Create app.py

app.py

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+from transformers import AutoModel, AutoTokenizer, pipeline
+import gradio as gr
+from PIL import Image
+
+# Load OCR model
+tokenizer = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True)
+model = AutoModel.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True, device_map='cuda', low_cpu_mem_usage=True)
+
+# Summarization model
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+# Question-Answering model (English)
+pipe_qa_en = pipeline('question-answering', model="deepset/roberta-base-squad2")
+
+# Question-Answering model (Arabic)
+pipe_qa_ar = pipeline("question-answering", model="gp-tar4/QA_FineTuned")
+
+# Translation model (English to Arabic)
+pipe_to_arabic = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ar")
+
+
+def summarize(text):
+    # Summarize the text
+    summary = summarizer(text, max_length=200, min_length=30, do_sample=False)
+    return summary[0]['summary_text']
+
+
+def question_answering(question, context, language='english'):
+    QA_input = {'question': question, 'context': context}
+    if language == 'arabic':
+        return pipe_qa_ar(QA_input)['answer']
+    return pipe_qa_en(QA_input)['answer']
+
+
+def to_arabic(text, max_length=512):
+    # Split the text into chunks if it is more than 512 characters
+    chunks = [text[i:i+max_length] for i in range(0, len(text), max_length)]
+    translated_chunks = [pipe_to_arabic(chunk)[0]['translation_text'] for chunk in chunks]
+    return ' '.join(translated_chunks)
+
+
+def process_image_and_text(image, text):
+    ocr_text = model.chat(tokenizer, image, ocr_type='ocr')
+    summarized_text = summarize(ocr_text)
+    return f"Input text: {text}\n\nProcessed OCR text: {summarized_text}"
+
+
+def process_image_qa(language, image, question):
+    ocr_text = model.chat(tokenizer, image, ocr_type='ocr')
+    if language == 'arabic':
+        translated_text = to_arabic(ocr_text)
+        return question_answering(question, translated_text, language='arabic')
+    return question_answering(question, ocr_text)
+
+
+# Gradio interfaces
+summarization_Interface = gr.Interface(
+    fn=process_image_and_text,
+    inputs=[gr.Image(type="filepath", label="Upload Image"), gr.Textbox(label="Input Text")],
+    outputs=gr.Textbox(label="Output Text"),
+    title="OCR & Summarization",
+    description="Upload an image and provide some text for summarization."
+)
+
+qa_Interface = gr.Interface(
+    fn=process_image_qa,
+    inputs=[gr.Radio(['Arabic', 'English'], label='Select Language', value='Arabic'), gr.Image(type="filepath", label="Upload Image"), gr.Textbox(label="Input Question")],
+    outputs=gr.Textbox(label="Answer Text"),
+    title="OCR & Question Answering",
+    description="Upload an image and ask a question in English or Arabic."
+)
+
+# Combine both interfaces into a tabbed interface
+apps_interface = gr.TabbedInterface([summarization_Interface, qa_Interface], tab_names=["Summarization", "Question Answering"])
+
+# Launch the app
+apps_interface.launch()