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app.py

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+import streamlit as st
+st.set_page_config(f'SDSN x GIZ Policy Tracing', layout="wide")
+
+import seaborn as sns
+import pdfplumber
+from pandas import DataFrame
+from keybert import KeyBERT
+import matplotlib.pyplot as plt
+import numpy as np
+import streamlit as st
+
+def app():
+
+    with st.container():
+        st.markdown("<h1 style='text-align: center; color: black;'> Policy Action Tracking</h1>", unsafe_allow_html=True)
+        st.write(' ')
+        st.write(' ')
+
+    with st.expander("ℹ️ - About this app", expanded=True):
+
+        st.write(
+            """     
+            The *Policy Action Tracker* app is an easy-to-use interface built in Streamlit for analyzing policy documents - developed by GIZ Data and the Sustainable Development Solution Network.
+
+            It uses a minimal keyword extraction technique that leverages multiple NLP embeddings and relies on [Transformers] (https://huggingface.co/transformers/) 🤗 to create keywords/keyphrases that are most similar to a document.
+            """
+        )
+
+        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) 
+
+
+
+app.run()