Update app.py

app.py

@@ -6,28 +6,48 @@ model_path = "abhisheky127/FeedbackSummarizerEnterpret"
 
 summarizer = pipeline("summarization", model=model_path)
 
+def postprocess(prediction, input):
+  if len(input.split(" "))<5:
+    return("None")
+  else:
+    return(prediction.split('.')[0]+".")
+
 st.title("Feedback Summarizer: Enterpret")
 st.markdown(
     """ 
         ####  Summarize reviews/feedbacks with fine-tuned T5-small language Model
         > *powered by Hugging Face T5, Streamlit*
-        ----
+        
         """
 )
+st.image("Screenshot 2023-06-25 at 11.49.26 PM.png")
+st.markdown("----")
+
+product = st.text_input('Product', '')
+
+type = st.text_input('Record Type', '')
+
+text = st.text_area('Text to Summarize', '''''')
+
+if st.button('Summarize'):
+    if len(product) and len(type) and len(text):
+        with st.spinner(
+                    "Summarizing your feedback : `{}` ".format(query)
+                ):
+                    text = "<product>{}</product><type>{}</type><text>{}</text>".format(product, type, text)
+                    
+                    res = summarizer(text)[0]["summary_text"]
+                    res = post_process(res, text)
+                    st.info(res, icon="🤖")
+    else:
+        st.write('I think something is missing, please check your inputs!')
+
+
+
 
-text = "<product>zoom</product><type>Appstore/Playstore</type><text>user: this is very successful meeting business</text>"
-pred = summarizer(text)
-st.write(pred)
 
-# file_name = st.file_uploader("Upload a hot dog candidate image")
 
-# if file_name is not None:
-#     col1, col2 = st.columns(2)
 
-#     image = Image.open(file_name)
-#     col1.image(image, use_column_width=True)
-#     predictions = pipeline(image)
+st.markdown("----")
+st.image("Screenshot 2022-11-20 at 5.40.54 AM.png")
 
-#     col2.header("Probabilities")
-#     for p in predictions:
-#         col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")