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app.py

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+import streamlit as st
+import tensorflow as tf
+import tensorflow_hub as hub
+
+
+
+
+new_model = tf.keras.models.load_model("best_model.h5",custom_objects={"KerasLayer": hub.KerasLayer})
+
+
+
+
+
+def welcome():
+    return "Welcome to my app"
+
+
+def main():
+    st.title("Financial News Sentiment Analysis App")
+    st.write(
+        "This app will tell you if mention news is Fake or Real by using Natural Language Processing")
+    html_temp = """
+    <div style="background-color:tomato;padding:10px">
+    <h2 style="color:white;text-align:center;">Financial News Sentiment Analysis </h2>
+    </div>
+    """
+    st.markdown(html_temp, unsafe_allow_html=True)
+
+    text = st.text_input("Enter your Financial News")
+
+
+    if st.button("Predict"):
+        pred_prob = new_model.predict([text])
+        predict = tf.squeeze(tf.round(pred_prob)).numpy()
+        st.subheader("AI thinks that ...")
+
+        if predict > 0:
+
+            st.success(
+                f"It's a Positive News.You can make your investment decision accordingly. Confidence Level is {tf.round(pred_prob, 3)}%",icon="✅")
+        else:
+            st.warning(
+                f"It's a Negative News. Think twice before you take any investment decision. Confidence Level is {tf.round(100 - pred_prob, 3)}%", icon="⚠️")
+
+    if st.button("About"):
+
+        st.text("Built with Streamlit")
+
+
+if __name__ == '__main__':
+    main()
+

best_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:175095238f145e32d1eb60eef932f9581e82a496de986fd8117c7e2ce3f2f13b
+size 598273408

requirements.txt

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+streamlit==1.15.1
+tensorflow_cpu==2.8.0
+tensorflow_hub==0.12.0
+