Update app.py

app.py

@@ -22,7 +22,7 @@ def draw_all(
         ```python
         # Key Features of this App.
         1. Advanced Text Summarizer
-        2. Sentiment Analysis
+        2. Key Word Extractor
         3. Question Answering
         4. Text Completion
        
@@ -40,7 +40,7 @@ with st.sidebar:
 def main():
     st.title("NLP IE Web App")
     menu = ["--Select--","Summarizer",
-            "Sentiment Analysis","Question Answering","Text Completion"]
+            "Keyword Extractor","Question Answering","Text Completion"]
     choice = st.sidebar.selectbox("What task would you like to do?", menu)
     if choice=="--Select--":
         
@@ -84,29 +84,29 @@ def main():
         
         
         
-    elif choice=="Sentiment Analysis":
-        st.subheader("Sentiment Analysis")
+    elif choice=="Keyword Extractor":
+        st.subheader("Keyword Extraction")
         #loading the pipeline
         sentiment_analysis = pipeline("sentiment-analysis")
-        st.write(" Enter the Text below To find out its Sentiment !")
+        raw_text = st.text_area("Enter some text:")
+        model_name = "yanekyuk/bert-uncased-keyword"
+        keyword_extractor = pipeline("text2text-generation", model=model_name, tokenizer=model_name, config={"wordwise": True})
 
-        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 ... 😐""")
-    
+            if st.button("Extract Keywords"):
+                # Extract keywords using the model
+                keywords = keyword_extractor(input_text, max_length=20, do_sample=False)[0]["generated_text"]
+                # Display the extracted keywords
+                st.write("Keywords:", keywords)
+
+
+            # Load the keyword extraction model
+
+  
     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")
+        question_answering = pipeline("question-answering", model = "distilbert-base-cased-distilled-squad")
         
 
         context = st.text_area("Context","Enter the Context Here")