import os import sys import yaml import pandas as pd import numpy as np import joblib import gradio as gr import plotly.express as px import plotly.graph_objects as go from datetime import datetime # Add scripts folder to python path to import feature extractor sys.path.append(os.path.dirname(os.path.abspath(__file__))) from build_features_v2 import extract_sota_features, STYLOMETRIC_COLS_V2 from text_generator import PARTIES from models_v2 import SOTAHybridDetector from camembert_encoder import CamemBERTEncoder def load_config(config_path="configs/config.yaml"): with open(config_path, "r", encoding="utf-8") as f: return yaml.safe_load(f) # Load pipeline config config = load_config() output_dir = config["paths"]["output_dir"] models_dir = config["paths"]["models_dir"] # Load predictions preds_path = os.path.join(output_dir, "recent_debates_predictions_v2.csv") if not os.path.exists(preds_path): print(f"Predictions v2 not found at {preds_path}. Trying fallback...") preds_path = os.path.join(output_dir, "recent_debates_predictions.csv") if not os.path.exists(preds_path): df_preds = pd.DataFrame(columns=["date", "speaker", "party", "prob_ai", "prediction", "text", "document_type", "confidence_score"]) else: df_preds = pd.read_csv(preds_path) df_preds["date"] = pd.to_datetime(df_preds["date"]) # Map raw feature names to clean French descriptions COLS_MAP_V2 = { 'num_chars': "Nombre de caractères", 'num_words': "Nombre de mots", 'num_sentences': "Nombre de phrases", 'avg_sentence_len': "Longueur moyenne des phrases", 'std_sentence_len': "Écart-type de la longueur des phrases", 'slv_normalized': "Complexité lexicale normalisée (SLV)", 'avg_word_len': "Longueur moyenne des mots", 'ratio_long_words': "Ratio de mots longs (>6 caractères)", 'vocabulary_diversity': "Diversité lexicale (TTR)", 'hapax_ratio': "Ratio d'Hapax (mots uniques)", 'yules_k': "Richesse lexicale (Yule's K)", 'maas_index': "Indice Maas", 'information_entropy': "Entropie de l'information", 'brunet_w': "Indice de Brunet W", 'ratio_punctuation': "Ratio de ponctuation", 'freq_uppercase': "Fréquence des majuscules", 'freq_digits': "Fréquence des chiffres", 'connector_ratio': "Ratio de connecteurs logiques", 'connector_diversity': "Diversité des connecteurs", 'repetition_ratio': "Ratio de répétitions lexicales", 'stopword_ratio': "Ratio de mots vides (stopwords)", 'mean_polarity_diff': "Polarité moyenne (positif/négatif)", 'syntactic_complexity_score': "Complexité syntaxique (subordonnées)", 'ratio_interrogative': "Ratio de phrases interrogatives", 'ratio_exclamative': "Ratio de phrases exclamatives", 'ratio_declarative': "Ratio de phrases déclaratives", 'imparfait_ratio': "Ratio de verbes à l'imparfait", 'futur_ratio': "Ratio de verbes au futur", 'conditional_ratio': "Ratio de verbes au conditionnel", 'passive_voice_ratio': "Ratio de tournures passives" } # Load model package model_pkg_path = os.path.join(models_dir, "best_detector_v2.pkl") if os.path.exists(model_pkg_path): print(f"Loading SOTA v2 model from {model_pkg_path}...") pkg = joblib.load(model_pkg_path) detector = pkg["model"] model_name = pkg["model_name"] xgb_raw = pkg["xgb_raw"] scalers = pkg["scalers"] stylometric_cols = pkg["stylometric_cols"] friendly_feature_names = [COLS_MAP_V2.get(col, col) for col in stylometric_cols] # Load CamemBERT Encoder encoder = CamemBERTEncoder() else: pkg = None detector = None xgb_raw = None encoder = None friendly_feature_names = [] print("Model package best_detector_v2.pkl not found. Run train_on_lucie_historical.py first.") def detect_live_text(input_text): """Predicts if the pasted text is AI-generated and returns metrics & explanation.""" if pkg is None or xgb_raw is None or encoder is None: return "Erreur : Modèle SOTA v2 ou encodeur CamemBERT non chargé.", None, None, None if not input_text or len(input_text.strip()) < 