Update app.py

app.py

@@ -10,6 +10,22 @@ import numpy as np
 import streamlit as st
 
 
+
+   ##@st.cache(allow_output_mutation=True)
+def load_model():
+    return KeyBERT()
+
+kw_model = load_model()
+
+keywords = kw_model.extract_keywords(
+text_str,
+keyphrase_ngram_range=(1, 2),
+use_mmr=True,
+stop_words="english",
+top_n=15,
+diversity=0.7,
+)
+
 with st.container():
     st.markdown("<h1 style='text-align: center; color: black;'> Policy Action Tracking</h1>", unsafe_allow_html=True)
     st.write(' ')
@@ -25,122 +41,7 @@ with st.expander("ℹ️ - About this app", expanded=True):
         """
     )
 
-    st.markdown("")
+st.markdown("")
 
 st.markdown("")
 st.markdown("##  📌 Step One: Upload document ")
-
-with st.container():
-
-    file = st.file_uploader('Upload PDF File', type=['pdf'])
-
-    if file is not None:
-        text = []
-        with pdfplumber.open(file) as pdf:
-            for page in pdf.pages:
-                text.append(page.extract_text())
-            text_str = ' '.join([page for page in text])
-
-            st.write('Number of pages:',len(pdf.pages))
-
-        @st.cache(allow_output_mutation=True)
-        def load_model():
-            return KeyBERT()
-
-        kw_model = load_model()
-
-        keywords = kw_model.extract_keywords(
-        text_str,
-        keyphrase_ngram_range=(1, 2),
-        use_mmr=True,
-        stop_words="english",
-        top_n=15,
-        diversity=0.7,
-        )
-
-        st.markdown("## 🎈 What is my document about?")
-    
-        df = (
-            DataFrame(keywords, columns=["Keyword/Keyphrase", "Relevancy"])
-            .sort_values(by="Relevancy", ascending=False)
-            .reset_index(drop=True)
-        )
-
-        df.index += 1
-
-        # Add styling
-        cmGreen = sns.light_palette("green", as_cmap=True)
-        cmRed = sns.light_palette("red", as_cmap=True)
-        df = df.style.background_gradient(
-            cmap=cmGreen,
-            subset=[
-                "Relevancy",
-            ],
-        )
-        c1, c2, c3 = st.columns([1, 3, 1])
-
-        format_dictionary = {
-            "Relevancy": "{:.1%}",
-        }
-
-        df = df.format(format_dictionary)
-
-        with c2:
-            st.table(df) 
-
-        ######## SDG!
-        from transformers import pipeline 
-
-        finetuned_checkpoint = "peter2000/roberta-base-finetuned-osdg"
-        classifier = pipeline("text-classification", model=finetuned_checkpoint)
-
-        word_list = text_str.split()
-        len_word_list = len(word_list)
-        par_list = []
-        par_len = 130
-        for i in range(0,len_word_list // par_len):
-            string_part = ' '.join(word_list[i*par_len:(i+1)*par_len])
-            par_list.append(string_part)
-            
-        labels = classifier(par_list)
-        labels_= [(l['label'],l['score']) for l in labels]
-        df = DataFrame(labels_, columns=["SDG", "Relevancy"])
-        df['text'] = par_list      
-        df = df.sort_values(by="Relevancy", ascending=False).reset_index(drop=True)  
-        df.index += 1
-        #df =df[df['Relevancy']>.95]
-        x = df['SDG'].value_counts()
-
-        plt.rcParams['font.size'] = 25
-        colors = plt.get_cmap('Blues')(np.linspace(0.2, 0.7, len(x)))
-        # plot
-        fig, ax = plt.subplots()
-        ax.pie(x, colors=colors, radius=2, center=(4, 4),
-             wedgeprops={"linewidth": 1, "edgecolor": "white"}, frame=False,labels =list(x.index))
-
-        st.markdown("## 🎈 Anything related to SDGs?")
-
-        c4, c5, c6 = st.columns([5, 7, 1])
-
-        # Add styling
-        cmGreen = sns.light_palette("green", as_cmap=True)
-        cmRed = sns.light_palette("red", as_cmap=True)
-        df = df.style.background_gradient(
-            cmap=cmGreen,
-            subset=[
-                "Relevancy",
-            ],
-        )
-
-        format_dictionary = {
-            "Relevancy": "{:.1%}",
-        }
-
-        df = df.format(format_dictionary)
-
-        with c4:
-            st.pyplot(fig)
-        with c5:
-            st.table(df) 
-
-