mib

README.md

@@ -1,7 +1,7 @@
 ---
 title: crisesStorylinesRAG
-app_file: app_pyvis_new.py
+app_file: app_pyvis.py
 sdk: gradio
-sdk_version: 5.33.1
+sdk_version: 5.0.1
 ---
-# crisesStorylinesRAG
+# crisesStorylinesRAG

app_pyvis_new.py

@@ -1,792 +0,0 @@
-import os
-import pandas as pd
-from datetime import date
-import gradio as gr
-from pyvis.network import Network
-import ast
-from openai import OpenAI
-import string
-from datetime import datetime
-import random
-import geopandas as gpd
-import folium
-from shapely.geometry import mapping
-from shapely.geometry import Polygon
-from shapely.ops import unary_union
-import dropbox
-from dropbox.exceptions import ApiError
-import io 
-from itertools import combinations
-from typing import Optional, Union
-import torch
-from transformers import T5ForConditionalGeneration, T5Tokenizer
-import inflect
-from itertools import combinations
-from typing import Optional, Union
-import torch
-import osmnx as osm
-
-
-# Replace these with your actual app key and secret
-APP_KEY = os.environ['APP_KEY']
-APP_SECRET = os.environ['APP_SECRET']
-REFRESH_TOKEN = os.environ['REFRESH_TOKEN']
-
-
-EMM_RETRIEVERS_OPENAI_API_BASE_URL="https://api-gpt.jrc.ec.europa.eu/v1"
-EMM_RETRIEVERS_OPENAI_API_KEY = os.environ['EMM_RETRIEVERS_OPENAI_API_KEY']
-
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-usr_tkn_consose_read = os.environ['usr_tkn_consose_read']
-
-inflect_engine = inflect.engine()
-
-model_id = os.environ['ie_model_id']
-tokenizer = T5Tokenizer.from_pretrained(model_id)
-model = T5ForConditionalGeneration.from_pretrained(model_id)
-
-    
-client1 = OpenAI(
-    api_key=EMM_RETRIEVERS_OPENAI_API_KEY,
-    base_url="https://api-gpt.jrc.ec.europa.eu/v1",
-)
-
-
-def geocode_emdat(location):
-    def process_geocoding(location_to_geocode):
-        try:
-            return osm.geocode_to_gdf(location_to_geocode)["geometry"].iloc[0]
-        except Exception:
-            return None
-
-    geocoded_location = process_geocoding(location)
-
-    if geocoded_location is None:
-        print(f"Error geocoding location '{location}'. Trying to correct with GPT-4.")
-        response = client1.chat.completions.create(
-            model="gpt-4o",
-            stream=False,
-            messages=[{"role": "user", "content": f"Correct spelling or grammar or substitute with most commonly used location name by Google Maps, give me only the answer in the form 'Country, Location' filled with the corrected Country and Location: '{location}'"}]
-        )
-        corrected_location = response.choices[0].message.content.strip()
-        geocoded_location = process_geocoding(corrected_location)
-
-    return geocoded_location
-
-
-def get_country_boundary(country_name):
-    # Filter the world GeoDataFrame for the country
-    country = world[world['NAME'] == country_name]
-    if not country.empty:
-        # Return the country's geometry
-        return country.geometry.iloc[0]
-    else:
-        # Return None if country not found
-        return None
-
-def get_geometries(row):
-    country = row['Country']
-    locations = row['Locations']
-    
-    # Return NaN if locations is NaN
-    if pd.isna(locations):
-        return None
-    
-    # Get the country's boundary
-    country_boundary = get_country_boundary(country)
-    
-    # If no country boundary is found, return None
-    if country_boundary is None:
-        return None
-    
-    locations_list = locations.split(', ')
-    
-    # Get polygons for each location, ignoring None results
-    polygons = [geocode_emdat(f"{country}, {location}") for location in locations_list]
-    polygons = [polygon for polygon in polygons if polygon is not None]
-    
-    # Filter polygons to remove those outside the country boundary
-    valid_polygons = [polygon for polygon in polygons if polygon.within(country_boundary)]
-    
-    # If there are no valid polygons, return None
-    if not valid_polygons:
-        return None
-    
-    # Combine them into a single geometry using unary_union
-    combined_geometry = unary_union(valid_polygons)
-    print(combined_geometry)
-    
-    return combined_geometry
-
-
-def singularize(text):
-    """Convert a word to its singular form."""