10: return "Veuillez entrer un texte plus long (au moins 10 caractères).", 0.0, 0.0, [] text_cleaned = " ".join(input_text.replace("’", "'").replace("œ", "oe").split()) # 1. Extract Stylometrics connecteurs = config.get("features", {}).get("connecteurs", [ "en effet", "par conséquent", "en outre", "néanmoins", "toutefois", "cependant" ]) sty_dict = extract_sota_features(text_cleaned, connecteurs) df_sty = pd.DataFrame([sty_dict]) X_sty = df_sty[stylometric_cols].values # 2. Extract CamemBERT embedding X_emb = encoder.encode_single(text_cleaned).reshape(1, -1) # 3. Scale and Combine X_sty_scaled = scalers["sty"].transform(X_sty) X_emb_scaled = scalers["emb"].transform(X_emb) X_combined = np.hstack([X_sty_scaled, X_emb_scaled]) # 4. Predict prob_ai = float(xgb_raw.predict_proba(X_combined)[0][1]) confidence = 2.0 * abs(prob_ai - 0.5) prediction = 1 if prob_ai >= 0.5 else 0 verdict = "🤖 TEXTE SUSPECTÉ GÉNÉRÉ PAR IA" if prediction == 1 else "✍️ TEXTE SUSPECTÉ HUMAIN" # 5. Explanations using SHAP import shap explainer = shap.TreeExplainer(xgb_raw) shap_values = explainer.shap_values(X_combined) # Restrict SHAP analysis to stylometrics for human readability sv_sty = shap_values[0][:len(stylometric_cols)] explanation_list = [] feat_contrib = list(zip(friendly_feature_names, sv_sty)) # Sort by absolute SHAP value to find most influential features feat_contrib_sorted = sorted(feat_contrib, key=lambda x: abs(x[1]), reverse=True) for name, score in feat_contrib_sorted[:10]: direction = "⚠️ Indique IA" if score > 0 else "🍀 Indique Humain" explanation_list.append({"Caractéristique": name, "Impact": f"{direction} ({score:+.3f})"}) explanation_df = pd.DataFrame(explanation_list) if explanation_list else pd.DataFrame(columns=["Caractéristique", "Impact"]) return verdict, prob_ai, confidence, explanation_df def make_interactive_chart(party_filter, doc_filter): if df_preds.empty: return go.Figure() df_filtered = df_preds.copy() if party_filter != "Tous": df_filtered = df_filtered[df_filtered["party"] == party_filter] if doc_filter != "Tous": df_filtered = df_filtered[df_filtered["document_type"] == doc_filter] df_filtered["prob_ai_plot"] = df_filtered["prob_ai"] + 1e-4 df_filtered["snippet"] = df_filtered["text"].apply(lambda t: (t[:150] + "...") if isinstance(t, str) and len(t) > 150 else str(t)) df_filtered["type_predit"] = df_filtered["prediction"].map({0: "Humain", 1: "IA"}) df_filtered["date_str"] = df_filtered["date"].dt.strftime("%Y-%m-%d") fig = px.scatter( df_filtered, x="date", y="prob_ai_plot", color="party", size="confidence_score", opacity=0.6, hover_data={ "date_str": True, "speaker": True, "party": True, "document_type": True, "prob_ai": ":.4f", "confidence_score": ":.4f", "type_predit": True, "snippet": True, "date": False, "prob_ai_plot": False }, labels={ "date": "Date de l'intervention", "prob_ai_plot": "Score de suspicion (Échelle Log)", "party": "Groupe politique", "confidence_score": "Confiance" }, title="Explorateur Temporel des Discours (2004-2026)" ) fig.update_layout( template="plotly_white", yaxis=dict( type="log", tickvals=[1e-4, 1e-3, 1e-2, 1e-1, 1.0], ticktext=["0.0001 (Humain)", "0.001", "0.01", "0.1", "1.0 (IA)"], title="Score de suspicion d'IA (Échelle Log)" ), xaxis=dict(title="Date de l'intervention"), legend_title_text="Groupe politique" ) return fig def make_weekly_chart(): if df_preds.empty: return go.Figure() df_weekly = df_preds.copy() df_weekly["week_start"] = df_weekly["date"] - pd.to_timedelta(df_weekly["date"].dt.weekday, unit='D') weekly_avg = df_weekly.groupby("week_start")["prob_ai"].agg(["mean", "count"]).reset_index() weekly_avg.columns = ["week_start", "prob_ai_mean", "speech_count"] weekly_avg["prob_ai_plot"] = weekly_avg["prob_ai_mean"] + 1e-4 fig = go.Figure() fig.add_trace(go.Scatter( x=weekly_avg["week_start"], y=weekly_avg["prob_ai_plot"], mode="lines+markers", name="Moyenne hebdomadaire", line=dict(color="#3f51b5", width=2), marker=dict(size=weekly_avg["speech_count"]/2 + 3, color="#3f51b5", opacity=0.8), hovertemplate="Semaine: %{x}
