Create app.py

app.py

@@ -0,0 +1,43 @@
+import pdfplumber
+from transformers import pipeline
+from gtts import gTTS
+import speech_recognition as sr
+import os 
+
+
+def extract_abstract(text):
+    # Simple logic to extract text between 'Abstract' and 'Introduction'
+    abstract_start = text.find('Abstract')
+    abstract_end = text.find('Introduction', abstract_start)
+    return text[abstract_start:abstract_end]
+
+def summarize_abstract(abstract):
+    summarizer = pipeline('summarization', model='facebook/bart-large-cnn')
+    summary = summarizer(abstract, max_length=130, min_length=30, do_sample=False)
+    return summary[0]['summary_text']
+
+def convert_to_speech(text):
+    tts = gTTS(text)
+    audio_file = 'summary.mp3'
+    tts.save(audio_file)
+    return audio_file
+
+def my_app_function(pdf_file):
+    with pdfplumber.open(pdf_file) as pdf:
+        first_page = pdf.pages[0]
+        text = first_page.extract_text()
+        abstract = extract_abstract(text)
+        summary = summarize_abstract(abstract)
+        audio_file = convert_to_speech(summary)
+        return summary, audio_file
+
+iface = gr.Interface(
+    fn=my_app_function,
+    inputs=gr.File(),  # Updated line
+    outputs=["text", "audio"],
+    title="PDF Abstract Summarizer",
+    description="This app reads PDFs, summarizes the abstract, and converts the summary to speech. Please upload PDFs with abstracts."
+)
+
+
+iface.launch()