update

app.py

@@ -6,7 +6,6 @@ from simplet5 import SimpleT5
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 
-@st.cache
 def load_t5():
     model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
 
@@ -14,7 +13,7 @@ def load_t5():
     return model, tokenizer
 
 
-@st.cache(allow_output_mutation=False)
+@st.cache
 def custom_model():
     return pipeline("summarization", model="my_awesome_sum/")
 
@@ -22,7 +21,7 @@ def custom_model():
 @st.cache
 def convert_df(df):
     # IMPORTANT: Cache the conversion to prevent computation on every rerun
-    return df.to_csv().encode("utf-8")
+    return df.to_csv(index=False).encode("utf-8")
 
 
 @st.cache
@@ -77,7 +76,7 @@ if st.button("Process"):
             model, tokenizer = load_t5()
             text = df["text"].values.tolist()
             summary = []
-            for x in stqdm(range(10)):
+            for x in stqdm(range(len(text))):
 
                 tokens_input = tokenizer.encode(
                     "summarize: " + text[x],
@@ -96,7 +95,7 @@ if st.button("Process"):
                 summary.append(summary_gen)
 
             output = pd.DataFrame(
-                {"text": df["text"].values.tolist()[0:10], "summary": summary}
+                {"text": df["text"].values.tolist(), "summary": summary}
             )
             csv = convert_df(output)
             st.download_button(
@@ -113,13 +112,13 @@ if st.button("Process"):
             load_one_line_summarizer(model=model)
 
             summary = []
-            for x in stqdm(range(10)):
+            for x in stqdm(range(len(text))):
                 try:
                     summary.append(model.predict(text[x])[0])
                 except:
                     pass
             output = pd.DataFrame(
-                {"text": df["text"].values.tolist()[0:10], "summary": summary}
+                {"text": df["text"].values.tolist(), "summary": summary}
             )
             csv = convert_df(output)
             st.download_button(