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