app.py

app.py

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+import requests
+import os
+import gradio as gr
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+headers = {
+    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.82 Safari/537.36'
+}
+
+def summarize (article_url):
+    session = requests.Session()
+
+    try:
+        response = session.get(article_url, headers = headers, timeout = 10)
+
+        if response.status_code == 200:
+            article = Article(article_url)
+            article.download()
+            article.parse()
+
+            # print(f'Title: {article.title}')
+            # print(f'Content: {article.text}')
+
+        else:
+            print(f'Failed to retrieve article at url: {article_url}')
+    except Exception as e:
+        print(f'Error fectching article at url: {article_url}')
+
+    article_title =  article.title
+    article_text = article.text
+
+    template = """You are a very good assistant that summarizes online articles 
+
+    Here's the article you want to summarize
+
+    ============
+    Title: {article_title}
+
+    {article_text}
+
+    ============
+
+    Write a summary of the previous article in a bulleted list.
+
+    """
+
+    prompt = template.format(article_title = article_title, article_text = article_text)
+
+
+    messages = [HumanMessage(content= prompt)]
+
+    # print('=============== Model================')
+
+    # print (messages)
+    chat = ChatOpenAI(model_name = 'gpt-3.5-turbo', temperature=0)
+
+    summary = chat(messages)
+
+    return(summary.content)
+
+
+demo = gr.Interface(
+    fn= summarize,
+    inputs = ['text'],
+    outputs = ['text'],
+    article = 'The project takes in a url of an article or blog and returns a summary of it.'
+)
+
+demo.launch()
+

requirements.txt

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+requests
+langchain
+python3.10
+newspaper3k