Score de suspicion moyen: %{y:.4f}
Nombre de discours: %{marker.size}" )) fig.add_shape( type="line", x0="2022-11-30", y0=1e-4, x1="2022-11-30", y1=1.0, line=dict(color="#e91e63", width=1.5, dash="dash"), ) fig.add_annotation( x="2022-11-30", y=0.5, text="Sortie de ChatGPT (Fin 2022)", showarrow=True, arrowhead=1, ax=-100, ay=-30, arrowcolor="#e91e63", font=dict(color="#e91e63") ) fig.update_layout( template="plotly_white", yaxis=dict( type="log", tickvals=[1e-4, 1e-3, 1e-2, 1e-1, 1.0], ticktext=["0.0001 (Humain)", "0.001", "0.01", "0.1", "1.0 (IA)"], title="Score de suspicion moyen (Log)" ), xaxis=dict(title="Semaine"), title="Tendance hebdomadaire du score de suspicion d'IA (2004-2026)" ) return fig def get_party_leaderboard(): if df_preds.empty: return pd.DataFrame(), go.Figure() df_post = df_preds[df_preds["date"] >= "2023-01-01"].copy() stats = df_post.groupby("party").agg( speech_count=("prob_ai", "count"), mean_suspicion=("prob_ai", "mean"), ai_ratio=("prediction", "mean") ).reset_index().sort_values(by="mean_suspicion", ascending=False) stats_show = stats.copy() stats_show["mean_suspicion"] = stats_show["mean_suspicion"].round(4) stats_show["ai_ratio"] = (stats_show["ai_ratio"] * 100).round(2).astype(str) + "%" stats_show.columns = ["Groupe Politique", "Nombre de discours", "Score suspicion moyen", "Proportion rédigée par IA"] fig = px.bar( stats, x="mean_suspicion", y="party", color="mean_suspicion", orientation="h", color_continuous_scale="Viridis", labels={"mean_suspicion": "Suspicion moyenne", "party": "Groupe politique"}, title="Score moyen de suspicion par groupe politique" ) fig.update_layout(showlegend=False, coloraxis_showscale=False) return stats_show, fig def get_speaker_leaderboard(): if df_preds.empty: return pd.DataFrame(), go.Figure() df_post = df_preds[df_preds["date"] >= "2023-01-01"].copy() stats = df_post.groupby("speaker").agg( speech_count=("prob_ai", "count"), mean_suspicion=("prob_ai", "mean"), ai_ratio=("prediction", "mean") ).reset_index().sort_values(by="mean_suspicion", ascending=False).head(10) stats_show = stats.copy() stats_show["mean_suspicion"] = stats_show["mean_suspicion"].round(4) stats_show["ai_ratio"] = (stats_show["ai_ratio"] * 100).round(2).astype(str) + "%" stats_show.columns = ["Député / Orateur", "Nombre de discours", "Score suspicion moyen", "Proportion rédigée par IA"] fig = px.bar( stats, x="mean_suspicion", y="speaker", color="mean_suspicion", orientation="h", color_continuous_scale="Plasma", labels={"mean_suspicion": "Suspicion moyenne", "speaker": "Orateur"}, title="Top 10 des orateurs les plus suspects" ) fig.update_layout(showlegend=False, coloraxis_showscale=False) return stats_show, fig def search_records(query_speaker, query_party): if df_preds.empty: return pd.DataFrame() df_filtered = df_preds.copy() if query_speaker: df_filtered = df_filtered[df_filtered["speaker"].str.contains(query_speaker, case=False, na=False)] if query_party != "Tous": df_filtered = df_filtered[df_filtered["party"] == query_party] df_show = df_filtered[["date", "speaker", "party", "document_type", "prob_ai", "prediction", "text"]].copy() df_show["date"] = df_show["date"].dt.strftime("%Y-%m-%d") df_show["prediction"] = df_show["prediction"].map({0: "Humain", 1: "IA"}) df_show.columns = ["Date", "Député", "Groupe", "Type", "Score IA", "Classification", "Texte"] return df_show.head(100) # Setup Gradio Interface with gr.Blocks(title="Détecteur d'IA Parlementaire") as demo: gr.HTML("""

Détecteur de Textes Parlementaires Générés par IA

Analyse stylométrique et temporelle continue (2004-2026) sur données réelles d'Hugging Face