-    if inflect_engine.singular_noun(text):
-        return inflect_engine.singular_noun(text)
-    return text
-
-def is_singular_plural_pair(word1, word2):
-    """Check if two words are singular/plural forms of each other."""
-    return singularize(word1) == singularize(word2)
-
-
-def extract_edge_and_clean(row, relations):
-    for relation in relations:
-        if relation in row['source']:
-            row['source'] = row['source'].replace(relation, '').strip()
-            row['edge'] = relation
-        elif relation in row['target']:
-            row['target'] = row['target'].replace(relation, '').strip()
-            row['edge'] = relation
-    return row
-
-def generate_with_temperature(prompt, temperature=1.0, top_k=50, top_p=0.95, max_length=50):
-    inputs = tokenizer(prompt, return_tensors='pt').to(device)
-
-    outputs = model.generate(
-        input_ids=inputs.input_ids,
-        max_length=max_length,
-        do_sample=True,
-        temperature=temperature,
-        top_k=top_k,
-        top_p=top_p
-    )
-
-    decoded_texts = tokenizer.batch_decode(outputs, skip_special_tokens=True)
-    return decoded_texts
-
-def generate_new_relations(
-    graph_df: pd.DataFrame,
-    new_node: str,
-    max_combinations_fraction: float = 0.3,
-    num_beams: int = 6,  # Note: Beams are ignored in sampling
-    max_length: int = 50,
-    temperature: float = 1.0,
-    top_k: int = 50,
-    top_p: float = 0.95,
-    seed: Optional[int] = None,
-    verbose: bool = False
-) -> pd.DataFrame:
-    if seed is not None:
-        random.seed(seed)
-
-    records = graph_df.to_dict('records')
-    all_combos = list(combinations(records, 3))
-    max_iters = max(1, int(max_combinations_fraction * len(all_combos)))
-    num_iters = random.randint(1, max_iters)
-
-    if verbose:
-        print(f"Total possible combinations: {len(all_combos)}")
-        print(f"Sampling {num_iters} combos")
-
-    all_predictions = []
-
-    for _ in range(num_iters):
-        combo = random.choice(all_combos)
-        for choice in ('source', 'target'):
-            last = combo[-1].copy()
-            if choice == 'target':
-                prompt_template = f"<extra_id_0> {new_node}."
-            else:
-                prompt_template = f"{new_node} <extra_id_0>."
-
-            for _ in range(2):
-                perm = list(combo[:-1])
-                random.shuffle(perm)
-
-                parts = ["If"]
-                for row in perm:
-                    parts.append(f"{row['source']} {row['edge']} {row['target']},")
-                    parts.append("and")
-                parts.append(f"{last['source']} {last['edge']} {last['target']},")
-                parts.append("then")
-                parts.append(prompt_template)
-                prompt = " ".join(parts)
-
-                if verbose:
-                    print("Generated Prompt:", prompt)
-
-                preds = generate_with_temperature(
-                    prompt, temperature, top_k, top_p, max_length
-                )
-
-                if verbose:
-                    print("Predictions:", preds)
-
-                for text in preds:
-                    #print("Generated Prompt:", prompt)
-                    #print("text = ", text)
-                    #print("last = ", last)
-                    all_predictions.append((choice, text, last))
-                    
-    
-
-    if not all_predictions:
-        return graph_df.copy()
-
-    grouped = {}
-    for choice, text, last in all_predictions:
-        key = (choice, last['source'], last['edge'], last['target'])
-        grouped.setdefault(key, []).append(text)
-
-    new_edges = []
-    for (choice, src, edge, tgt), texts in grouped.items():
-        common = set(texts)
-        if not common:
-            continue
-        for pred in common:
-            if choice == 'target':
