Update app.py

app.py

@@ -11,72 +11,52 @@ st.subheader("Search for News and classify the headlines with sentiment analysis
 query = st.text_input("Enter Query")
 
 models = [
-          "j-hartmann/emotion-english-distilroberta-base",
-          "SamLowe/roberta-base-go_emotions"
-          # "distilbert/distilbert-base-uncased-finetuned-sst-2-english"
-        ]
+    "j-hartmann/emotion-english-distilroberta-base",
+    "SamLowe/roberta-base-go_emotions"
+]
 
 settings = {
-  # "langregion": "en/US",
-  "period": "1d",
-  "model": models[0],
-  "number_of_pages": 5
+    "period": "1d",
+    "model": models[0],
+    "number_of_pages": 5
 }
 
-
 with st.sidebar:
-  st.title("Settings")
-  # add language and country parameters
-  # st.header("Language and Country")
-  
-  # settings["langregion"] = st.selectbox("Select Language", ["en/US", "fr/FR"])
-  # input field for number of pages
-  st.header("Number of Pages")
-  settings["number_of_pages"] = st.number_input("Enter Number of Pages", min_value=1, max_value=10)
-
-  # settings["region"] = settings["langregion"].split("/")[0]
-  # settings["lang"] = settings["langregion"].split("/")[1]
+    st.title("Settings")
+    st.header("Number of Pages")
+    settings["number_of_pages"] = st.number_input("Enter Number of Pages", min_value=1, max_value=10)
 
-  # add period parameter
-  st.header("Period")
-  settings["period"] = st.selectbox("Select Period", ["1d", "7d", "30d"])
-  # Add models parameters
-  st.header("Models")
-  settings["model"] = st.selectbox("Select Model", models)
+    st.header("Period")
+    settings["period"] = st.selectbox("Select Period", ["1d", "7d", "30d"])
 
+    st.header("Models")
+    settings["model"] = st.selectbox("Select Model", models)
 
 if st.button("Search"):
-  classifier = pipeline(task="text-classification", model=settings["model"], top_k=None)
-  # display a loading progress
-  with st.spinner("Loading last news ..."):
-    allnews = wna.get_news(settings, query)
-    st.dataframe(allnews)
-  with st.spinner("Processing received news ..."):
-    df = pd.DataFrame(columns=["sentence", "date","best","second"])
-    # loop on each sentence and call classifier
-    for curnews in allnews:
-      #st.write(curnews)
-      cur_sentence = curnews["title"]
-      cur_date = curnews["date"]
-      model_outputs = classifier(cur_sentence)
-      cur_result = model_outputs[0]
-      #st.write(cur_result)
-      # get label 1
-      label = cur_result[0]['label']
-      score = cur_result[0]['score']
-      percentage = round(score * 100, 2)
-      str1 = label + " (" + str(percentage) + ")%"
-      # get label 2
-      label = cur_result[1]['label']
-      score = cur_result[1]['score']
-      percentage = round(score * 100, 2)
-      str2 = label + " (" + str(percentage) + ")%"
-      # insert cur_sentence and cur_result into dataframe
-      df.loc[len(df.index)] = [cur_sentence, cur_date, str1, str2]
+    classifier = pipeline(task="text-classification", model=settings["model"], top_k=None)
+
+    with st.spinner("Loading last news ..."):
+        allnews = wna.get_news(settings, query)
+        st.dataframe(allnews)
+
+    with st.spinner("Processing received news ..."):
+        df = pd.DataFrame(columns=["sentence", "date", "best", "second"])
+        
+        for curnews in allnews:
+            cur_sentence = curnews["title"]
+            cur_date = curnews["date"]
+            model_outputs = classifier(cur_sentence)
+            cur_result = model_outputs[0]
+
+            label1 = cur_result[0]['label']
+            score1 = round(cur_result[0]['score'] * 100, 2)
+            str1 = f"{label1} ({score1}%)"
 
-  # write info on the output
-  st.write("Number of sentences:", len(df))
-  st.write("Language:", settings["lang"], "Country:", settings["region"])
+            label2 = cur_result[1]['label']
+            score2 = round(cur_result[1]['score'] * 100, 2)
+            str2 = f"{label2} ({score2}%)"
 
-  st.dataframe(df)
+            df.loc[len(df.index)] = [cur_sentence, cur_date, str1, str2]
 
+    st.write("Number of sentences:", len(df))
+    st.dataframe(df)