Update app.py

app.py

@@ -9,141 +9,138 @@ 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.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):
+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.
+    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("")
+        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("##  📌 Step One: Upload document ")
+
+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?")
     
-    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()
+        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) 
+
+