Initial PDF evaluator app

app.py

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+import gradio as gr
+import torch
+from pypdf import PdfReader
+from PIL import Image
+import io
+
+from transformers import (
+    TrOCRProcessor,
+    VisionEncoderDecoderModel,
+    AutoTokenizer,
+    AutoModelForCausalLM
+)
+
+# ============================================================
+# Device
+# ============================================================
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# ============================================================
+# Load Models (cached by HF Spaces)
+# ============================================================
+ocr_processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-printed")
+ocr_model = VisionEncoderDecoderModel.from_pretrained(
+    "microsoft/trocr-base-printed"
+).to(device)
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "Qwen/Qwen2.5-1.5B-Instruct",
+    trust_remote_code=True
+)
+
+qwen_model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen2.5-1.5B-Instruct",
+    device_map="auto",
+    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+    trust_remote_code=True
+)
+
+# ============================================================
+# Helpers
+# ============================================================
+def is_scanned_pdf(reader):
+    for page in reader.pages:
+        if page.extract_text():
+            return False
+    return True
+
+def extract_text_from_pdf(file):
+    reader = PdfReader(file)
+    scanned = is_scanned_pdf(reader)
+
+    extracted_text = []
+
+    if not scanned:
+        # Digital PDF
+        for page in reader.pages:
+            text = page.extract_text()
+            if text:
+                extracted_text.append(text)
+
+    else:
+        # OCR only embedded images (HF-safe)
+        for page in reader.pages:
+            if "/XObject" in page["/Resources"]:
+                xobjects = page["/Resources"]["/XObject"].get_object()
+                for obj in xobjects:
+                    xobj = xobjects[obj]
+                    if xobj["/Subtype"] == "/Image":
+                        image = Image.open(io.BytesIO(xobj.get_data())).convert("RGB")
+                        pixel_values = ocr_processor(
+                            images=image,
+                            return_tensors="pt"
+                        ).pixel_values.to(device)
+
+                        with torch.no_grad():
+                            ids = ocr_model.generate(pixel_values)
+
+                        text = ocr_processor.batch_decode(
+                            ids,
+                            skip_special_tokens=True
+                        )[0]
+                        extracted_text.append(text)
+
+    return "\n\n".join(extracted_text)
+
+def evaluate_text(text):
+    prompt = f"""
+You are a strict academic evaluator.
+
+Evaluate the following document and assign marks out of 10.
+
+Criteria:
+- Clarity
+- Structure
+- Technical depth
+- Language quality
+- Completeness
+
+DOCUMENT:
+---------
+{text[:6000]}
+---------
+
+Respond strictly in this format:
+
+Score: X/10
+Justification:
+Strengths:
+Weaknesses:
+"""
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+
+    with torch.no_grad():
+        output = qwen_model.generate(
+            **inputs,
+            max_new_tokens=400,
+            do_sample=False
+        )
+
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+# ============================================================
+# Gradio Function
+# ============================================================
+def process_pdf(pdf_file):
+    extracted_text = extract_text_from_pdf(pdf_file)
+    evaluation = evaluate_text(extracted_text)
+
+    return extracted_text, evaluation
+
+# ============================================================
+# Gradio UI
+# ============================================================
+with gr.Blocks(title="PDF Evaluator (OCR + Qwen)") as demo:
+    gr.Markdown("""
+    # 📄 PDF Evaluator
+    Upload a PDF to:
+    - Extract text (OCR if needed)
+    - Evaluate content using Qwen
+    - Get marks out of 10
+    """)
+
+    pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"])
+    extract_btn = gr.Button("Extract & Evaluate")
+
+    extracted_output = gr.Textbox(
+        label="Extracted Text",
+        lines=20
+    )
+
+    evaluation_output = gr.Textbox(
+        label="Evaluation",
+        lines=10
+    )
+
+    extract_btn.click(
+        process_pdf,
+        inputs=pdf_input,
+        outputs=[extracted_output, evaluation_output]
+    )
+
+demo.launch()

requirements.txt

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+torch
+transformers
+accelerate
+pypdf
+pillow
+gradio
+sentencepiece