Adding files

.gitattributes

@@ -1,35 +0,0 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text

main.py

@@ -1,7 +1,22 @@
 from fastapi import FastAPI
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse
+
+from transformers import pipeline
+
+from pydantic import BaseModel
 
 app = FastAPI()
+summarizer = pipeline("summarization", model="Falconsai/text_summarization")
+
+@app.get("/prediction")
+def pre(input):
+    output = summarizer(input)
+    print(output)
+    return {"output": output[0]}
+
+app.mount("/static", StaticFiles(directory="static"), name="static")
 
-@app.get('/hello')
-def hello():
-    return {"hello": "succesfully deployed"}
+@app.get('/')
+def index() -> FileResponse:
+    return FileResponse(path='./static/static.html', media_type="text/html")

model.py

@@ -0,0 +1,44 @@
+from transformers import pipeline
+from transformers import ( TokenClassificationPipeline, AutoModelForTokenClassification, AutoTokenizer)
+from transformers.pipelines import AggregationStrategy
+import numpy as np
+
+
+# ================================= summarize code =================================
+def summerize(text):
+    summarizer = pipeline("summarization", model="Falconsai/text_summarization")
+
+    text_len = len(text.split(' '))
+    max_length = int((text_len * 80)/100)
+    min_length = int((text_len * 30)/100)
+
+    return{"output": summarizer(text, max_length=max_length, min_length=min_length, do_sample=True)}
+
+
+# =================================s keywords code =================================
+
+
+# Define keyphrase extraction pipeline
+# class KeyphraseExtractionPipeline(TokenClassificationPipeline):
+#     def __init__(self, model, *args, **kwargs):
+#         super().__init__(
+#             model=AutoModelForTokenClassification.from_pretrained(model),
+#             tokenizer=AutoTokenizer.from_pretrained(model),
+#             *args,
+#             **kwargs
+#         )
+
+#     def postprocess(self, all_outputs):
+#         results = super().postprocess(
+#             all_outputs=all_outputs,
+#             aggregatsion_strategy=AggregationStrategy.FIRST,
+#         )
+#         return np.unique([result.get("word").strip() for result in results])
+
+# # Load pipeline
+# model_name = "ml6team/keyphrase-extraction-distilbert-inspec"
+# extractor = KeyphraseExtractionPipeline(model=model_name)
+
+# keyphrases = extractor(text)
+
+# print(keyphrases)

static/script.js

@@ -0,0 +1,22 @@
+// takes a text input and hit the endpoint with the text, then return the output
+const textGenForm = document.querySelector('form');
+const translateText = async (text) => {
+    const inferResponse = await fetch(`prediction?input=${text}`);
+    const inferJson = await inferResponse.json();
+
+    return inferJson.output;
+};
+
+
+textGenForm.addEventListener('submit', async (event) => {
+  event.preventDefault();
+
+  const textGenInput = document.getElementById('text-gen-input');
+  const textGenParagraph = document.querySelector('.text-gen-output');
+
+  try {
+    textGenParagraph.textContent = await translateText(textGenInput.value);
+  } catch (err) {
+    console.error(err);
+  }
+});

static/static.html

@@ -0,0 +1,35 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+  <meta charset="UTF-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Buddy</title>
+  <link rel="stylesheet" href="../static/style.css" />
+  <script type="module" src="../static/script.js"></script>
+</head>
+
+<body>
+  <main>
+    <section id="text-gen">
+      <h1>Learning becomes easy</h1>
+      <form>
+        <p>Input your paragraph here</p>
+        <label for="text-gen-input"></label>
+        <input id="text-gen-input" type="text" placeholder="Enter your parargraph here" />
+        <button id="text-gen-submit">Submit</button>
+      </form>
+    </section>
+    <section>
+      <p class="text-gen-output"></p>
+      <ul>
+        <li><span>Foo</span></li>
+        <li><span>Bar</span></li>
+        <li><span>Bat</span></li>
+      </ul>
+    </section>
+  </main>
+
+</body>
+
+</html>

static/style.css

@@ -0,0 +1,17 @@
+h1{
+    text-align: center;
+}
+form{
+    margin: 2rem 5rem;
+}
+input{
+    margin: 0 auto;
+    width:100%;
+}
+ul li {
+  color: red;
+}
+
+ul li span {
+  color: blue;
+}