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70from flask import Flask, request, jsonify
import torch
import numpy as np
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "climatebert/distilroberta-base-climate-sentiment"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
model.eval()
app = Flask(__name__)
app.config["TEMPLATES_AUTO_RELOAD"] = True
@app.route("/", methods=["GET", "POST"])
def index():
if request.method == "POST":
file = request.files["file"]
text = file.read().decode("utf-8")
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
output = model(**inputs)
y_pred = np.argmax(output.logits.numpy(), axis=1).tolist()
response = {"Received Text": text, "Prediction": y_pred}
return jsonify(response)
return "Please submit a POST request with a file containing text."
if __name__ == "__main__":
app.run(host='0.0.0.0', port=5000)
from flask import Flask, request
import torch
import numpy as np
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import uvicorn
model_name = "climatebert/distilroberta-base-climate-sentiment"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
model.eval()
app = Flask(__name__)
app.config["TEMPLATES_AUTO_RELOAD"] = True
@app.route("/", methods=["GET", "POST"])
def index():
request.method == "POST"
file = request.files["file"]
text = file["text"]
inputs = tokenizer(text)
with torch.no_grad():
output = model(**inputs)
y_pred = np.argmax(output.logits.numpy(), axis=1).tolist()
response = {"Received Text": text, "Prediction": y_pred}
return response
if __name__ == "__main__":
app.run(host='0.0.0.0', port=5000)