Create app.py

app.py

@@ -0,0 +1,99 @@
+import gradio as gr
+import fitz
+import torch
+from transformers import pipeline
+import time, logging, re
+import matplotlib.pyplot as plt
+import io
+
+logging.basicConfig(level=logging.ERROR)
+device = -1  # CPU-only
+print("⚠️ CPU-only. Expect ~20–30s for 300,000 chars.")
+
+try:
+    summarizer = pipeline("summarization", model="t5-small", device=device, torch_dtype=torch.float32)
+except Exception as e:
+    print(f"❌ Model loading failed: {str(e)}")
+    exit(1)
+
+def visualize_chunk_status(chunk_data):
+    status_colors = {'summarized': 'green', 'skipped': 'orange', 'error': 'red'}
+    labels = [f"C{i['chunk']}" for i in chunk_data]
+    colors = [status_colors.get(i['status'], 'gray') for i in chunk_data]
+    times = [i.get('time', 0.1) for i in chunk_data]  # Avoid zero-time bars
+
+    fig, ax = plt.subplots(figsize=(10, 2.5))
+    ax.barh(labels, times, color=colors)
+    ax.set_xlabel("Time (s)")
+    ax.set_title("📊 Chunk Processing Status")
+    plt.tight_layout()
+
+    buf = io.BytesIO()
+    plt.savefig(buf, format='png')
+    buf.seek(0)
+    return buf
+
+def summarize_file(file_bytes):
+    start = time.time()
+    chunk_info = []
+
+    try:
+        doc = fitz.open(stream=file_bytes, filetype="pdf")
+        text = "".join(page.get_text("text") for page in doc)
+        text = re.sub(r"\$\s*([^$]+)\s*\$", r"\1", text)
+        text = re.sub(r"\\cap", "intersection", text)
+        text = re.sub(r"\s+", " ", text).strip()
+        text = "".join(c for c in text if ord(c) < 128)
+    except Exception as e:
+        return f"❌ Text extraction failed: {str(e)}", None
+
+    if not text.strip():
+        return "❌ No text found", None
+
+    text = text[:300000]
+    chunks = [text[i:i+2000] for i in range(0, len(text), 2000)]
+    summaries = []
+
+    for i, chunk in enumerate(chunks):
+        chunk_start = time.time()
+        chunk_result = {'chunk': i+1, 'status': '', 'time': 0}
+
+        if time.time() - start > 20:
+            summaries.append("⚠️ Stopped early")
+            break
+
+        if sum(1 for c in chunk if not c.isalnum()) / len(chunk) > 0.5:
+            summaries.append(f"**Chunk {i+1}**: Skipped (equation-heavy)")
+            chunk_result['status'] = 'skipped'
+        else:
+            try:
+                summary = summarizer(chunk, max_length=60, min_length=10, do_sample=False)[0]['summary_text']
+                summaries.append(f"**Chunk {i+1}**:\n{summary}")
+                chunk_result['status'] = 'summarized'
+            except Exception as e:
+                summaries.append(f"**Chunk {i+1}**: ❌ Error: {str(e)}")
+                chunk_result['status'] = 'error'
+
+        chunk_result['time'] = time.time() - chunk_start
+        chunk_info.append(chunk_result)
+
+    final_summary = f"**Chars**: {len(text)}\n**Time**: {time.time()-start:.2f}s\n\n" + "\n\n".join(summaries)
+    image_buf = visualize_chunk_status(chunk_info)
+    return final_summary, image_buf
+
+demo = gr.Interface(
+    fn=summarize_file,
+    inputs=gr.File(label="📄 Upload PDF", type="binary"),
+    outputs=[
+        gr.Textbox(label="📝 Summarized Output"),
+        gr.Image(label="📊 Visual Process Flow")
+    ],
+    title="AI-Powered PDF Summarizer",
+    description="Summarizes long PDFs (up to 300,000 characters) and visualizes chunk-level automation status."
+)
+
+if __name__ == "__main__":
+    try:
+        demo.launch(share=False, server_port=7860)
+    except Exception as e:
+        print(f"❌ Gradio launch failed: {str(e)}")