Update app.py

app.py

@@ -1,52 +1,23 @@
 import gradio as gr
-from PyPDF2 import PdfFileReader
-from transformers import pipeline
 
-# Function to extract text from PDF
-def extract_text_from_pdf(pdf_path):
-    with open(pdf_path, 'rb') as file:
-        pdf_reader = PdfFileReader(file)
-        text = ""
-        for page_num in range(pdf_reader.numPages):
-            page = pdf_reader.getPage(page_num)
-            text += page.extractText()
-        return text
+# Function to process the input message and PDF file
+def process_input(message, pdf_file):
+    # Save the uploaded PDF file
+    pdf_file.save("uploaded_pdf.pdf")
 
-# Function to extract the abstract from the text
-def extract_abstract(text):
-    abstract = ""
-    found_abstract = False
-    paragraphs = text.split('\n')
-    for index, paragraph in enumerate(paragraphs):
-        if 'Abstract' in paragraph:
-            found_abstract = True
-            abstract = paragraphs[index + 1]  # Get the next paragraph as the abstract
-    return abstract if found_abstract else "Abstract not found"
-
-# Function to summarize text
-def summarize_text(text):
-    summarizer = pipeline("summarization", model="ainize/bart-base-cnn")
-    summarized_text = summarizer(text, max_length=50, min_length=5, do_sample=False)[0]['summary_text']
-    return summarized_text
-
-# Function to convert text to speech
-def text_to_speech(text):
-    from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech
-    processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
-    model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
-    inputs = processor(text, return_tensors="pt")
-    speech = model.generate_speech(inputs["input_ids"])
-    return speech.numpy().tobytes(), 16000  # Return audio data and sample rate
+    # Process the message and return a result
+    result = f"Message: {message}\nPDF file uploaded successfully!"
+    return result
 
 # Gradio interface
 iface = gr.Interface(
-    fn=lambda pdf_file: text_to_speech(summarize_text(extract_abstract(extract_text_from_pdf(pdf_file.name)))),
-    inputs=gr.File(label="Upload PDF", type="file"),
-    outputs="audio",
-    live=True,
-    title="PDF Abstract Summarizer with Text-to-Speech",
-    description="Upload a PDF, and I will extract the abstract, summarize it, and convert it to speech."
+    fn=process_input,
+    inputs=[
+        gr.inputs.Textbox(label="Enter your message"),
+        gr.inputs.File(label="Upload a PDF file", type="file", accept=".pdf")
+    ],
+    outputs=gr.outputs.Textbox(label="Result")
 )
 
-# Launch the interface
+# Launch the Gradio interface
 iface.launch()