-                new_edges.append({'source': pred, 'edge': None, 'target': new_node})
-            else:
-                new_edges.append({'source': new_node, 'edge': None, 'target': pred})
-
-    new_df = pd.DataFrame(new_edges).drop_duplicates()
-
-    relations = ['causes', 'prevents']
-    new_df = new_df.apply(lambda row: extract_edge_and_clean(row, relations), axis=1)
-
-    result = pd.concat([graph_df, new_df], ignore_index=True)
-
-    result['pair'] = result.apply(lambda x: tuple(sorted([x['source'], x['target']])), axis=1)
-    result = result.drop_duplicates(subset=['pair'])
-    result = result[result['source'] != result['target']]
-    result = result.drop(columns=['pair'])
-    # Remove duplicates based on plural/singular forms
-    result['source_singular'] = result['source'].apply(singularize)
-    result['target_singular'] = result['target'].apply(singularize)
-    result = result.drop_duplicates(subset=['source_singular', 'edge', 'target_singular'])
-    result = result[result['source'] != result['target']]
-    result = result.drop(columns=['source_singular', 'target_singular'])
-
-    return result
-
-# Function to get a Dropbox client, refreshing the token if needed
-def get_dropbox_client():
-    try:
-        # Create a Dropbox client using the refresh token
-        dbx = dropbox.Dropbox(
-            oauth2_refresh_token=REFRESH_TOKEN,
-            app_key=APP_KEY,
-            app_secret=APP_SECRET
-        )
-        return dbx
-    except Exception as e:
-        print(f"Error creating Dropbox client: {e}")
-        return None
-
-    
-client1 = OpenAI(
-    api_key=EMM_RETRIEVERS_OPENAI_API_KEY,
-    base_url="https://api-gpt.jrc.ec.europa.eu/v1",
-)
-
-df = pd.read_csv("https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/ETOHA/storylines/emdat2.csv", sep=',', header=0, dtype=str, encoding='utf-8')
-
-world = gpd.read_file('https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/ETOHA/storylines/ne_110m_admin_0_countries.shp')
-
-
-# Function to get fallback coordinates from GeoPandas
-def get_country_centroid(country_name):
-    # Filter the world GeoDataFrame for the country
-    country = world[world['NAME'] == country_name]
-    if not country.empty:
-        # Get the centroid of the country's geometry
-        centroid = country.geometry.centroid.iloc[0]
-        return (centroid.y, centroid.x)
-    else:
-        # Default to (0, 0) if country not found
-        return (0, 0)
-
-# Function to plot a geometry using Folium
-def plot_geometry_folium(geometry, location_name='Location', country_name=None):
-    if geometry is not None:
-        # Get the centroid for initial map location
-        centroid = geometry.centroid
-        initial_coords = (centroid.y, centroid.x)
-    else:
-        # Use geopandas to get fallback coordinates
-        initial_coords = get_country_centroid(country_name)
-
-    # Create the map centered at initial_coords
-    m = folium.Map(location=initial_coords, zoom_start=6)
-
-    if geometry is not None:
-        # Convert to GeoJSON for Folium if geometry exists
-        geo_json = mapping(geometry)
-        # Add GeoJSON to the map
-        folium.GeoJson(geo_json, name=location_name).add_to(m)
-    else:
-        # Add a marker to indicate the country location
-        folium.Marker(initial_coords, popup=location_name).add_to(m)
-
-    # Return the HTML representation of the map object
-    return m._repr_html_()
-
-
-def gpt_story(storyline):
-    prompt = (
-        "Use the information provided to create a short, clear, and useful narrative about a disaster event. "
-        "The goal is to help decision-makers (e.g. policy makers, disaster managers, civil protection) understand what happened, why, and what it caused. "
-        "Keep it short and focused.\n\n"
-        "Include all key information, but keep the text concise and easy to read. Avoid technical jargon.\n\n"
-        "Steps to Follow:\n"
