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import os |
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import requests |
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import json |
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from io import BytesIO |
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from flask import Flask, jsonify, render_template, request, send_file |
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from modules.inference import infer_t5 |
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from modules.dataset import query_emotion |
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API_TOKEN = os.getenv("BIG_GAN_TOKEN") |
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app = Flask(__name__) |
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@app.route("/") |
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def index(): |
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return render_template("index.html") |
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@app.route("/infer_biggan") |
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def biggan(): |
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input = request.args.get("input") |
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output = requests.request( |
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"POST", |
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"https://api-inference.huggingface.co/models/osanseviero/BigGAN-deep-128", |
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headers={"Authorization": f"Bearer {API_TOKEN}"}, |
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data=json.dumps(input), |
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) |
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return send_file(BytesIO(output.content), mimetype="image/png") |
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@app.route("/infer_t5") |
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def t5(): |
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input = request.args.get("input") |
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output = infer_t5(input) |
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return jsonify({"output": output}) |
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@app.route("/query_emotion") |
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def emotion(): |
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start = request.args.get("start") |
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end = request.args.get("end") |
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print(start) |
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print(end) |
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output = query_emotion(int(start), int(end)) |
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return jsonify({"output": output}) |
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if __name__ == "__main__": |
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app.run(host="0.0.0.0", port=7860) |
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