change

app.py

@@ -1,94 +1,88 @@
 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
+import numpy as np
+import random
+import time
+from openai import OpenAI
+from dotenv import load_dotenv
+import logging
+import os
+import requests
 
-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)
+logger = logging.getLogger(__name__)
+load_dotenv()
+GEMINI_API_KEY=os.getenv("GEMINI_API_KEY")
+GEMINI_URL=os.getenv("GEMINI_URL")
 
-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.
+# client = OpenAI(api_key=XAI_API_KEY, base_url=XAI_BASE)
 
-[DATA SUMMARY]
-{summary}
+def call_grok(user_msg:str,history:list[tuple[str, str]]):
+    # if not XAI_API_KEY:
+    #     raise gr.Error("Please set the XAI_API_KEY environment variable.")
+    # messages=[{"role":"system","content":"Tu es u n assistant intelligent"}]
+    # for user, assistant in( history or []):
+    #     if user:
+    #         messages.append({"role":"user", "content":user})
+    #     if assistant:
+    #         messages.append({"role":"assistant", "content":assistant})
+    #     messages.append({"role":"assistant","content":user_msg})
 
-[QUESTION]
-{question}
-"""
-    return llm_generate(prompt)
+    # try:
+    #     response = client.chat.completions.create(
+    #         model=XAI_MODEL,
+    #         messages=messages,
+    #         temperature=0,
+    #         max_tokens=1024,
+    #         top_p=1,
+    #         frequency_penalty=0,
+    #         presence_penalty=0,
+    #         stop=None,
+    #     )
+    #     return response.choices[0].message.content.strip()
+    # except Exception as e:
+    #     logger.error(f"Error calling Grok: {e}")
+    if not GEMINI_API_KEY:
+        raise gr.Error("Please set the GEMINI_API_KEY environment variable.")
 
-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
+    
+    contents=[]
+    SYSTEM_PROMPT = "Tu es un assistant utile, concis et amical. Réponds en français."
+    contents = []
+    for u, a in (history or []):
+        if u:
+            contents.append({"role": "user",  "parts": [{"text": u}]})
+        if a:
+            contents.append({"role": "model", "parts": [{"text": a}]})
+    contents.append({"role": "user", "parts": [{"text": user_msg}]})
+    url =f"{GEMINI_URL}?key={GEMINI_API_KEY}"
+    headers = {"Content-Type": "application/json"}  
+    payload = {
+        # 👉 Le SEUL et unique message system est passé ici :
+        "systemInstruction": {
+            "role": "system",
+            "parts": [{"text": SYSTEM_PROMPT}]   # une seule part !
+        },
+        "contents": contents
+    }
+    try:
+        resp=requests.post(url, json=payload, headers=headers)
+    except requests.RequestException as e:
+        logger.error(f"Error calling Gemini: {e}")
 
-    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])
+    if resp.status_code == 200:
+        response = resp.json()
+        logger.info(f"Response from Gemini: {response}")
+        return response["candidates"][0]["content"]["parts"][0]["text"].strip()
+    else:
+        logger.error(f"Error calling Gemini: {resp.status_code} {resp.text}")
+def chat_fn(message,history):
+    response = call_grok(message, history or [])
+    return response
 
-    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])
 
+with gr.Blocks() as demo:
+    gr.ChatInterface(chat_fn, title="MasterClass LLM + Gradio")
 if __name__ == "__main__":
     demo.launch()
-  
+
+