-        "1. Start with what happened: Briefly describe the disaster event (what, where, when, who was affected).\n"
-        "2. Explain why it happened: Use the evidence provided to describe possible causes or triggers (e.g. heavy rainfall, poor infrastructure, heatwave).\n"
-        "3. Show the impacts: Highlight key impacts such as fatalities, displacement, health effects, or damage.\n"
-        "4. Connect the dots: Show how different factors are linked. Use simple cause-effect language (e.g. drought led to crop failure, which caused food insecurity).\n"
-        "5. Mention complexity if needed: If there were multiple contributing factors or reinforcing effects (e.g. climate + conflict), briefly explain them.\n"
-        "6. Keep it useful: Write with a decision-maker in mind. Focus on what matters: drivers, impacts, and lessons for preparedness or response.\n\n"
-        f"Information: {storyline}"
-    )
-
-    completion = client1.chat.completions.create(
-        model='gpt-4o',
-        messages=[
-            {"role": "system", "content": "You are a disaster manager expert in risk dynamics."},
-            {"role": "user", "content": prompt}
-        ]
-    )
-
-    # Extract the content from the response
-    message_content = completion.choices[0].message.content
-    return message_content
-
-
-# DataFrame to store evaluation data
-evaluation_df = pd.DataFrame(columns=["DisNo.", "TPN", "TPL", "FPN", "FPL", "FNN", "FNL", "User ID"])
-
-
-
-def try_parse_date(y, m, d):
-    try:
-        if not y or not m or not d:
-            return None
-        return date(int(float(y)), int(float(m)), int(float(d)))
-    except (ValueError, TypeError):
-        return None
-
-def plot_cgraph_pyvis(grp):
-    if not grp:
-        return "<div>No data available to plot.</div>"
-
-    net = Network(notebook=False, directed=True)
-    edge_colors_dict = {"causes": "red", "prevents": "green"}
-
-    for src, rel, tgt in grp:
-        src = str(src)
-        tgt = str(tgt)
-        rel = str(rel)
-        net.add_node(src, shape="circle", label=src)
-        net.add_node(tgt, shape="circle", label=tgt)
-        edge_color = edge_colors_dict.get(rel, 'black')
-        net.add_edge(src, tgt, title=rel, label=rel, color=edge_color)
-
-    net.repulsion(
-        node_distance=200,
-        central_gravity=0.2,
-        spring_length=200,
-        spring_strength=0.05,
-        damping=0.09
-    )
-    net.set_edge_smooth('dynamic')
-
-    html = net.generate_html()
-    html = html.replace("'", "\"")
-
-    # Adjust the iframe style to center the graph and fit the container
-    html_s = f"""
-    <div style="display: flex; justify-content: center; align-items: center;">
-        <iframe style="width: 90%; height: 800px; margin: 0 auto;" name="result" allow="midi; geolocation; microphone; camera; 
-        display-capture; encrypted-media;" sandbox="allow-modals allow-forms 
-        allow-scripts allow-same-origin allow-popups 
-        allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" 
-        allowpaymentrequest="" frameborder="0" srcdoc='{html}'></iframe>
-    </div>
-    """
-    
-    return html_s
-
-def generate_unique_user_id():
-    # Generate a timestamp string
-    timestamp_str = datetime.now().strftime("%Y%m%d%H%M%S")
-    # Generate a random string of 5 letters
-    random_str = ''.join(random.choices(string.ascii_letters, k=5))
-    # Combine both to form a unique User ID
-    return f"{timestamp_str}_{random_str}"
-
-def load_initial_data():
-    dbx = get_dropbox_client()
-    try:
-        # Try to download the existing CSV file from Dropbox
-        metadata, res = dbx.files_download(DROPBOX_FILE_PATH)
-        csv_content = res.content.decode('utf-8')
-        # Read the CSV content into a DataFrame
-        df = pd.read_csv(io.StringIO(csv_content))
-        print("Loaded existing data from Dropbox.")
