Upload app (1).py

app (1).py

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+import streamlit as st
+from transformers import pipeline
+
+def analyze_financial_news():
+    access = "hf_"
+    token = "hhbFNpjKohezoexWMlyPUpvJQLWlaFhJaa"
+
+    # Load the text classification model and finetuned model pipeline
+    classification = pipeline("text-classification", model="nickmuchi/finbert-tone-finetuned-finance-topic-classification", token=access+token)
+    sentiment_analysis = pipeline("text-classification", model="JoanParanoid/FinNews_SentimentAnalysis_v2")
+
+    st.set_page_config(page_title="Energy/Oil-Related Financial News Sentiment Analysis", page_icon="♕")
+
+    # Streamlit application layout
+    st.title("Energy/Oil-Related Financial News Sentiment Analysis")
+    st.write("Conduct Sentiment Analysis for Energy/Oil-Related Financial News to Find Out the Trend In Energy/Oil Industry and Make Wise Decisions!")
+    st.image("./Fin.jpg", use_column_width=True)
+
+    # Text input for user to enter the text
+    text = st.text_area("Enter the Financial News Content", "")
+
+    analyze_clicked = st.button("Analyze")
+
+    if analyze_clicked:
+        # Perform text classification on the input text
+        results = classification(text)[0]
+
+        # Check if the classification is "Energy | Oil"
+        if results["label"] == "Energy | Oil":
+            # If the news is related to Energy | Oil, perform sentiment analysis
+            sentiment_results = sentiment_analysis(text)[0]
+            
+            # Display the sentiment analysis result
+            st.write("This financial news belongs to the 'Energy | Oil' category.")
+            st.write("Sentiment:", sentiment_results["label"])
+            st.write("Sentiment Score:", sentiment_results["score"])
+        else:
+            st.write("This financial news does not belong to the 'Energy | Oil' category. Please enter a relevant news article.")
+
+def main():
+    analyze_financial_news()
+
+if __name__ == "__main__":
+    main()