Create app.py

app.py

@@ -0,0 +1,177 @@
+import os
+import time
+from threading import Thread
+import gradio as gr
+import spaces
+from PIL import Image
+import torch
+from transformers import (
+    AutoProcessor,
+    AutoModelForImageTextToText,
+    Qwen2_5_VLForConditionalGeneration,
+    TextIteratorStreamer,
+)
+MODEL_PATHS = {
+    "Model 3 (structured handwritting)": (
+        "Emeritus-21/Finetuned-full-HTR-model",
+        AutoModelForImageTextToText,
+    ),
+}
+
+MAX_NEW_TOKENS_DEFAULT = 512
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# ---------------------------
+# Preload models at startup
+# ---------------------------
+_loaded_processors = {}
+_loaded_models = {}
+
+print("🚀 Preloading models into GPU/CPU memory...")
+
+for name, (repo_id, cls) in MODEL_PATHS.items():
+    try:
+        print(f"Loading {name} ...")
+        processor = AutoProcessor.from_pretrained(repo_id, trust_remote_code=True)
+        model = cls.from_pretrained(
+            repo_id,
+            trust_remote_code=True,
+            torch_dtype=torch.float16
+        ).to(device).eval()
+        _loaded_processors[name] = processor
+        _loaded_models[name] = model
+        print(f"✅ {name} ready.")
+    except Exception as e:
+        print(f"⚠️ Failed to load {name}: {e}")
+
+# ---------------------------
+# Warmup (GPU)
+# ---------------------------
+#@spaces.GPU
+def warmup():
+    try:
+        default_model_choice = list(MODEL_PATHS.keys())[0]
+        processor = _loaded_processors[default_model_choice]
+        model = _loaded_models[default_model_choice]
+
+        messages = [{"role": "user", "content": [{"type": "text", "text": "Warmup."}]}]
+        chat_prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        inputs = processor(text=[chat_prompt], images=None, return_tensors="pt").to(device)
+
+        with torch.inference_mode():
+            _ = model.generate(**inputs, max_new_tokens=1)
+
+        return f"GPU warm and {default_model_choice} ready."
+    except Exception as e:
+        return f"Warmup skipped: {e}"
+
+# ---------------------------
+# OCR Function (RAW ONLY)
+# ---------------------------
+#@spaces.GPU
+def ocr_image(image: Image.Image, model_choice: str, query: str = None,
+              max_new_tokens: int = MAX_NEW_TOKENS_DEFAULT,
+              temperature: float = 0.1, top_p: float = 1.0, top_k: int = 0, repetition_penalty: float = 1.0):
+
+    if image is None:
+        yield "Please upload an image."
+        return
+
+    if model_choice not in _loaded_models:
+        yield f"Invalid model: {model_choice}"
+        return
+
+    processor = _loaded_processors[model_choice]
+    model = _loaded_models[model_choice]
+
+    if query and query.strip():
+        prompt = query.strip()
+    else:
+        prompt = (
+            "You are a professional Handwritten OCR system.\n"
+            "TASK: Read the handwritten image and transcribe the text EXACTLY as written.\n"
+            "- Preserve original structure and line breaks.\n"
+            "- Keep spacing, bullet points, numbering, and indentation.\n"
+            "- Render tables as Markdown tables if present.\n"
+            "- Do NOT autocorrect spelling or grammar.\n"
+            "- Do NOT merge lines.\n"
+            "Return RAW transcription only."
+        )
+
+    messages = [{
+        "role": "user",
+        "content": [
+            {"type": "image", "image": image},
+            {"type": "text", "text": prompt}
+        ]
+    }]
+
+    chat_prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = processor(text=[chat_prompt], images=[image], return_tensors="pt").to(device)
+
+    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+
+    generation_kwargs = dict(
+        **inputs,
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        do_sample=False,
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        repetition_penalty=repetition_penalty
+    )
+
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+
+    buffer = ""
+    for new_text in streamer:
+        new_text = new_text.replace("<|im_end|>", "")
+        buffer += new_text
+        time.sleep(0.01)
+        yield buffer
+
+# ---------------------------
+# Gradio Interface
+# ---------------------------
+with gr.Blocks() as demo:
+    gr.Markdown("## wilson Handwritten OCR ")
+
+    model_choice = gr.Radio(
+        choices=list(MODEL_PATHS.keys()),
+        value=list(MODEL_PATHS.keys())[0],
+        label="Select OCR Model"
+    )
+
+    with gr.Tab("🖼 Image Inference"):
+        query_input = gr.Textbox(label="Custom Prompt (optional)", placeholder="Leave empty for RAW structured output")
+        image_input = gr.Image(type="pil", label="Upload Handwritten Image")
+
+        with gr.Accordion("⚙️ Advanced Options", open=False):
+            max_new_tokens = gr.Slider(1, 2048, value=MAX_NEW_TOKENS_DEFAULT, step=1, label="Max new tokens")
+            temperature = gr.Slider(0.1, 2.0, value=0.1, step=0.05, label="Temperature")
+            top_p = gr.Slider(0.05, 1.0, value=1.0, step=0.05, label="Top-p (nucleus)")
+            top_k = gr.Slider(0, 1000, value=0, step=1, label="Top-k")
+            repetition_penalty = gr.Slider(0.8, 2.0, value=1.0, step=0.05, label="Repetition penalty")
+
+        with gr.Row():
+            extract_btn = gr.Button("📤 Extract RAW Text", variant="primary")
+            clear_btn = gr.Button("🧹 Clear")
+
+        raw_output = gr.Textbox(label="📜 RAW Structured Output (exact as written)", lines=18, show_copy_button=True)
+
+        extract_btn.click(
+            fn=ocr_image,
+            inputs=[image_input, model_choice, query_input, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
+            outputs=[raw_output],
+            api_name="ocr_image"  # <--- THIS IS THE CRUCIAL FIX
+        )
+
+        clear_btn.click(
+            fn=lambda: ("", None, ""),
+            outputs=[raw_output, image_input, query_input]
+        )
+
+if __name__ == "__main__":
+    demo.queue(max_size=50).launch(share=True, ssr_mode=False, show_error=True)