Setup server

.Dockerfile

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+RUN git clone https://huggingface.co/GuysTrans/bart-base-finetuned-xsum
+
+COPY . .
+
+CMD ["FLASK_APP=server.py", "flask", "run", "--host", "0.0.0.0", "--port", "7860"]

chat.py

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+from huggingface_hub.hf_api import HfFolder
+from transformers import AutoModelForSeq2SeqLM
+from transformers import AutoTokenizer
+
+HfFolder.save_token('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')
+
+model_checkpoint = "facebook/bart-base"
+model_name = model_checkpoint.split("/")[-1]
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+
+model = AutoModelForSeq2SeqLM.from_pretrained(f"{model_name}-finetuned-xsum")
+
+
+def generate_summary(question, model):
+    inputs = tokenizer(
+        question,
+        padding="max_length",
+        truncation=True,
+        max_length=512,
+        return_tensors="pt",
+    )
+    input_ids = inputs.input_ids.to(model.device)
+    attention_mask = inputs.attention_mask.to(model.device)
+    outputs = model.generate(input_ids, attention_mask=attention_mask, max_new_tokens=512)
+    output_str = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+    return outputs, output_str
+
+
+summaries_before_tuning = generate_summary(
+    "Hi I'm XXXXXXX XXXXXXX I was told by a doctor I have either pneumonia or nodularity within the right lung upper lobe if idon't respond to antibiotics.Is that poosible and can you pneumni?Penelope or I have a mass and it's probably cancer",
+    model)[1]
+print(summaries_before_tuning)

id_rsa

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+-----BEGIN OPENSSH PRIVATE KEY-----
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+-----END OPENSSH PRIVATE KEY-----

id_rsa.pub

@@ -0,0 +1 @@
+ssh-rsa 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 root@84fe2a873bda

requirements.txt

@@ -0,0 +1,3 @@
+transformers
+flask
+torch

server.py

@@ -0,0 +1,107 @@
+from flask import Flask, request
+import requests
+import os
+from huggingface_hub.hf_api import HfFolder
+from transformers import AutoModelForSeq2SeqLM
+from transformers import AutoTokenizer
+
+HfFolder.save_token('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')
+
+model_checkpoint = "facebook/bart-base"
+model_name = model_checkpoint.split("/")[-1]
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+
+model = AutoModelForSeq2SeqLM.from_pretrained(f"{model_name}-finetuned-xsum")
+
+
+def generate_summary(question, model):
+    inputs = tokenizer(
+        question,
+        padding="max_length",
+        truncation=True,
+        max_length=512,
+        return_tensors="pt",
+    )
+    input_ids = inputs.input_ids.to(model.device)
+    attention_mask = inputs.attention_mask.to(model.device)
+    outputs = model.generate(input_ids, attention_mask=attention_mask, max_new_tokens=512)
+    output_str = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+    return outputs, output_str
+
+
+app = Flask(__name__)
+
+FB_API_URL = 'https://graph.facebook.com/v2.6/me/messages'
+VERIFY_TOKEN = '5rApTs/BRm6jtiwApOpIdjBHe73ifm6mNGZOsYkwwAw='
+PAGE_ACCESS_TOKEN = os.environ['PAGE_ACCESS_TOKEN']  # paste your page access token here>"
+
+
+def get_bot_response(message):
+    return generate_summary(message, model)[1][0]
+
+
+def verify_webhook(req):
+    if req.args.get("hub.verify_token") == VERIFY_TOKEN:
+        return req.args.get("hub.challenge")
+    else:
+        return "incorrect"
+
+
+def respond(sender, message):
+    """Formulate a response to the user and
+    pass it on to a function that sends it."""
+    response = get_bot_response(message)
+    send_message(sender, response)
+    return response
+
+
+def is_user_message(message):
+    """Check if the message is a message from the user"""
+    return (message.get('message') and
+            message['message'].get('text') and
+            not message['message'].get("is_echo"))
+
+
+@app.route("/webhook", methods=['GET', 'POST'])
+def listen():
+    """This is the main function flask uses to
+    listen at the `/webhook` endpoint"""
+    if request.method == 'GET':
+        return verify_webhook(request)
+
+    if request.method == 'POST':
+        payload = request.json
+        event = payload['entry'][0]['messaging']
+        res = "ok"
+        for x in event:
+            if is_user_message(x):
+                text = x['message']['text']
+                sender_id = x['sender']['id']
+                res = respond(sender_id, text)
+
+        return res
+
+
+def send_message(recipient_id, text):
+    """Send a response to Facebook"""
+    payload = {
+        'message': {
+            'text': text
+        },
+        'recipient': {
+            'id': recipient_id
+        },
+        'notification_type': 'regular'
+    }
+
+    auth = {
+        'access_token': PAGE_ACCESS_TOKEN
+    }
+
+    response = requests.post(
+        FB_API_URL,
+        params=auth,
+        json=payload
+    )
+
+    return response.json()