Update app.py

app.py

@@ -2,9 +2,15 @@ import streamlit as st
 from transformers import T5ForConditionalGeneration, T5Tokenizer, pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
 import nltk
 from youtube_transcript_api import YouTubeTranscriptApi
+import torch
+from textblob import TextBlob
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
 
 # Download NLTK data
 nltk.download('punkt')
+nltk.download('averaged_perceptron_tagger')
+nltk.download('stopwords')
 
 # Load models and tokenizers
 summary_model_name = 'utrobinmv/t5_summary_en_ru_zh_base_2048'
@@ -24,6 +30,15 @@ def summarize_text(text, prefix):
     result = summary_tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
     return result[0]
 
+# Function to generate tags
+def generate_tags(text):
+    with torch.no_grad():
+        inputs = tag_tokenizer(text, max_length=256, truncation=True, return_tensors="pt")
+        output = tag_model.generate(**inputs, num_beams=8, do_sample=True, min_length=10, max_length=64, num_return_sequences=1)
+        decoded_output = tag_tokenizer.batch_decode(output, skip_special_tokens=True)[0]
+        tags = list(set(decoded_output.strip().split(", ")))
+    return tags
+
 # Function to fetch YouTube transcript
 def fetch_transcript(url):
     video_id = url.split('watch?v=')[-1]
@@ -34,19 +49,61 @@ def fetch_transcript(url):
     except Exception as e:
         return str(e)
 
+# Function to extract keywords and generate hashtags
+def extract_keywords(content):
+    text = content.lower()
+    sentences = nltk.sent_tokenize(text)
+    keywords = []
+    for sentence in sentences:
+        words = nltk.word_tokenize(sentence)
+        tags = nltk.pos_tag(words)
+        for word, tag in tags:
+            if tag.startswith('NN'):
+                keywords.append(word)
+    return keywords
+
+def generate_hashtags(content, max_hashtags=10):
+    keywords = extract_keywords(content)
+    hashtags = []
+    for keyword in keywords:
+        hashtag = "#" + keyword
+        if len(hashtag) <= 20:
+            hashtags.append(hashtag)
+    return hashtags[:max_hashtags]
+
+# Function to extract point of view
+def extract_point_of_view(text):
+    stop_words = set(stopwords.words('english'))
+    words = word_tokenize(str(text))
+    filtered_words = [word for word in words if word.casefold() not in stop_words]
+    text = ' '.join(filtered_words)
+
+    blob = TextBlob(text)
+    polarity = blob.sentiment.polarity
+    subjectivity = blob.sentiment.subjectivity
+
+    if polarity > 0.5:
+        point_of_view = "Positive"
+    elif polarity < -0.5:
+        point_of_view = "Negative"
+    else:
+        point_of_view = "Neutral"
+
+    return point_of_view
+
 # Streamlit app title
-st.title("Multi-purpose Machine Learning App: WAVE_AI")
+st.title("Multi-purpose AI App: WAVE_AI")
 
 # Create tabs for different functionalities
-tab1, tab2, tab3, tab4 = st.tabs(["Text Summarization", "Text Tag Generation", "Image Captioning", "YouTube Transcript"])
+tab1, tab2, tab3, tab4, tab5 = st.tabs(["Text Summarization", "Text Tag Generation", "Image Captioning", "YouTube Transcript", "LinkedIn Post Analysis"])
 
 # Text Summarization Tab
 with tab1:
-    st.header("Text Summarization")
+    st.header("Summarize Title Maker")
 
     input_text = st.text_area("Enter the text to summarize:", height=300)
 
-    if st.button("Generate Summaries"):
+    if st.button("Generate the Title"):
         if input_text:
             title1 = summarize_text(input_text, 'summary: ')
             title2 = summarize_text(input_text, 'summary brief: ')
@@ -59,17 +116,14 @@ with tab1:
 
 # Text Tag Generation Tab
 with tab2:
-    st.header("Text Tag Generation")
+    st.header("Tag Generation from Text")
     
     text = st.text_area("Enter the text for tag extraction:", height=200)
     
     if st.button("Generate Tags"):
         if text:
             try:
-                inputs = tag_tokenizer([text], max_length=512, truncation=True, return_tensors="pt")
-                output = tag_model.generate(**inputs, num_beams=8, do_sample=True, min_length=10, max_length=64)
-                decoded_output = tag_tokenizer.batch_decode(output, skip_special_tokens=True)[0]
-                tags = list(set(decoded_output.strip().split(", ")))
+                tags = generate_tags(text)
                 st.write("**Generated Tags:**")
                 st.write(tags)
             except Exception as e:
@@ -83,7 +137,8 @@ with tab3:
     
     image_url = st.text_input("Enter the URL of the image:")
     
-    if image_url:
+    if st.button("Analysis Image"):
+        if image_url:
         try:
             st.image(image_url, caption="Provided Image", use_column_width=True)
             caption = captioner(image_url)
@@ -108,3 +163,36 @@ with tab4:
                 st.error(f"An error occurred: {transcript}")
         else:
             st.warning("Please enter a URL.")
+
+# LinkedIn Post Analysis Tab
+with tab5:
+    st.header("LinkedIn Post Analysis AI")
+
+    text = st.text_area("Enter the LinkedIn Post:")
+
+    if st.button("Analyze:"):
+        if text:
+            # Generate tags
+            tags = generate_tags(text)
+            st.subheader("The Most Tracked KeyWords:")
+            st.write(tags)
+
+            # Generate summaries
+            summary1 = summarize_text(text, 'summary: ')
+            summary2 = summarize_text(text, 'summary brief: ')
+            st.subheader("Summary Title 1:")
+            st.write(summary1)
+            st.subheader("Summary Title 2:")
+            st.write(summary2)
+
+            # Generate hashtags
+            hashtags = generate_hashtags(text)
+            st.subheader("Generated Hashtags for the Post")
+            st.write(hashtags)
+
+            # Extract point of view
+            point_of_view = extract_point_of_view(text)
+            st.subheader("Tone of the Post:")
+            st.write(point_of_view)
+        else:
+            st.warning("Please enter text to analyze.")