Update app.py

app.py

@@ -1,313 +1,31 @@
 import gradio as gr
-import spaces
-from transformers import Qwen2VLForConditionalGeneration, AutoProcessor, TextIteratorStreamer
-from qwen_vl_utils import process_vision_info
-import torch
+from torchvision import models, transforms
 from PIL import Image
-import os
-import uuid
-import io
-from threading import Thread
-from reportlab.lib.pagesizes import A4
-from reportlab.lib.styles import getSampleStyleSheet
-from reportlab.lib import colors
-from reportlab.platypus import SimpleDocTemplate, Image as RLImage, Paragraph, Spacer
-from reportlab.lib.units import inch
-from reportlab.pdfbase import pdfmetrics
-from reportlab.pdfbase.ttfonts import TTFont
-import docx
-from docx.enum.text import WD_ALIGN_PARAGRAPH
-
-# Define model options
-MODEL_OPTIONS = {
-    "Qwen2VL Base": "Qwen/Qwen2-VL-2B-Instruct",
-    "Latex OCR": "prithivMLmods/Qwen2-VL-OCR-2B-Instruct",
-    "Math Prase": "prithivMLmods/Qwen2-VL-Math-Prase-2B-Instruct",
-    "Text Analogy Ocrtest": "prithivMLmods/Qwen2-VL-Ocrtest-2B-Instruct"
-}
-
-# Preload models and processors into CPU
-models = {}
-processors = {}
-for name, model_id in MODEL_OPTIONS.items():
-    print(f"Loading {name}...")
-    models[name] = Qwen2VLForConditionalGeneration.from_pretrained(
-        model_id,
-        trust_remote_code=True,
-        torch_dtype=torch.float32  # Use float32 to ensure CPU usage
-    ).eval()  # No `.to('cuda')`, will run on CPU by default
-    processors[name] = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
-
-image_extensions = Image.registered_extensions()
-
-def identify_and_save_blob(blob_path):
-    """Identifies if the blob is an image and saves it."""
-    try:
-        with open(blob_path, 'rb') as file:
-            blob_content = file.read()
-            try:
-                Image.open(io.BytesIO(blob_content)).verify()  # Check if it's a valid image
-                extension = ".png"  # Default to PNG for saving
-                media_type = "image"
-            except (IOError, SyntaxError):
-                raise ValueError("Unsupported media type. Please upload a valid image.")
-
-            filename = f"temp_{uuid.uuid4()}_media{extension}"
-            with open(filename, "wb") as f:
-                f.write(blob_content)
-
-            return filename, media_type
-
-    except FileNotFoundError:
-        raise ValueError(f"The file {blob_path} was not found.")
-    except Exception as e:
-        raise ValueError(f"An error occurred while processing the file: {e}")
-
-def qwen_inference(model_name, media_input, text_input=None):
-    """Handles inference for the selected model on CPU."""
-    model = models[model_name]
-    processor = processors[model_name]
-
-    if isinstance(media_input, str):
-        media_path = media_input
-        if media_path.endswith(tuple([i for i in image_extensions.keys()])):
-            media_type = "image"
-        else:
-            try:
-                media_path, media_type = identify_and_save_blob(media_input)
-            except Exception as e:
-                raise ValueError("Unsupported media type. Please upload a valid image.")
-
-    messages = [
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": media_type,
-                    media_type: media_path
-                },
-                {"type": "text", "text": text_input},
-            ],
-        }
-    ]
-
-    text = processor.apply_chat_template(
-        messages, tokenize=False, add_generation_prompt=True
-    )
-    image_inputs, _ = process_vision_info(messages)
-    inputs = processor(
-        text=[text],
-        images=image_inputs,
-        padding=True,
-        return_tensors="pt",
-    )
-
-    # Ensure model runs on CPU
-    streamer = TextIteratorStreamer(
-        processor.tokenizer, skip_prompt=True, skip_special_tokens=True
-    )
-    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)
-
-    thread = Thread(target=model.generate, kwargs=generation_kwargs)
-    thread.start()
-
-    buffer = ""
-    for new_text in streamer:
-        buffer += new_text
-        # Remove <|im_end|> or similar tokens from the output
-        buffer = buffer.replace("<|im_end|>", "")
-        yield buffer
-
-def format_plain_text(output_text):
-    """Formats the output text as plain text without LaTeX delimiters."""
