updated code

app.py

@@ -1,24 +1,36 @@
 import streamlit as st
 import wna_googlenews as wna
+import pandas as pd
 from transformers import pipeline
 
+st.set_page_config(layout="wide")
 
 st.title("WNA Google News App")
-st.subheader("Search for News")
-
-# create a dropdown menu for selecting days range
-days_range = st.selectbox("Select Days Range", ["1d", "7", "30d"])
+st.subheader("Search for News and classify the headlines with sentiment analysis")
 
 query = st.text_input("Enter Query")
 
 settings = {
   "lang": "fr",
   "region": "FR",
-  "period": days_range
+  "period": "1d"
 }
 
 classifier = pipeline(task="text-classification", model="SamLowe/roberta-base-go_emotions", top_k=None)
 
+with st.sidebar:
+  st.title("Settings")
+  # add language and country parameters
+  st.header("Language and Country")
+  settings["lang"] = st.selectbox("Select Language", ["en", "fr"])
+  settings["region"] = st.selectbox("Select Country", ["US", "FR"])
+
+  # add period parameter
+  st.header("Period")
+  settings["period"] = st.selectbox("Select Period", ["1d", "7", "30d"])
+
+
+
 
 if st.button("Search"):
   df = wna.get_news(settings, query)
@@ -27,7 +39,31 @@ if st.button("Search"):
   sentences = df["title"]
   # convert into array
   sentences = sentences.tolist()
-  st.write(sentences)
-  model_outputs = classifier(sentences)
-  st.write(model_outputs[0])
+  # st.write(sentences)
+  # create new dataframe
+  df = pd.DataFrame(columns=["sentence", "best","second"])
+  # loop on each sentence and call classifier
+  for sentence in sentences:
+    cur_sentence = sentence
+    model_outputs = classifier(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, str1, str2]
+
+  # write info on the output
+  st.write("Number of sentences:", len(df))
+  st.write("Language:", settings["lang"], "Country:", settings["region"])
+
+  st.dataframe(df)