Update app.py

app.py

@@ -1,63 +1,64 @@
 import streamlit as st
-import openai
-import nltk
-nltk.download('punkt')
+# import openai
+# import nltk
+# nltk.download('punkt')
 
-# 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,
-    )
-
-    generated_text = response.choices[0].text.strip()
-
-    keywords = generated_text.split(',')
-
-    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
-
-    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,
-    )
-
-    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")
-
-text = st.text_area("Enter some text:")
-
-if st.button("Generate Keywords"):
-    generate_keywords(text)
-
-if st.button("Generate Summary"):
-    generate_summary(text)
+#     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()
+
+#     keywords = generated_text.split(',')
+
+#     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
+
+#     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,
+#     )
+
+#     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")
+
+# text = st.text_area("Enter some text:")
+
+# if st.button("Generate Keywords"):
+#     generate_keywords(text)
+
+# if st.button("Generate Summary"):
+#     generate_summary(text)