update app.py

app.py

@@ -1,10 +1,146 @@
-#adding
-import streamlit as st
+import streamlit as st 
+from time import sleep
+from stqdm import stqdm
+import pandas as pd
 from transformers import pipeline
+import json
+import spacy
+import spacy_streamlit
 
-pipe = pipeline('sentiment-analysis')
-text = st.text_area('enter some text!')
 
-if text:
-    out = pipe(text)
-    st.json(out)
+def draw_all(
+    key,
+    plot=False,
+):
+    st.write(
+        """
+        # NLP Web App
+        
+        This Natural Language Processing Based Web App can do anything u can imagine with Text. 😱 
+        
+        This App is built using pretrained transformers which are capable of doing wonders with the Textual data.
+        
+        ```python
+        # Key Features of this App.
+        1. Advanced Text Summarizer
+        2. Sentiment Analysis
+        3. Question Answering
+        4. Text Completion
+       
+        ```
+        """
+    )
+
+    
+
+with st.sidebar:
+    draw_all("sidebar")
+
+
+#main function that holds all the options
+def main():
+    st.title("NLP IE Web App")
+    menu = ["--Select--","Summarizer",
+            "Sentiment Analysis","Question Answering","Text Completion"]
+    choice = st.sidebar.selectbox("What task would you like to do?", menu)
+    if choice=="--Select--":
+        
+        st.write("""
+                 
+                 Welcome to the the Web App of Data Dynamos. As an IE student of the Master of Business Analyitics and Big Data you have the opportunity to
+                 do anything with your lectures you like
+        """)
+        
+        st.write("""
+                 
+                Never heard of NLP? No way! Natural Language Processing (NLP) is a computational technique
+                to process human language in all of it's complexity
+        """)
+        
+        st.write("""
+                 
+                 NLP is an vital discipline in Artificial Intelligence and keeps growing
+        """)
+        
+        
+        st.image('banner_image.png')
+    
+    
+    
+    elif choice=="Summarizer":
+        st.subheader("Text Summarization")
+        st.write(" Enter the Text you want to summarize !")
+        raw_text = st.text_area("Your Text","Enter Your Text Here")
+        num_words = st.number_input("Enter Number of Words in Summary")
+        
+        if raw_text!="" and num_words is not None:
+            num_words = int(num_words)
+            summarizer = pipeline('summarization')
+            summary = summarizer(raw_text, min_length=num_words,max_length=50)
+            s1 = json.dumps(summary[0])
+            d2 = json.loads(s1)
+            result_summary = d2['summary_text']
+            result_summary = '. '.join(list(map(lambda x: x.strip().capitalize(), result_summary.split('.'))))
+            st.write(f"Here's your Summary : {result_summary}")
+        
+        
+        
+    elif choice=="Sentiment Analysis":
+        st.subheader("Sentiment Analysis")
+        #loading the pipeline
+        sentiment_analysis = pipeline("sentiment-analysis")
+        st.write(" Enter the Text below To find out its Sentiment !")
+
+        raw_text = st.text_area("Your Text","Enter Text Here")
+        if raw_text !="Enter Text Here":
+            result = sentiment_analysis(raw_text)[0]
+            sentiment = result['label']
+            for _ in stqdm(range(50), desc="Please wait a bit. The model is fetching the results !!"):
+                sleep(0.1)
+            if sentiment =="POSITIVE":
+                st.write("""# This text has a Positive Sentiment.  🤗""")
+            elif sentiment =="NEGATIVE":
+                st.write("""# This text has a Negative Sentiment. 😤""")
+            elif sentiment =="NEUTRAL":
+                st.write("""# This text seems Neutral ... 😐""")
+    
+    elif choice=="Question Answering":
+        st.subheader("Question Answering")
+        st.write(" Enter the Context and ask the Question to find out the Answer !")
+        question_answering = pipeline("question-answering")
+        
+
+        context = st.text_area("Context","Enter the Context Here")
+
+        #This is the text box for the question
+        question = st.text_area("Your Question","Enter your Question Here")
+        
+        if context !="Enter Text Here" and question!="Enter your Question Here":
+            #we are passing question and the context
+            result = question_answering(question=question, context=context)
+            #dump the result in json and load it again
+            s1 = json.dumps(result)
+            d2 = json.loads(s1)
+            generated_text = d2['answer']
+            #joining and capalizing by dot
+            generated_text = '. '.join(list(map(lambda x: x.strip().capitalize(), generated_text.split('.'))))
+            st.write(f" Here's your Answer :\n {generated_text}")
+    
+    elif choice=="Text Completion":
+        st.subheader("Text Completion")
+        st.write(" Enter the uncomplete Text to complete it automatically using AI !")
+        text_generation = pipeline("text-generation")
+        message = st.text_area("Your Text","Enter the Text to complete")
+        
+        
+        if message !="Enter the Text to complete":
+            generator = text_generation(message)
+            s1 = json.dumps(generator[0])
+            d2 = json.loads(s1)
+            generated_text = d2['generated_text']
+            generated_text = '. '.join(list(map(lambda x: x.strip().capitalize(), generated_text.split('.'))))
+            st.write(f" Here's your Generate Text :\n   {generated_text}")
+
+    #main function to run
+if __name__ == '__main__':
+	main()