Delete app.py

app.py

@@ -1,75 +0,0 @@
-pip install gradio PyMuPDF
-
-import gradio as gr
-from transformers import T5Tokenizer, MT5ForConditionalGeneration
-import fitz  # PyMuPDF
-
-# Load the fine-tuned tokenizer and model
-model_name = "fine-tuned-mt5"
-new_tokenizer = T5Tokenizer.from_pretrained(model_name, clean_up_tokenization_spaces=True)
-new_model = MT5ForConditionalGeneration.from_pretrained(model_name)
-
-# Function to extract text from PDF using PyMuPDF
-def extract_text_from_pdf(pdf_file):
-    text = ""
-    # Open the PDF file
-    with fitz.open(pdf_file) as doc:
-        for page in doc:
-            text += page.get_text()  # Extract text from each page
-    return text
-
-# Summarization function
-def summarize_pdf(pdf_file, max_summary_length):
-    # Extract text from the PDF
-    input_text = extract_text_from_pdf(pdf_file)
-
-    # Tokenize the input to check length
-    tokenized_input = new_tokenizer.encode(input_text, return_tensors='pt')
-
-
-
-    try:
-        # Generate the summary
-        summary_ids = new_model.generate(
-            tokenized_input,
-            max_length=max_summary_length,
-            min_length=30,
-            num_beams=15,
-            repetition_penalty=5.0,
-            no_repeat_ngram_size=2
-        )
-
-        # Decode the generated summary
-        summary = new_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-
-        # Clean up the summary to remove unwanted tokens
-        cleaned_summary = ' '.join([token for token in summary.split() if not token.startswith('<extra_id_')]).strip()
-
-        # Ensure the summary ends with a complete sentence
-        if cleaned_summary:
-            last_period_index = cleaned_summary.rfind('.')
-            if last_period_index != -1 and last_period_index < len(cleaned_summary) - 1:
-                cleaned_summary = cleaned_summary[:last_period_index + 1]
-            else:
-                cleaned_summary = cleaned_summary.strip()
-
-        return cleaned_summary if cleaned_summary else "No valid summary generated."
-
-    except Exception as e:
-        return str(e)  # Return the error message for debugging
-
-# Define the Gradio interface
-interface = gr.Interface(
-    fn=summarize_pdf,
-    inputs=[
-        gr.File(label="Upload PDF"),
-        gr.Slider(50, 300, step=10, label="Max summary length")
-    ],
-    outputs="textbox",  # A textbox for the output summary
-    title="PDF Text Summarizer",
-    description="Upload a PDF file to summarize its content."
-)
-
-# Launch the interface
-# Launch the interface with debug mode enabled
-interface.launch(debug=True)