-    except ApiError as e:
-        # If file not found, initialize an empty DataFrame
-        if e.error.is_path() and e.error.get_path().is_not_found():
-            df = pd.DataFrame(columns=["DisNo.", "User Feedback", "User ID"])
-            print("No existing file found on Dropbox. Initialized an empty DataFrame.")
-        else:
-            print(f"Error downloading file: {e}")
-            df = pd.DataFrame(columns=["DisNo.", "User Feedback", "User ID"])
-    return df
-
-
-def append_to_csv_on_dropbox(new_content, dropbox_path):
-    dbx = get_dropbox_client()
-    if not dbx:
-        print("Failed to create Dropbox client.")
-        return
-
-    try:
-        # Try to download existing file content
-        metadata, res = dbx.files_download(dropbox_path)
-        existing_content = res.content.decode('utf-8')
-    except ApiError as e:
-        # If file not found, start with empty content
-        if e.error.is_path() and e.error.get_path().is_not_found():
-            existing_content = ''
-        else:
-            print(f"Error downloading file: {e}")
-            return
-
-    # Append new content without header if file already exists
-    if existing_content:
-        new_content_lines = new_content.splitlines()
-        new_content_without_header = '\n'.join(new_content_lines[1:])
-        combined_content = existing_content.rstrip('\n') + '\n' + new_content_without_header
-    else:
-        combined_content = new_content
-
-    try:
-        # Upload combined content back to Dropbox (overwrite)
-        dbx.files_upload(combined_content.encode('utf-8'), dropbox_path, mode=dropbox.files.WriteMode.overwrite)
-        print(f"Appended and uploaded to {dropbox_path} successfully!")
-    except Exception as e:
-        print(f"Error uploading file: {e}")
-
-
-#def append_to_csv_on_dropbox(new_content, dropbox_path):
-#    dbx = dropbox.Dropbox(ACCESS_TOKEN)
-
-#    try:
-        # Try to download existing file content
-#        metadata, res = dbx.files_download(dropbox_path)
-#        existing_content = res.content.decode('utf-8')
-#    except ApiError as e:
-        # If file not found, start with empty content
-#        if e.error.is_path() and e.error.get_path().is_not_found():
-#            existing_content = ''
-#        else:
-#            print(f"Error downloading file: {e}")
-#            return
-
-    # Append new content without header if file already exists
-#    if existing_content:
-#        new_content_lines = new_content.splitlines()
-#        new_content_without_header = '\n'.join(new_content_lines[1:])
-#        combined_content = existing_content.rstrip('\n') + '\n' + new_content_without_header
-#    else:
-#        combined_content = new_content
-
-#    try:
-        # Upload combined content back to Dropbox (overwrite)
-#        dbx.files_upload(combined_content.encode('utf-8'), dropbox_path, mode=dropbox.files.WriteMode.overwrite)
-#        print(f"Appended and uploaded to {dropbox_path} successfully!")
-#    except Exception as e:
-#        print(f"Error uploading file: {e}")
-
-def save_data_to_dropbox():
-    # Convert DataFrame to CSV string
-    csv_content = evaluation_df.to_csv(index=False)
-    # Append the CSV content to the file on Dropbox
-    append_to_csv_on_dropbox(csv_content, DROPBOX_FILE_PATH)
-
-def save_data(dis_no, user_feedback):
-    global evaluation_df
-
-    if not dis_no or dis_no == "Select a Disaster Event":
-        print("Invalid input. Ensure a disaster event is selected.")
-        return
-
-    user_id = generate_unique_user_id()
-    new_data = pd.DataFrame([[dis_no, user_feedback, user_id]], 
-                            columns=["DisNo.", "User Feedback", "User ID"])
-    evaluation_df = pd.concat([evaluation_df, new_data], ignore_index=True)
-    print("Updated DataFrame:")
-    print(evaluation_df)
-
-    save_data_to_dropbox()
-    print(f"Data saved: DisNo: {dis_no}, Feedback: {user_feedback}, User ID: {user_id}")
-    
-    return "Thank you, your answer has been recorded! This will help make AI more reliable."
