Delete app.py

app.py

@@ -1,43 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-
-access = "hf_"
-token = "hhbFNpjKohezoexWMlyPUpvJQLWlaFhJaa"
-
-# Load the text classification model pipeline
-analysis = pipeline("text-classification", model='ZephyruSalsify/FinNews_SentimentAnalysis_v3')
-classification = pipeline("text-classification", model="nickmuchi/finbert-tone-finetuned-finance-topic-classification", token=access+token)
-
-st.set_page_config(page_title="Financial News Analysis", page_icon="♕")
-
-# Streamlit application layout
-st.title("Financial News Analysis")
-st.write("Analyze corresponding Topic and Trend for Financial News!")
-st.image("./Fin.jpg", use_column_width = True)
-
-# Text input for user to enter the text
-text = st.text_area("Enter the Financial News", "")
-
-# Perform text classification when the user clicks the "Classify" button
-if st.button("Analyze"):
-
-    label_1 = ""
-    score_1 = 0.0
-    label_2 = ""
-    score_2 = 0.0
-
-    # Perform text analysis on the input text
-    results_1 = analysis(text)[0]
-    results_2 = classification(text)[0]
-
-    label_1 = results_1["label"]
-    score_1 = results_1["score"]
-    label_2 = results_2["label"]
-    score_2 = results_2["score"]
-            
-st.write("Financial Text:", text)
-st.write("Trend:", label_1)
-st.write("Trend_Score:", score_1)
-
-st.write("Finance Topic:", label_2)
-st.write("Topic_Score:", score_2)