Update app (1).py

app (1).py

@@ -1,231 +1,65 @@
-
-# import os
-# import sys
-# import numpy as np
-# import cv2
-
-# # ==========================================
-# # 🔧 PATCH 1: Fix Torchvision Compatibility
-# # ==========================================
-# import torchvision.models.vgg
-# if not hasattr(torchvision.models.vgg, 'model_urls'):
-#     torchvision.models.vgg.model_urls = {
-#         'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth'
-#     }
-
-# # ==========================================
-# # 🔧 PATCH 2: Fix NumPy Crash AND Coordinates
-# # ==========================================
-# import craft_text_detector.craft_utils as craft_utils_module
-
-# # CRITICAL FIX: Added 'ratio_net=2' to the signature and calculation.
-# # Without this, your boxes are 2x smaller than they should be.
-# def fixed_adjustResultCoordinates(polys, ratio_w, ratio_h, ratio_net=2):
-#     if not polys:
-#         return []
-    
-#     adjusted = []
-#     for poly in polys:
-#         if poly is None or len(poly) == 0:
-#             continue
-        
-#         # Convert to numpy array safely
-#         poly = np.array(poly).reshape(-1, 2)
-        
-#         # Scale coordinates: 
-#         # 1. ratio_net scales from Heatmap -> Resized Image
-#         # 2. ratio_w/h scales from Resized Image -> Original Image
-#         poly[:, 0] *= (ratio_w * ratio_net)
-#         poly[:, 1] *= (ratio_h * ratio_net)
-        
-#         adjusted.append(poly)
-    
-#     return adjusted
-
-# # Apply the patch
-# craft_utils_module.adjustResultCoordinates = fixed_adjustResultCoordinates
-# # ==========================================
-
-# import gradio as gr
-# from craft_hw_ocr import OCR
-
-# print("⏳ Loading OCR models...")
-# ocr = OCR.load_models()
-# print("✅ Models loaded!")
-
-# def do_ocr(inp):
-#     if inp is None:
-#         return None, "No image uploaded."
-    
-#     try:
-#         # Detection
-#         # 'detected_img' is usually just the input image in this library
-#         detected_img, results = OCR.detection(inp, ocr[2])
-        
-#         # Visualization
-#         # Now that coordinates are scaled correctly, this should look right
-#         viz_img = OCR.visualize(inp, results)
-        
-#         # Recognition
-#         try:
-#             # Note: The library has a typo 'recoginition' (extra 'i')
-#             bboxes, text = OCR.recoginition(detected_img, results, ocr[0], ocr[1])
-#         except Exception as e:
-#             print(f"Recognition error: {e}")
-#             text = f"Detection successful, but recognition failed: {str(e)}"
-        
-#         return viz_img, text
-        
-#     except Exception as e:
-#         print(f"OCR error: {e}")
-#         return None, f"Error processing image: {str(e)}"
-  
-# inputs = gr.Image(label="Upload Image")
-# o1 = gr.Image(label="Detections")
-# o2 = gr.Textbox(label="Text")
-
-# title = "CRAFT-OCR (Fixed Coords)"
-# description = "Handwriting OCR using CRAFT + TrOCR. Patched for NumPy and Coordinates."
-
-# gr.Interface(
-#     fn=do_ocr, 
-#     inputs=inputs, 
-#     outputs=[o1, o2], 
-#     title=title, 
-#     description=description
-# ).launch()
-
-
-
-
-
-
-
-import os
-import sys
-import numpy as np
-import cv2
-
-# ==========================================
-# 🔧 PATCH 1: Fix Torchvision Compatibility
-# ==========================================
-import torchvision.models.vgg
-if not hasattr(torchvision.models.vgg, 'model_urls'):
-    torchvision.models.vgg.model_urls = {
-        'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth'
-    }
-
-# ==========================================
-# 🔧 PATCH 2: Fix NumPy Crash AND Coordinates
-# ==========================================
-import craft_text_detector.craft_utils as craft_utils_module
-
-def fixed_adjustResultCoordinates(polys, ratio_w, ratio_h, ratio_net=2):
-    if not polys:
-        return []
-    
-    adjusted = []
-    for poly in polys:
-        if poly is None or len(poly) == 0:
-            continue
-        
-        # Convert to numpy array safely
-        poly = np.array(poly).reshape(-1, 2)
-        
-        # Scale coordinates correctly
-        poly[:, 0] *= (ratio_w * ratio_net)
-        poly[:, 1] *= (ratio_h * ratio_net)
-        
-        adjusted.append(poly)
-    
-    return adjusted
-
-craft_utils_module.adjustResultCoordinates = fixed_adjustResultCoordinates
-# ==========================================
-
 import gradio as gr
-from craft_hw_ocr import OCR
-# Import the core prediction function to bypass the wrapper limitations
-from craft_text_detector.predict import get_prediction
-
-print("⏳ Loading OCR models...")
-ocr = OCR.load_models()
-# ocr[2] is the Craft object wrapper
-craft_wrapper = ocr[2]
-# We extract the actual networks to run them manually
-craft_net = craft_wrapper.craft_net
-refine_net = craft_wrapper.refine_net
-print("✅ Models loaded!")
-
-def do_ocr(inp, text_threshold, link_threshold, low_text):
-    if inp is None:
-        return None, "No image uploaded."
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+import torch
+from PIL import Image
+
+# --- Model Setup ---
+# We load the model outside the inference function to cache it on startup
+MODEL_ID = "microsoft/trocr-small-handwritten"
+
+print(f"Loading {MODEL_ID}...")
+processor = TrOCRProcessor.from_pretrained(MODEL_ID)
+model = VisionEncoderDecoderModel.from_pretrained(MODEL_ID)
+
+# Check for GPU (Free Spaces are usually CPU-only, but this handles upgrades)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+print(f"Model loaded on device: {device}")
+
+# --- Inference Function ---
+def process_image(image):
+    if image is None:
+        return "Please upload an image."
     
