Delete app.py

app.py

@@ -1,91 +0,0 @@
-import gradio as gr
-import fitz  # PyMuPDF
-import openai
-import os
-
-# Load API key from Hugging Face secret
-openai.api_key = os.getenv("OPENAI_API_KEY")
-
-# Extract text from PDF
-def extract_text_from_pdf(file):
-    text = ""
-    with fitz.open(stream=file.read(), filetype="pdf") as doc:
-        for page in doc:
-            text += page.get_text()
-    return text
-
-# Split text into chunks
-def split_text(text, max_tokens=1500):
-    import textwrap
-    return textwrap.wrap(text, max_tokens)
-
-# Generate summary
-def generate_summary(text):
-    chunks = split_text(text)
-    summaries = []
-    for i, chunk in enumerate(chunks):
-        print(f"⏳ Summarizing chunk {i + 1}/{len(chunks)}...")
-        response = openai.ChatCompletion.create(
-            model="gpt-3.5-turbo",
-            messages=[{"role": "user", "content": f"Summarize the following:\n{chunk}"}],
-            temperature=0.5,
-        )
-        summaries.append(response['choices'][0]['message']['content'].strip())
-    return "\n\n".join(summaries)
-
-# Generate all formats
-def summarize_paper(pdf_file):
-    try:
-        raw_text = extract_text_from_pdf(pdf_file)
-        summary = generate_summary(raw_text)
-
-        response = openai.ChatCompletion.create(
-            model="gpt-3.5-turbo",
-            messages=[
-                {"role": "user", "content": f"Make an ELI5 version of this:\n{summary}"}
-            ],
-            temperature=0.7,
-        )
-        eli5 = response['choices'][0]['message']['content'].strip()
-
-        response = openai.ChatCompletion.create(
-            model="gpt-3.5-turbo",
-            messages=[
-                {"role": "user", "content": f"Write a TL;DR of this:\n{summary}"}
-            ],
-            temperature=0.7,
-        )
-        tldr = response['choices'][0]['message']['content'].strip()
-
-        response = openai.ChatCompletion.create(
-            model="gpt-3.5-turbo",
-            messages=[
-                {"role": "user", "content": f"Why does this research matter? {summary}"}
-            ],
-            temperature=0.7,
-        )
-        why_it_matters = response['choices'][0]['message']['content'].strip()
-
-        return summary, eli5, tldr, why_it_matters
-    except Exception as e:
-        return f"❌ Error: {str(e)}", "", "", ""
-
-# Gradio Interface
-with gr.Blocks() as demo:
-    gr.Markdown("📄 **Paper News Summarizer**\nUpload a research paper PDF and get a human-friendly summary, ELI5, TL;DR, and why it matters.")
-    with gr.Row():
-        pdf_file = gr.File(label="Upload Research Paper (PDF)", file_types=[".pdf"])
-    submit_btn = gr.Button("Submit")
-    clear_btn = gr.Button("Clear")
-
-    with gr.Row():
-        full_summary = gr.Textbox(label="📘 Full Summary", lines=10, interactive=False)
-        eli5_summary = gr.Textbox(label="🧒 ELI5", lines=5, interactive=False)
-    with gr.Row():
-        tldr_summary = gr.Textbox(label="⚡ TL;DR", lines=2, interactive=False)
-        importance = gr.Textbox(label="🎯 Why It Matters", lines=5, interactive=False)
-
-    submit_btn.click(summarize_paper, inputs=pdf_file, outputs=[full_summary, eli5_summary, tldr_summary, importance])
-    clear_btn.click(lambda: ("", "", "", ""), outputs=[full_summary, eli5_summary, tldr_summary, importance])
-
-demo.launch()