Create app.py

app.py

@@ -0,0 +1,102 @@
+import gradio as gr
+import pandas as pd
+import torch
+from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
+from PIL import Image
+import io
+from datetime import datetime
+
+# โหลดโมเดลและ processor จาก Hugging Face
+model_name = "Qwen/Qwen2-VL-7B-Instruct"
+processor = AutoProcessor.from_pretrained(model_name)
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+def extract_data_from_image(images):
+    results = []
+
+    for idx, img_file in enumerate(images):
+        try:
+            image = Image.open(io.BytesIO(img_file.read())).convert("RGB")
+
+            # Prompt บอกโมเดลว่าให้ทำอะไร
+            prompt = """
+            กรุณาสกัดข้อมูลสำคัญจากเอกสารนี้:
+            - วันที่
+            - ยอดรวม
+            - ชื่อร้านค้า
+            - เลขใบเสร็จ
+            
+            กรุณาตอบในรูปแบบ JSON
+            """
+
+            messages = [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "image"},
+                        {"type": "text", "text": prompt}
+                    ]
+                }
+            ]
+
+            text_prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+            inputs = processor(text=text_prompt, images=image, return_tensors="pt").to(model.device).bfloat16()
+
+            with torch.no_grad():
+                generated_ids = model.generate(**inputs, max_new_tokens=512)
+
+            generated_ids_trimmed = [out_ids[len(inputs["input_ids"][0]):] for out_ids in generated_ids]
+            answer = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+
+            try:
+                structured = eval(answer.replace("```json", "").replace("```", ""))
+            except:
+                structured = {"raw_response": answer}
+
+            results.append({
+                "file_name": img_file.name,
+                "data": str(structured),
+                "timestamp": datetime.now().isoformat()
+            })
+
+        except Exception as e:
+            results.append({
+                "file_name": img_file.name,
+                "data": f"เกิดข้อผิดพลาด: {str(e)}",
+                "timestamp": datetime.now().isoformat()
+            })
+
+    df = pd.DataFrame(results)
+    df["structured_data"] = df["data"].astype(str)
+
+    # บันทึกเป็น Parquet
+    parquet_path = "output.parquet"
+    df.to_parquet(parquet_path)
+
+    return {
+        "table": df[["file_name", "structured_data"]],
+        "download": parquet_path
+    }
+
+# UI Components
+title = "📄 ระบบสกัดข้อมูลเอกสารอัตโนมัติ (รองรับภาษาไทย)"
+description = "อัปโหลดภาพหลายไฟล์ → สกัดข้อมูล → แยกหัวข้อ → บันทึกเป็น Parquet"
+
+interface = gr.Interface(
+    fn=extract_data_from_image,
+    inputs=gr.File(type="file", file_types=["image"], multiple=True),
+    outputs=[
+        gr.Dataframe(label="ผลลัพธ์"),
+        gr.File(label="ดาวน์โหลด Parquet")
+    ],
+    title=title,
+    description=description,
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    interface.launch()