     try:
-        print(f"⚙️ Running Direct Inference: Text={text_threshold}, Link={link_threshold}, Low={low_text}")
-        
-        # 1. Direct Detection (Bypassing Craft.detect_text)
-        # This calls the engine directly, which accepts all our sliders.
-        prediction_result = get_prediction(
-            image=inp,
-            craft_net=craft_net,
-            refine_net=refine_net,
-            text_threshold=text_threshold,
-            link_threshold=link_threshold,
-            low_text=low_text,
-            cuda=False,  # Space is CPU
-            poly=True
-        )
-        
-        # 2. Visualization
-        # OCR.visualize expects (image, results_dict)
-        viz_img = OCR.visualize(inp, prediction_result)
+        # 1. Convert to RGB (standardizes input)
+        image = image.convert("RGB")
         
-        # 3. Recognition
-        try:
-            bboxes, text = OCR.recoginition(inp, prediction_result, ocr[0], ocr[1])
-        except Exception as e:
-            text = f"Detection successful, but recognition failed: {e}"
+        # 2. Preprocess
+        pixel_values = processor(images=image, return_tensors="pt").pixel_values.to(device)
         
-        return viz_img, text
+        # 3. Generate text
+        generated_ids = model.generate(pixel_values)
+        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
         
+        return generated_text
     except Exception as e:
-        print(f"OCR error: {e}")
-        return None, f"Error processing image: {str(e)}"
-
-# ------------------------------------------------------
-# 🎛️ UI with Tuning Sliders
-# ------------------------------------------------------
-with gr.Blocks(title="CRAFT-OCR Tuner") as demo:
-    gr.Markdown("## 🔧 CRAFT-OCR Parameter Tuner")
-    gr.Markdown("Adjust sliders to fix issues like merged words or background noise.")
+        return f"Error: {str(e)}"
+
+# --- Gradio Interface ---
+# Using the Blocks API for a clean layout
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # ✍️ Handwritten Text Recognition
+        Using Microsoft's **TrOCR Small** model. Upload a handwritten note to transcribe it.
+        """
+    )
     
     with gr.Row():
-        with gr.Column(scale=1):
-            input_img = gr.Image(label="Upload Image")
-            
-            gr.Markdown("### 🎛️ Fine-Tune Detection")
-            
-            text_thres = gr.Slider(0.1, 0.9, value=0.7, step=0.05, label="Text Threshold", 
-                                   info="Confidence to consider a pixel as text. Higher = Less Noise.")
-            
-            link_thres = gr.Slider(0.1, 0.9, value=0.4, step=0.05, label="Link Threshold", 
-                                   info="Confidence to link characters. HIGHER value splits merged words (Fixes 'Hamburgthen').")
-            
-            low_text = gr.Slider(0.1, 0.9, value=0.4, step=0.05, label="Low Text Threshold", 
-                                 info="Filters background noise. Higher = Cleaner background.")
-            
-            btn = gr.Button("Run OCR", variant="primary")
+        with gr.Column():
+            input_img = gr.Image(type="pil", label="Upload Image")
+            submit_btn = gr.Button("Transcribe", variant="primary")
         
-        with gr.Column(scale=1):
-            viz_output = gr.Image(label="Detections (Verify Boxes)")
-            text_output = gr.Textbox(label="Recognized Text", lines=10)
+        with gr.Column():
+            output_text = gr.Textbox(label="Result", interactive=False)
+            
+    # Examples help users test it immediately without uploading their own file
+    # (Uncomment the list below if you upload example images to your repo)
+    # gr.Examples(["sample1.jpg"], inputs=input_img)
 
-    btn.click(
-        fn=do_ocr, 
-        inputs=[input_img, text_thres, link_thres, low_text], 
-        outputs=[viz_output, text_output]
-    )
+    submit_btn.click(fn=process_image, inputs=input_img, outputs=output_text)
 
+# Launch for Spaces
 if __name__ == "__main__":
     demo.launch()