Create app_bu.py

app_bu.py

@@ -0,0 +1,75 @@
+import os 
+#os.system('pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.9/index.html')
+
+import transformers
+import streamlit as st
+
+from transformers import AutoTokenizer, AutoModelWithLMHead
+from transformers import pipeline
+
+sentiment_tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-imdb-sentiment")
+    
+def load_text_gen_model():
+    generator = pipeline("text-generation", model="gpt2-medium")
+    return generator 
+    
+@st.cache
+def get_sentiment_model():
+    sentiment_model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-imdb-sentiment")
+    return sentiment_model 
+
+def get_summarizer_model():
+    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+    return summarizer
+
+      
+def get_sentiment(text):
+    input_ids = sentiment_tokenizer .encode(text + '</s>', return_tensors='pt')
+    output = sentiment_extractor.generate(input_ids=input_ids,max_length=2)
+    dec = [sentiment_tokenizer.decode(ids) for ids in output]
+    label = dec[0]
+    return label
+    
+
+def get_qa_model():
+    model_name = "deepset/roberta-base-squad2"
+
+    qa_pipeline = pipeline('question-answering', model=model_name, tokenizer=model_name)
+    return qa_pipeline
+        
+sentiment_extractor   = get_sentiment_model()
+summarizer = get_summarizer_model()
+answer_generator = get_qa_model()
+
+
+st.header("Review Analyzer")
+
+#action = st.sidebar.selectbox("Pick an Action", ["Analyse a Review","Generate an Article","Create an Image"])
+
+#if action == "Analyse a Review":
+st.subheader("Paste/write a review here..")
+review = st.text_area("")
+
+if review:
+    
+    start_sentiment_analysis = st.button("Get the Sentiment of the Review")
+    start_summarizing = st.button("Summarize the review")
+    start_topic_extraction = st.button("Find the key topic")
+
+    if start_sentiment_analysis:
+        sentiment = get_sentiment(review)
+        st.write(sentiment)
+        
+    if start_summarizing:
+        summary = summarizer(review, max_length=130, min_length=30, do_sample=False)
+        st.write(summary)
+    
+    if start_topic_extraction:
+        QA_input = {'question': 'what is the review about?',
+                    'context': review}
+        answer = answer_generator(QA_input)
+        st.write(answer)
+
+        
+         
+