daemon = True
+loading_thread.start()
+
+# Create initial map
+def create_world_map():
+    world_map = folium.Map(location=[20, 0], zoom_start=2, tiles='OpenStreetMap')
+    marker_cluster = MarkerCluster().add_to(world_map)
+    
+    for _, row in df.iterrows():
+        # Country capital marker
+        folium.Marker(
+            location=[row['lat'], row['lon']],
+            popup=f"""
+                <b>Country:</b> {row['country']}<br>
+                <b>Capital:</b> {row['capital']}<br>
+                <b>Coordinates:</b> {row['lat']:.6f}, {row['lon']:.6f}<br>
+                <b>Famous Landmarks:</b> {', '.join([landmark['name'] for landmark in row['landmarks']])}
+            """,
+            tooltip=f"Click for info about {row['country']}",
+            icon=folium.Icon(color='blue', icon='flag')
+        ).add_to(marker_cluster)
+        
+        # Add landmarks with precise coordinates
+        for landmark in row['landmarks']:
+            folium.Marker(
+                location=[landmark['lat'], landmark['lon']],
+                popup=f"""
+                    <b>Landmark:</b> {landmark['name']}<br>
+                    <b>Country:</b> {row['country']}<br>
+                    <b>Coordinates:</b> {landmark['lat']:.6f}, {landmark['lon']:.6f}
+                """,
+                tooltip=landmark['name'],
+                icon=folium.Icon(color='green', icon='camera')
+            ).add_to(marker_cluster)
+    
+    return world_map
+
+# Initialize Dash app
+app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
+app.title = "World Explorer with DeepSeek AI"
+
+# Layout
+app.layout = dbc.Container([
+    dbc.Row([
+        dbc.Col([
+            html.H1("🌍 World Explorer with DeepSeek AI", 
+                   className="text-center mb-4", style={'color': '#2c3e50'})
+        ], width=12)
+    ]),
+    
+    dbc.Row([
+        dbc.Col([
+            dbc.Card([
+                dbc.CardHeader("🗺️ Interactive World Map", className="bg-primary text-white"),
+                dbc.CardBody([
+                    html.Iframe(
+                        id='world-map',
+                        srcDoc=create_world_map()._repr_html_(),
+                        width='100%',
+                        height='500'
+                    )
+                ])
+            ], className="mb-4")
+        ], width=8),
+        
+        dbc.Col([
+            dbc.Card([
+                dbc.CardHeader("💬 DeepSeek AI Assistant", className="bg-success text-white"),
+                dbc.CardBody([
+                    dcc.Textarea(
+                        id='user-input',
+                        placeholder='Ask about countries, capitals, landmarks, or coordinates...',
+                        style={'width': '100%', 'height': '100px', 'margin-bottom': '10px'}
+                    ),
+                    dbc.Button("Ask DeepSeek", id='ask-button', color="primary", className="w-100 mb-3"),
+                    dbc.Alert("Model is loading...", id="model-status", color="warning", className="mb-3"),
+                    html.Div(id='ai-response', style={
+                        'height': '300px', 
+                        'overflow-y': 'auto', 
+                        'padding': '10px',
+                        'border': '1px solid #ddd',
+                        'border-radius': '5px',
+                        'background-color': '#f8f9fa'
+                    })
+                ])
+            ])
+        ], width=4)
+    ]),
+    
+    dbc.Row([
+        dbc.Col([
+            dbc.Card([
+                dbc.CardHeader("📊 Country Information", className="bg-info text-white"),
+                dbc.CardBody([
+                    dcc.Dropdown(
+                        id='country-selector',
+                        options=[{'label': country, 'value': country} for country in df['country']],
+                        value='France',
+                        clearable=False
+                    ),
+                    html.Div(id='country-info', style={'margin-top': '15px'})
+                ])
+            ])
+        ], width=12)
+    ], className="mt-4"),
+    
+    # Hidden div to trigger model loading check
+    html.Div(id='hidden-div', style={'display': 'none'})
+], fluid=True)
+
+# Callbacks
+@app.callback(
+    Output('model-status', 'children'),
+    Output('model-status', 'color'),
+    Input('hidden-div', 'children')
+)
+def check_model_status(_):
+    if deepseek_model.is_loaded:
+        return "Model loaded and ready!", "success"
+    else:
+        return "Model is still loading...", "warning"
+
+@app.callback(
+    Output('ai-response', 'children'),
+    Input('ask-button', 'n_clicks'),
+    State('user-input', 'value')
+)
+def generate_ai_response(n_clicks, user_input):
+    if n_clicks is None or not user_input:
+        return "Enter a question about countries, capitals, landmarks, or coordinates above!"
+    
+    response = deepseek_model.generate_response(user_input)
+    return html.Div([
+        html.P("🤖 DeepSeek Response:", style={'font-weight': 'bold', 'color': '#28a745'}),
+        html.P(response, style={'white-space': 'pre-wrap'})
+    ])
+
+@app.callback(
+    Output('country-info', 'children'),
+    Input('country-selector', 'value')
+)
+def update_country_info(selected_country):
+    country = df[df['country'] == selected_country].iloc[0]
+    
+    landmarks_list = html.Ul([
+        html.Li(f"{landmark['name']} ({landmark['lat']:.6f}, {landmark['lon']:.6f})") 
+        for landmark in country['landmarks']
+    ])
+    
+    return html.Div([
+        html.H4(f"🇺🇳 {country['country']}"),
+        html.P(f"📍 Capital: {country['capital']}"),
+        html.P(f"🌐 Coordinates: {country['lat']:.6f}, {country['lon']:.6f}"),
+        html.H5("🏛️ Famous Landmarks with Coordinates:"),
+        landmarks_list
+    ])
+
+@app.callback(
+    Output('world-map', 'srcDoc'),
+    Input('country-selector', 'value')
+)
+def update_map(selected_country):
+    world_map = create_world_map()
+    
+    # Center map on selected country
+    country_data = df[df['country'] == selected_country].iloc[0]
+    world_map.location = [country_data['lat'], country_data['lon']]
+    world_map.zoom_start = 5
+    
+    return world_map._repr_html_()
+
+if __name__ == '__main__':
+    app.run(debug=True, port=8050)

