Create app.py

app.py

@@ -0,0 +1,38 @@
+import torch
+import gradio
+from transformers import pipeline
+from bs4 import BeautifulSoup
+import requests
+
+def SUMMARIZE(Url):
+    summarizer = pipeline("summarization", model="stevhliu/my_awesome_billsum_model")
+    r = requests.get(Url)
+    soup = BeautifulSoup(r.text, 'html.parser')
+    results = soup.find_all(['hl', 'p'])
+    text = [result.text for result in results]
+    Article = ''.join(text)
+    sentences = Article.split(' ')
+    current_chunk = 0
+    chunks = []
+    for sentence in sentences:
+        if len(chunks) == current_chunk + 1:
+            if len(chunks[current_chunk]) + len(sentence.split(' ')) <= max_chunk:
+                chunks[current_chunk].extend(sentence.split(' '))
+            else:
+                current_chunk += 1
+                chunks.append(sentence.split(' '))
+        else:
+            #print(current_chunk)
+            chunks.append(sentence.split(' '))
+
+    for chunk_id in range(len(chunks)):
+        chunks[chunk_id] = ' '.join(chunks[chunk_id])
+    res = summarizer(chunks, max_length=120, min_length=30, do_sample=False)
+    for i in range(len(res)):
+        return res[i].values()
+        
+
+interface = gradio.Interface(fn=SUMMARIZE,
+                             inputs=gradio.TextArea(lines=2, value="https://medium.com/analytics-vidhya/openai-gpt-3-language-models-are-few-shot-learners-82531b3d3122"),
+                             outputs=gradio.TextArea())
+interface.launch(share=True)