-
-
-DROPBOX_FILE_PATH = '/evaluation_data.csv'
-evaluation_df = load_initial_data()
-
-
-def update_row_dropdown(disaster_type=None, country=None):
-    # Start with the entire dataframe
-    filtered_df = df
-
-    # Step 1: Filter by Disaster Type
-    if disaster_type:
-        filtered_df = filtered_df[filtered_df['Disaster Type'] == disaster_type]
-
-    # Step 2: Further filter by Country
-    if country:
-        filtered_df = filtered_df[filtered_df['Country'] == country]
-
-    # Step 3: Generate and sort the DisNo. choices based on the filtered DataFrame
-    choices = sorted(filtered_df['DisNo.'].tolist()) if not filtered_df.empty else []
-
-    # Add a placeholder option at the beginning
-    choices = ["Select a Disaster Event"] + choices
-
-    print(f"Available DisNo. for {disaster_type} in {country}: {choices}")
-    
-    # Return the update for the dropdown, defaulting to the placeholder
-    return gr.update(choices=choices, value=choices[0] if choices else None)
-
-
-
-def display_info(selected_row_str, country):
-    if not selected_row_str or selected_row_str == 'Select a Disaster Event':
-        print("No valid disaster event selected.")
-        return ('No valid event selected.', '<div>No graph available.</div>', '', '', '')
-
-    print(f"Selected Country: {country}, Selected Row: {selected_row_str}")
-
-    # Filter the dataframe for the selected disaster number
-    row_data = df[df['DisNo.'] == selected_row_str]
-
-    if not row_data.empty:
-        #print(f"Row data: {row_data}")
-        row_data["geometry"] =  row_data.apply(get_geometries, axis=1)
-        
-        row_data = row_data.squeeze()
-
-        # Combine the relevant columns into a single storyline with labels
-        storyline_parts = [
-            f"Key Information: {row_data.get('key information', '')}",
-            f"Severity: {row_data.get('severity', '')}",
-            f"Key Drivers: {row_data.get('key drivers', '')}",
-            f"Main Impacts, Exposure, and Vulnerability: {row_data.get('main impacts, exposure, and vulnerability', '')}",
-            f"Likelihood of Multi-Hazard Risks: {row_data.get('likelihood of multi-hazard risks', '')}",
-            f"Best Practices for Managing This Risk: {row_data.get('best practices for managing this risk', '')}",
-            f"Recommendations and Supportive Measures for Recovery: {row_data.get('recommendations and supportive measures for recovery', '')}"
-        ]
-        storyline = "\n\n".join(part for part in storyline_parts if part.split(': ')[1])  # Include only non-empty parts
-        cleaned_storyline = gpt_story(storyline)
-        causal_graph_caption = row_data.get('llama graph', '')
-        grp = ast.literal_eval(causal_graph_caption) if causal_graph_caption else []
-        causal_graph_html = plot_cgraph_pyvis(grp)
-
-        # Create the Folium map
-        geometry = row_data.get('geometry', None)
-        folium_map_html = plot_geometry_folium(geometry, location_name=country, country_name=country)
-
-        # Parse and format the start date
-        start_date_str = f"{row_data['Start Year']}-{row_data['Start Month']}-{row_data['Start Day']}"
-
-        # Parse and format the end date
-        end_date_str = f"{row_data['End Year']}-{row_data['End Month']}-{row_data['End Day']}"
-
-        return (
-            cleaned_storyline,
-            causal_graph_html,
-            folium_map_html,
-            start_date_str,
-            end_date_str
-        ) 
-    else:
-        print("No valid data found for the selection.")
-        return ('No valid data found.', '<div>No graph available.</div>', '', '', '')
-
-def process_new_node(selected_row_str, new_node):
-    if not selected_row_str or selected_row_str == 'Select a Disaster Event':
-        print("No valid disaster event selected.")