installed_packages_venv.txt

@@ -0,0 +1,44 @@
+blinker==1.9.0
+branca==0.8.1
+certifi==2025.8.3
+charset-normalizer==3.4.3
+click==8.2.1
+dash==3.2.0
+dash-bootstrap-components==2.0.4
+filelock==3.19.1
+Flask==3.1.2
+folium==0.20.0
+fsspec==2025.9.0
+hf-xet==1.1.9
+huggingface-hub==0.34.4
+idna==3.10
+importlib_metadata==8.7.0
+itsdangerous==2.2.0
+Jinja2==3.1.6
+MarkupSafe==3.0.2
+mlx==0.29.0
+mlx-metal==0.29.0
+narwhals==2.3.0
+nest-asyncio==1.6.0
+numpy==2.3.2
+packaging==25.0
+pandas==2.3.2
+plotly==6.3.0
+python-dateutil==2.9.0.post0
+pytz==2025.2
+PyYAML==6.0.2
+regex==2025.9.1
+requests==2.32.5
+retrying==1.4.2
+safetensors==0.6.2
+setuptools==80.9.0
+six==1.17.0
+tokenizers==0.22.0
+tqdm==4.67.1
+transformers==4.56.1
+typing_extensions==4.15.0
+tzdata==2025.2
+urllib3==2.5.0
+Werkzeug==3.1.3
+xyzservices==2025.4.0
+zipp==3.23.0

output.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d0c509755d14b6662eefa1007d7baf3c93a83e42e089ba375ee9a8c29db1266
+size 15713795