Delete app_1.py

app_1.py

@@ -1,92 +0,0 @@
-import gradio as gr
-from PIL import Image
-import json
-from byaldi import RAGMultiModalModel
-from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
-from qwen_vl_utils import process_vision_info
-import torch
-import re
-
-# Load models
-def load_models():
-    RAG = RAGMultiModalModel.from_pretrained("vidore/colpali")
-    model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True, torch_dtype=torch.float32)  # float32 for CPU
-    processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True)
-    return RAG, model, processor
-
-RAG, model, processor = load_models()
-
-# Function for OCR and search
-def ocr_and_search(image, keyword):
-    text_query = "Extract all the text in Sanskrit and English from the image."
-    
-    # Prepare message for Qwen model
-    messages = [
-        {
-            "role": "user",
-            "content": [
-                {"type": "image", "image": image},
-                {"type": "text", "text": text_query},
-            ],
-        }
-    ]
-    
-    # Process the image
-    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-    image_inputs, video_inputs = process_vision_info(messages)
-    inputs = processor(
-        text=[text],
-        images=image_inputs,
-        videos=video_inputs,
-        padding=True,
-        return_tensors="pt",
-    ).to("cpu")  # Use CPU
-    
-    # Generate text
-    with torch.no_grad():
-        generated_ids = model.generate(**inputs, max_new_tokens=2000)
-    generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
-    extracted_text = processor.batch_decode(
-        generated_ids_trimmed,
-        skip_special_tokens=True,
-        clean_up_tokenization_spaces=False
-    )[0]
-    
-    # Perform keyword search with highlighting
-    keyword_lower = keyword.lower()
-    sentences = extracted_text.split('. ')
-    matched_sentences = []
-    for sentence in sentences:
-        if keyword_lower in sentence.lower():
-            highlighted_sentence = re.sub(
-                f'({re.escape(keyword)})',
-                r'<mark>\1</mark>',
-                sentence,
-                flags=re.IGNORECASE
-            )
-            matched_sentences.append(highlighted_sentence)
-    
-    return extracted_text, matched_sentences
-
-# Gradio App
-def app(image, keyword):
-    extracted_text, search_results = ocr_and_search(image, keyword)
-    search_results_str = "<br>".join(search_results) if search_results else "No matches found."
-    return extracted_text, search_results_str
-
-# Gradio Interface
-iface = gr.Interface(
-    fn=app,
-    inputs=[
-        gr.Image(type="pil", label="Upload an Image"),
-        gr.Textbox(label="Enter keyword to search in extracted text", placeholder="Keyword")
-    ],
-    outputs=[
-        gr.Textbox(label="Extracted Text"),
-        gr.HTML(label="Search Results"),
-    ],
-    title="OCR and Keyword Search in Images",
-)
-
-# Launch Gradio App
-iface.launch()