Update app.py

app.py

@@ -5,58 +5,76 @@ from transformers import pipeline
 
 st.set_page_config(layout="wide")
 
-st.title("WNA Google News App")
-st.subheader("Search for News and classify the headlines with sentiment analysis")
+st.title("Google News LLM")
 
-query = st.text_input("Enter Query")
+query = st.text_input("Search for news")
 
 models = [
-    "j-hartmann/emotion-english-distilroberta-base",
-    "SamLowe/roberta-base-go_emotions"
-]
+          "j-hartmann/emotion-english-distilroberta-base",
+          "SamLowe/roberta-base-go_emotions",
+          "distilbert/distilbert-base-uncased-finetuned-sst-2-english"
+        ]
 
 settings = {
-    "period": "1d",
-    "model": models[0],
-    "number_of_pages": 5
+  "langregion": "en/US",
+  "period": "1d",
+  "model": models[0],
+  "number_of_pages": 5
 }
 
-with st.sidebar:
-    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)
 
-    st.header("Period")
-    settings["period"] = st.selectbox("Select Period", ["1d", "7d", "30d"])
+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)
 
-    st.header("Models")
-    settings["model"] = st.selectbox("Select Model", models)
+  settings["region"] = settings["langregion"].split("/")[0]
+  settings["lang"] = settings["langregion"].split("/")[1]
 
-if st.button("Search"):
-    classifier = pipeline(task="text-classification", model=settings["model"], top_k=None)
+  # 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)
 
-    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}%)"
+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]
 
-            label2 = cur_result[1]['label']
-            score2 = round(cur_result[1]['score'] * 100, 2)
-            str2 = f"{label2} ({score2}%)"
+  # write info on the output
+  st.write("Number of sentences:", len(df))
 
-            df.loc[len(df.index)] = [cur_sentence, cur_date, str1, str2]
+  st.dataframe(df)
 
-    st.write("Number of sentences:", len(df))
-    st.dataframe(df)