-        return '<div>No graph available.</div>'
-
-    if not new_node:
-        print("No new node provided.")
-        return '<div>No graph available.</div>'
-
-    print(f"Selected Row: {selected_row_str}, New Node: {new_node}")
-
-    # Filter the dataframe for the selected disaster number
-    row_data = df[df['DisNo.'] == selected_row_str]
-
-    if not row_data.empty:
-        row_data = row_data.squeeze()
-        causal_graph_caption = row_data.get('llama graph', '')
-        grp = ast.literal_eval(causal_graph_caption) if causal_graph_caption else []
-        source, relations, target = list(zip(*grp))
-        kg_df = pd.DataFrame({'source': source, 'target': target, 'edge': relations})
-
-        # Call the generate_new_relations function
-        result_df = generate_new_relations(
-            graph_df=kg_df,
-            new_node=new_node,
-            max_combinations_fraction=0.1,
-            temperature=0.8,  # Adjust temperature for diversity
-            top_k=50,  # Top-k sampling
-            top_p=0.95,  # Top-p (nucleus) sampling
-            seed=42,  # Optional for reproducibility
-            verbose=False  # Optional for debugging
-        )
-
-        # Plot the updated graph with the new relations
-        source = result_df['source'].astype(str)
-        relations = result_df['edge'].astype(str)
-        target = result_df['target'].astype(str)
-        grp = zip(source, relations, target)
-        causal_graph_html = plot_cgraph_pyvis(grp)
-        return causal_graph_html
-    else:
-        print("No valid data found for the selection.")
-        return '<div>No graph available.</div>'
-
-def build_interface():
-    with gr.Blocks() as interface:
-        gr.Markdown(
-        """
-        # From Complexity to Clarity: Leveraging AI to Decode Interconnected Risks
-
-        Welcome to our Gradio application, developed and maintained by [JRC](https://joint-research-centre.ec.europa.eu/) Units: **E1**, **F7**, and **T5**. This is part of the **EMBRACE Portfolio on Risks**. <br><br>
-
-        **Overview**:  
-        This application employs advanced AI techniques like Retrieval-Augmented Generation (RAG) on [EMM](https://emm.newsbrief.eu/) news. It extracts relevant media content on disaster events recorded in [EM-DAT](https://www.emdat.be/), including floods, wildfires, droughts, epidemics, and disease outbreaks. <br><br>
-
-        **How It Works**:  
-        For each selected event (filterable by Disaster Type, Country, and Disaster Number), the app:
-        - Retrieves pertinent news chunks via the EMM RAG service.
-        - Uses multiple LLMs from the [GPT@JRC](https://gpt.jrc.ec.europa.eu/) portfolio to:
-            - Extract critical impact data (e.g., fatalities, affected populations).
-            - Transform unstructured news into coherent, structured storylines.
-            - Build causal knowledge graphs — *impact chains* — highlighting drivers, impacts, and interactions. <br><br>
-
-        **Explore Events**:  
-        Use the selectors below to explore events by **Disaster Type**, **Country**, and **Disaster Number (DisNo)**. <br>
-        Once an event is selected, the app will display the **causal impact-chain graph**, illustrating key factors and their interrelationships. <br>
-        Below the graph, you'll find the **AI-generated narrative**, presenting a structured storyline of the event based on relevant news coverage. <br><br>
-
-        **Outcome**:  
-        These outputs offer a deeper understanding of disaster dynamics, supporting practitioners, disaster managers, and policy-makers in identifying patterns, assessing risks, and enhancing preparedness and response strategies.
