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app.py

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+import torch
+import gradio as gr
+import numpy as np
+import pickle
+import pandas as pd
+from sentence_transformers import SentenceTransformer
+from model import VotePredictor  # <-- make sure this matches your model file
+
+# Load models
+main_model = VotePredictor(country_count=193)
+main_model.load_state_dict(torch.load("vote_predictor_epoch27.pt", map_location="cpu"))
+main_model.eval()
+
+problem_model = VotePredictor(country_count=193)
+problem_model.load_state_dict(torch.load("problem_country_model.pt", map_location="cpu"))
+problem_model.eval()
+
+# Load country encoder
+with open("country_encoder.pkl", "rb") as f:
+    country_encoder = pickle.load(f)
+
+# Vectorizer
+vectorizer = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+
+# Define problem countries (same as used during training)
+problem_countries = [ 
+    'SURINAME',
+    'TURKMENISTAN',
+    'MARSHALL ISLANDS',
+    'MYANMAR',
+    'GABON',
+    'CENTRAL AFRICAN REPUBLIC',
+    'ISRAEL',
+    'REPUBLIC OF THE CONGO',
+    'LIBERIA',
+    'SOMALIA',
+    'CANADA',
+    "LAO PEOPLE'S DEMOCRATIC REPUBLIC",
+    'TUVALU',
+    'DEMOCRATIC REPUBLIC OF THE CONGO',
+    'MONTENEGRO',
+    'VANUATU',
+    'UNITED STATES',
+    'TÜRKİYE',
+    'SEYCHELLES',
+    'SERBIA',
+    'CABO VERDE',
+    'VENEZUELA (BOLIVARIAN REPUBLIC OF)',
+    'KIRIBATI',
+    'IRAN (ISLAMIC REPUBLIC OF)',
+    'SOUTH SUDAN',
+    'ALBANIA',
+    'CZECHIA',
+    'DOMINICA',
+    'SAO TOME AND PRINCIPE',
+    'ESWATINI',
+    'CHAD',
+    'EQUATORIAL GUINEA',
+    'GAMBIA',
+    'LIBYA',
+    "CÔTE D'IVOIRE",
+    'SAINT CHRISTOPHER AND NEVIS',
+    'RWANDA',
+    'TONGA',
+    'NIGER',
+    'MICRONESIA (FEDERATED STATES OF)',
+    'SYRIAN ARAB REPUBLIC',
+    'NAURU',
+    'PALAU',
+    'NORTH MACEDONIA',
+    'NETHERLANDS',
+    'BOLIVIA (PLURINATIONAL STATE OF)'
+]
+
+# Vote function
+def predict_votes(resolution_text):
+    # Vectorize once
+    vec = vectorizer.encode([resolution_text])
+    x_tensor = torch.tensor(vec, dtype=torch.float32)
+
+    countries = []
+    votes = []
+
+    for country in country_encoder.classes_:
+        country_id = country_encoder.transform([country])[0]
+        c_tensor = torch.tensor([country_id], dtype=torch.long)
+
+        model = problem_model if country in problem_countries else main_model
+
+        with torch.no_grad():
+            logit = model(x_tensor, c_tensor).squeeze()
+            prob = torch.sigmoid(logit).item()
+            vote = "✅ Yes" if prob > 0.5 else "❌ Not Yes"
+
+        countries.append(country)
+        votes.append(vote)
+
+    df = pd.DataFrame({
+        "Country": countries,
+        "Vote": votes
+    }).sort_values("Country")
+
+    return df
+
+# Gradio UI
+iface = gr.Interface(
+    fn=predict_votes,
+    inputs=gr.Textbox(lines=15, label="Paste UN Resolution Text Here"),
+    outputs=gr.Dataframe(label="Predicted Votes by Country"),
+    title="UN Resolution Vote Predictor",
+    description="This model predicts how each UN country will vote on a given resolution based on the text. Uses BERT embeddings and two models: one for normal countries, one for chaos monkeys.",
+    live=False
+)
+
+iface.launch()

country_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8eeeddbf8b87e39f0f2c309a21f534578fb23460ca007f9ca5e050815bc7dbdc
+size 2707

problem_country_model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b66c5b005f79f542ebcbed11bdd91efe8f1fee0b9b4ab188a9cfac32470ebb5b
+size 436554

vote_predictor_epoch27.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:81077277124887c2d91b3f03affcc387e962ea75771ab1bfeb99a765da7f72ba
+size 455443