Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+import PyPDF2
+
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf_file):
+    text = ""
+    pdf_reader = PyPDF2.PdfReader(pdf_file)
+    for page_num in range(len(pdf_reader.pages)):
+        page = pdf_reader.getPage(page_num)
+        text += page.extractText()
+    return text
+
+# Streamlit app
+def main():
+    st.title('PDF Text Extraction')
+    
+    uploaded_file = st.file_uploader("Upload a PDF file", type="pdf")
+    if uploaded_file is not None:
+        st.write("File uploaded successfully!")
+
+        # Extract text when file is uploaded
+        text = extract_text_from_pdf(uploaded_file)
+
+        st.write("### Extracted Text:")
+        st.write(text)
+
+        # Use Hugging Face's pipeline for further NLP tasks
+        st.write("### NLP Analysis:")
+        nlp_task = st.selectbox("Select NLP Task", ["Named Entity Recognition", "Sentiment Analysis"])
+
+        if nlp_task == "Named Entity Recognition":
+            ner = pipeline("ner")
+            entities = ner(text)
+            st.write(entities)
+        
+        if nlp_task == "Sentiment Analysis":
+            sentiment_analysis = pipeline("sentiment-analysis")
+            sentiment = sentiment_analysis(text)
+            st.write(sentiment)
+
+if __name__ == "__main__":
+    main()