Update app.py

app.py

@@ -1,64 +1,64 @@
 import streamlit as st
-# import openai
-# import nltk
-# nltk.download('punkt')
+import openai
+import nltk
+nltk.download('punkt')
 
-st.write("ok")
-# # Set up OpenAI API credentials
-# openai.api_key = "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"
+# st.write("ok")
+# Set up OpenAI API credentials
+openai.api_key = "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"
 
-# # Define function to generate keywords
-# def generate_keywords(text):
-#     num_keywords = 6
-#     cleaned_text = text.strip()
+# Define function to generate keywords
+def generate_keywords(text):
+    num_keywords = 6
+    cleaned_text = text.strip()
   
-#     response = openai.Completion.create(
-#         engine="text-davinci-002",
-#         prompt=f"What are {num_keywords} highly related keywords for the following text?\n{cleaned_text}\n\nKeywords:",
-#         max_tokens=50,
-#         n=1,
-#         stop=None,
-#         temperature=0.5,
-#         best_of=num_keywords,
-#     )
+    response = openai.Completion.create(
+        engine="text-davinci-002",
+        prompt=f"What are {num_keywords} highly related keywords for the following text?\n{cleaned_text}\n\nKeywords:",
+        max_tokens=50,
+        n=1,
+        stop=None,
+        temperature=0.5,
+        best_of=num_keywords,
+    )
 
-#     generated_text = response.choices[0].text.strip()
+    generated_text = response.choices[0].text.strip()
 
-#     keywords = generated_text.split(',')
+    keywords = generated_text.split(',')
 
-#     st.write("Top Keywords:")
-#     for i, keyword in enumerate(keywords[:num_keywords]):
-#         st.write(f"{i+1}. {keyword.strip()}")
+    st.write("Top Keywords:")
+    for i, keyword in enumerate(keywords[:num_keywords]):
+        st.write(f"{i+1}. {keyword.strip()}")
 
-# # Define function to generate summary
-# def generate_summary(text):
-#     summary_length = 2
+# Define function to generate summary
+def generate_summary(text):
+    summary_length = 2
 
-#     cleaned_text = text.strip()
+    cleaned_text = text.strip()
 
-#     response = openai.Completion.create(
-#         engine="text-davinci-002",
-#         prompt=f"Please summarize the following text in {summary_length} sentences:\n{cleaned_text}\n\nSummary:",
-#         max_tokens=100,
-#         n=1,
-#         stop=None,
-#         temperature=0.5,
-#     )
+    response = openai.Completion.create(
+        engine="text-davinci-002",
+        prompt=f"Please summarize the following text in {summary_length} sentences:\n{cleaned_text}\n\nSummary:",
+        max_tokens=100,
+        n=1,
+        stop=None,
+        temperature=0.5,
+    )
 
-#     generated_text = response.choices[0].text.strip()
-#     st.write("Description:")
-#     # st.write(generated_text)
-#     sentences = nltk.sent_tokenize(generated_text)
-#     for sentence in sentences:
-#       st.write(sentence)
+    generated_text = response.choices[0].text.strip()
+    st.write("Description:")
+    # st.write(generated_text)
+    sentences = nltk.sent_tokenize(generated_text)
+    for sentence in sentences:
+      st.write(sentence)
 
-# # Set up Streamlit app
-# st.title("Text Summarization and Keyword Extraction")
+# Set up Streamlit app
+st.title("Text Summarization and Keyword Extraction")
 
-# text = st.text_area("Enter some text:")
+text = st.text_area("Enter some text:")
 
-# if st.button("Generate Keywords"):
-#     generate_keywords(text)
+if st.button("Generate Keywords"):
+    generate_keywords(text)
 
-# if st.button("Generate Summary"):
-#     generate_summary(text)
+if st.button("Generate Summary"):
+    generate_summary(text)