-        """
-    )
-
-        # Create dropdowns for Disaster Type, Country, and Disaster Event #
-        disaster_type_dropdown = gr.Dropdown(
-            choices=[''] + df['Disaster Type'].unique().tolist(),
-            label="Select Disaster Type"
-        )
-        country_dropdown = gr.Dropdown(
-            choices=[''],  # Initially empty; will be populated based on disaster type
-            label="Select Country"
-        )
-        row_dropdown = gr.Dropdown(
-            choices=[],
-            label="Select Disaster Event #",
-            interactive=True
-        )
-
-        with gr.Column():
-            disaster_type_dropdown
-            country_dropdown
-            row_dropdown
-
-            gr.Markdown("### AI-Generated Storyline:")  # Title
-            outputs = [
-                gr.Textbox(label="Storyline", interactive=False, lines=10),
-                gr.HTML(label="Original Causal Graph"),  # Change from gr.Plot to gr.HTML
-                gr.HTML(label="Location Map"),   # Add HTML output for Folium map
-                gr.HTML(label="Updated Causal Graph")  # New HTML component for the updated graph
-            ]
-
-            # New Radio button for user feedback
-            feedback_radio = gr.Radio(
-                choices=[
-                    "Fully Correct",
-                    "Mostly Correct",
-                    "Partially Incorrect",
-                    "Incorrect",
-                    "Difficult to Judge"
-                ],
-                label="According to your expert knowledge, evaluate the graph based on the following descriptions:",
-                interactive=True
-            )
-
-            # Add descriptions for each option
-            gr.Markdown("""
-            - **Fully Correct**: All relevant nodes are accurately identified. The relationships among these nodes, including their directions (causes or prevents), are correct.
-            - **Mostly Correct**: The majority of relevant nodes are identified. However, some relationships may be missing or incorrect, such as a missing link or an inaccurate parent-child relationship. Despite these minor issues, the overall structure is largely accurate.
-            - **Partially Incorrect**: Some important nodes are missing and there are errors in the directions of relationships. However, the graph still captures some of the main characteristics of the intended relationships.
-            - **Incorrect**: The majority of links are incorrect, and many relevant nodes are either missing or inaccurately represented.
-            - **Difficult to Judge**: The graph is ambiguous or lacks sufficient context for accurate assessment.
-            """)
-
-            # New section for generating new scenarios
-            gr.Markdown("### Generate New Scenarios")  # Subtitle for the new section
-            new_node_input = gr.Textbox(
-                label="Enter a new variable or factor that might interact with the current graph to generate a plausible scenario:",
-                placeholder="e.g., tornado",
-                interactive=True
-            )
-
-            # Button to save the data
-            save_button = gr.Button("Save Your Answer")
-
-            # Feedback message
-            feedback_message = gr.Markdown("", visible=True)
-
-            # Button to process new node
-            process_button = gr.Button("Add New Node")
-
-        # Update country choices based on selected disaster type
-        disaster_type_dropdown.change(
-            fn=lambda disaster_type: gr.update(
-                choices=[''] + sorted(df[df['Disaster Type'] == disaster_type]['Country'].unique().tolist()),
-                value=''
-            ),
-            inputs=disaster_type_dropdown,
-            outputs=country_dropdown
-        )
-
-        # Update DisNo. choices based on selected disaster type and country
-        country_dropdown.change(
-            fn=update_row_dropdown,
-            inputs=[disaster_type_dropdown, country_dropdown],
-            outputs=row_dropdown
-        )
-
-        # Display information based on selected DisNo.
-        row_dropdown.change(
-            fn=display_info,
-            inputs=[row_dropdown, country_dropdown],
-            outputs=outputs[:3]  # Do not overwrite the updated graph slot
-        )
-
-        # Handle saving data on button click
-        save_button.click(
-            fn=save_data,
-            inputs=[row_dropdown, feedback_radio],
-            outputs=[feedback_message],
-            #api_name="Save Answer"
-        )
-
-        # Handle processing of the new node
-        process_button.click(
-            fn=process_new_node,
-            inputs=[row_dropdown, new_node_input],
-            outputs=[outputs[3]]  # Update only the updated graph output
-        )
-
-    return interface
-
-app = build_interface()
-app.launch()

requirements.txt

@@ -3,13 +3,4 @@ pandas
 pyvis
 matplotlib
 networkx
-openai
-dropbox 
-geopandas
-shapely
-osmnx
-folium
-torch
-transformers
-inflect
-sentencepiece
+openai