Create app.py

app.py

@@ -0,0 +1,33 @@
+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+
+model_path = "abhisheky127/FeedbackSummarizerEnterpret"
+
+summarizer = pipeline("summarization", model=model_path)
+
+ st.title("Feedback Summarizer: Enterpret")
+st.markdown(
+    """ 
+        ####  Summarize reviews/feedbacks with fine-tuned T5-small language Model
+        > *powered by Hugging Face T5, Streamlit*
+        ----
+        """
+)
+
+text = "<product>zoom</product><type>Appstore/Playstore</type><text>user: this is very successful meeting business</text>"
+pred = summarizer(text)
+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)
+
+#     col2.header("Probabilities")
+#     for p in predictions:
+#         col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")