""") with gr.Tabs(): # TAB 1: Live Detector with gr.TabItem("🔍 Détecteur en Direct"): gr.Markdown("### Testez l'écriture d'un discours en collant son contenu ci-dessous :") with gr.Row(): with gr.Column(scale=2): input_box = gr.Textbox( label="Texte politique / Discours en français", placeholder="Saisissez ou collez l'intervention d'un député ici...", lines=12 ) btn_predict = gr.Button("Analyser le texte", variant="primary") with gr.Column(scale=1): output_verdict = gr.Textbox(label="Verdict de classification", interactive=False) output_score = gr.Label(label="Score de suspicion (Probabilité d'IA)") output_confidence = gr.Slider(label="Niveau de confiance du modèle", minimum=0, maximum=1, value=0, interactive=False) gr.Markdown("---") gr.Markdown("### 🔍 Pourquoi cette décision ? (Coefficients les plus influents)") output_explanation = gr.DataFrame(headers=["Caractéristique", "Impact"], datatype=["str", "str"], wrap=True) btn_predict.click( fn=detect_live_text, inputs=input_box, outputs=[output_verdict, output_score, output_confidence, output_explanation] ) # TAB 2: Temporal Trends with gr.TabItem("📈 Tendances Temporelles & Cartographie"): gr.Markdown("### Analyse continue hebdomadaire avec échelle logarithmique") with gr.Row(): with gr.Column(): party_filter = gr.Dropdown(choices=["Tous"] + PARTIES, value="Tous", label="Filtrer par Groupe Politique") with gr.Column(): doc_filter = gr.Dropdown(choices=["Tous", "intervention_seance", "prise_position", "explication_vote", "amendement", "reponse_debat", "discours_groupe"], value="Tous", label="Filtrer par Type de Document") with gr.Row(): with gr.Column(): chart_scatter = gr.Plot(label="Cartographie interactive des points (Tous les discours)") gr.Markdown("---") with gr.Row(): chart_line = gr.Plot(label="Tendance Moyenne Hebdomadaire (Log Scale)") party_filter.change(fn=make_interactive_chart, inputs=[party_filter, doc_filter], outputs=chart_scatter) doc_filter.change(fn=make_interactive_chart, inputs=[party_filter, doc_filter], outputs=chart_scatter) demo.load(fn=make_interactive_chart, inputs=[party_filter, doc_filter], outputs=chart_scatter) demo.load(fn=make_weekly_chart, inputs=None, outputs=chart_line) # TAB 3: Leaderboards with gr.TabItem("🏆 Classements (Post-2022)"): gr.Markdown("### 📊 Classements de suspicion d'utilisation de l'IA (Période 2023-2026)") with gr.Row(): with gr.Column(): gr.Markdown("#### 🏛️ Classement des Groupes Politiques") output_party_leaderboard = gr.DataFrame(datatype=["str", "int", "str", "str"]) chart_party_leaderboard = gr.Plot() with gr.Column(): gr.Markdown("#### 👤 Top 10 des Députés les plus suspects") output_speaker_leaderboard = gr.DataFrame(datatype=["str", "int", "str", "str"]) chart_speaker_leaderboard = gr.Plot() # Load stats demo.load(fn=get_party_leaderboard, inputs=None, outputs=[output_party_leaderboard, chart_party_leaderboard]) demo.load(fn=get_speaker_leaderboard, inputs=None, outputs=[output_speaker_leaderboard, chart_speaker_leaderboard]) # TAB 4: Data Explorer with gr.TabItem("🗄️ Explorateur des Discours"): gr.Markdown("### Parcourez les 3 000 interventions récentes scorées par le pipeline") with gr.Row(): with gr.Column(): search_speaker = gr.Textbox(placeholder="Rechercher par nom de député...", label="Orateur") with gr.Column(): search_party = gr.Dropdown(choices=["Tous"] + PARTIES, value="Tous", label="Groupe Politique") btn_search = gr.Button("Rechercher", variant="secondary") gr.Markdown("#### Résultats (Top 100 max) :") output_table = gr.DataFrame(wrap=True) btn_search.click( fn=search_records, inputs=[search_speaker, search_party], outputs=output_table ) demo.load(fn=search_records, inputs=[search_speaker, search_party], outputs=output_table) if __name__ == "__main__": print("Launching Gradio application in share mode...") demo.launch(server_name="0.0.0.0", server_port=7860, share=True, theme=gr.themes.Soft())