Upload Deploy_AskLAQ3.py

deploy/Deploy_AskLAQ3.py

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+import os, torch, pandas as pd, gradio as gr, uvicorn, nest_asyncio
+from flask import Flask, render_template, request, jsonify
+from sentence_transformers import SentenceTransformer, util
+from fastapi import FastAPI
+from fastapi.middleware.wsgi import WSGIMiddleware
+
+# Configuration
+BASE_DIR = os.path.abspath(os.path.dirname(__file__))
+flask_app = Flask(__name__, 
+                  template_folder=os.path.join(BASE_DIR, "templates"),
+                  static_folder=os.path.join(BASE_DIR, "static"))
+
+# Chargement IA
+model = SentenceTransformer("OrdalieTech/Solon-embeddings-mini-beta-1.1", device="cpu", trust_remote_code=True)
+
+@flask_app.route("/")
+def index():
+    return render_template("index.html")
+
+@flask_app.route("/ask", methods=["POST"])
+def ask():
+    try:
+        data = request.get_json()
+        question = data.get("question", "")
+        
+        df = pd.read_csv("dataset_2026.csv")
+        emb_base = torch.load("embeddings_questions.pt", map_location="cpu")
+        
+        emb_q = model.encode(question, convert_to_tensor=True, normalize_embeddings=True)
+        scores = util.pytorch_cos_sim(emb_q, emb_base)[0]
+        idx = torch.argmax(scores).item()
+        
+        return jsonify({
+            "response": df["rationale"].iloc[idx],
+            "confidence": int(scores[idx].item() * 100)
+        })
+    except Exception as e:
+        return jsonify({"error": str(e)})
+
+# Montage FastAPI (pour Hugging Face)
+app = FastAPI()
+app.mount("/", WSGIMiddleware(flask_app))
+
+if __name__ == "__main__":
+    nest_asyncio.apply()
+    uvicorn.run(app, host="0.0.0.0", port=7860)