Add application file

app.py

@@ -0,0 +1,93 @@
+import gradio as gr
+import pandas as pd
+from utils import summarize_dataframe, validate_columns
+from llm_adapter import llm_generate
+from planner import plan_from_llm
+from viz import plot_bar, plot_line, plot_scatter, plot_hist
+
+def load_file(file):
+    if file is None:
+        return None, "Aucun fichier.", "", None, None
+    df = pd.read_csv(file.name)
+    preview = df.head(10)
+    summary = summarize_dataframe(df)
+    cols = list(df.columns)
+    return df, "✅ Dataset chargé", summary, preview, gr.update(choices=cols)
+
+def ask_llm(summary, question):
+    if not summary or not question:
+        return "Charge un dataset et pose une question."
+    prompt = f"""Voici un résumé de données suivi d'une question.
+Réponds en puces (6-10) + mini conclusion actionnable.
+
+[DATA SUMMARY]
+{summary}
+
+[QUESTION]
+{question}
+"""
+    return llm_generate(prompt)
+
+def ask_to_chart(df, summary, question, bins):
+    if df is None:
+        return None, "Charge un dataset."
+    plan = plan_from_llm(summary, question, list(df.columns))
+    # Validation & défauts
+    x = plan.get("x"); y = plan.get("y"); chart = plan.get("chart", "bar"); agg = plan.get("agg", "count")
+    valid = validate_columns(df, [x, y])
+    x_ok = valid[0] if len(valid) > 0 else None
+    y_ok = valid[1] if len(valid) > 1 else None
+    if x_ok is None:
+        return None, f"Colonne X invalide. Colonnes disponibles: {list(df.columns)}"
+    # Routing
+    fig = None
+    if chart == "bar":
+        fig = plot_bar(df, x_ok, y_ok, agg)
+    elif chart == "line":
+        if y_ok is None: return None, "Pour une courbe, X et Y sont requis."
+        fig = plot_line(df, x_ok, y_ok, agg)
+    elif chart == "scatter":
+        if y_ok is None: return None, "Pour un scatter, X et Y sont requis."
+        fig = plot_scatter(df, x_ok, y_ok)
+    elif chart == "hist":
+        fig = plot_hist(df, x_ok, bins or 20)
+    else:
+        return None, f"Type de graphique non supporté: {chart}"
+    return fig, f"OK → plan: {plan}"
+
+with gr.Blocks(title="Assistant analytique LLM + Gradio") as demo:
+    gr.Markdown("## De la donnée brute aux insights visuels — LLM + Gradio")
+
+    with gr.Row():
+        file = gr.File(label="Uploader un CSV")
+        load_btn = gr.Button("Charger")
+
+    df_state = gr.State(None)
+    status = gr.Markdown()
+    summary_box = gr.Textbox(label="Résumé (schéma + stats)", lines=12)
+    preview = gr.Dataframe(label="Aperçu (10 premières lignes)", interactive=False)
+    cols_dropdown = gr.Dropdown(choices=[], visible=False)  # juste pour transporter les colonnes
+
+    def _load(file):
+        df, stat, summary, prev, cols = load_file(file)
+        return df, stat, summary, prev, cols
+
+    load_btn.click(_load, [file], [df_state, status, summary_box, preview, cols_dropdown])
+
+    gr.Markdown("---")
+    gr.Markdown("### 🧠 Questions à l'IA")
+    question = gr.Textbox(label="Pose une question (FR/EN)")
+    answer = gr.Markdown()
+    ask_btn = gr.Button("Demander à l'IA")
+    ask_btn.click(ask_llm, [summary_box, question], [answer])
+
+    gr.Markdown("---")
+    gr.Markdown("### 📊 Ask → Chart (par LLM)")
+    bins = gr.Slider(5, 100, value=20, step=1, label="Bins (hist)")
+    plot = gr.Plot(label="Graphique")
+    plot_msg = gr.Markdown()
+    chart_btn = gr.Button("Proposer et tracer le graphique")
+    chart_btn.click(ask_to_chart, [df_state, summary_box, question, bins], [plot, plot_msg])
+
+if __name__ == "__main__":
+    demo.launch()