-    plain_text = output_text.replace("\\(", "").replace("\\)", "").replace("\\[", "").replace("\\]", "")
-    return plain_text
-
-def generate_document(media_path, output_text, file_format, font_choice, font_size, line_spacing, alignment, image_size):
-    """Generates a document with the input image and plain text output."""
-    plain_text = format_plain_text(output_text)
-    if file_format == "pdf":
-        return generate_pdf(media_path, plain_text, font_choice, font_size, line_spacing, alignment, image_size)
-    elif file_format == "docx":
-        return generate_docx(media_path, plain_text, font_choice, font_size, line_spacing, alignment, image_size)
-
-def generate_pdf(media_path, plain_text, font_choice, font_size, line_spacing, alignment, image_size):
-    """Generates a PDF document."""
-    filename = f"output_{uuid.uuid4()}.pdf"
-    doc = SimpleDocTemplate(
-        filename,
-        pagesize=A4,
-        rightMargin=inch,
-        leftMargin=inch,
-        topMargin=inch,
-        bottomMargin=inch
-    )
-    styles = getSampleStyleSheet()
-    styles["Normal"].fontName = font_choice
-    styles["Normal"].fontSize = int(font_size)
-    styles["Normal"].leading = int(font_size) * line_spacing
-    styles["Normal"].alignment = {
-        "Left": 0,
-        "Center": 1,
-        "Right": 2,
-        "Justified": 4
-    }[alignment]
-
-    font_path = f"font/{font_choice}"
-    pdfmetrics.registerFont(TTFont(font_choice, font_path))
-
-    story = []
-
-    image_sizes = {
-        "Small": (200, 200),
-        "Medium": (400, 400),
-        "Large": (600, 600)
-    }
-    img = RLImage(media_path, width=image_sizes[image_size][0], height=image_sizes[image_size][1])
-    story.append(img)
-    story.append(Spacer(1, 12))
-
-    text = Paragraph(plain_text, styles["Normal"])
-    story.append(text)
-
-    doc.build(story)
-    return filename
-
-def generate_docx(media_path, plain_text, font_choice, font_size, line_spacing, alignment, image_size):
-    """Generates a DOCX document."""
-    filename = f"output_{uuid.uuid4()}.docx"
-    doc = docx.Document()
-
-    image_sizes = {
-        "Small": docx.shared.Inches(2),
-        "Medium": docx.shared.Inches(4),
-        "Large": docx.shared.Inches(6)
-    }
-    doc.add_picture(media_path, width=image_sizes[image_size])
-    doc.add_paragraph()
-
-    paragraph = doc.add_paragraph()
-    paragraph.paragraph_format.line_spacing = line_spacing
-    paragraph.paragraph_format.alignment = {
-        "Left": WD_ALIGN_PARAGRAPH.LEFT,
-        "Center": WD_ALIGN_PARAGRAPH.CENTER,
-        "Right": WD_ALIGN_PARAGRAPH.RIGHT,
-        "Justified": WD_ALIGN_PARAGRAPH.JUSTIFY
-    }[alignment]
-    run = paragraph.add_run(plain_text)
-    run.font.name = font_choice
-    run.font.size = docx.shared.Pt(int(font_size))
-
-    doc.save(filename)
-    return filename
-
-# Gradio app setup
-with gr.Blocks() as demo:
-    gr.Markdown("# Qwen2VL Models: Vision and Language Processing")
-
-    with gr.Tab(label="Image Input"):
-
-        with gr.Row():
-            with gr.Column():
-                model_choice = gr.Dropdown(
-                    label="Model Selection",
-                    choices=list(MODEL_OPTIONS.keys()),
-                    value="Latex OCR"
-                )
-                input_media = gr.File(
-                    label="Upload Image", type="filepath" 
-                )
-                text_input = gr.Textbox(label="Question", placeholder="Ask a question about the image...")
-                submit_btn = gr.Button(value="Submit")
-
-            with gr.Column():
-                output_text = gr.Textbox(label="Output Text", lines=10)
-                plain_text_output = gr.Textbox(label="Standardized Plain Text", lines=10)
-
-        submit_btn.click(
-            qwen_inference, [model_choice, input_media, text_input], [output_text]
-        ).then(
-            lambda output_text: format_plain_text(output_text), [output_text], [plain_text_output]
-        )
-
-        with gr.Row():
-            gr.Examples(
-                examples=[
-                    ["examples/1.png", "summarize the letter", "Text Analogy Ocrtest"],
-                    ["examples/2.jpg", "Summarize the full image in detail", "Latex OCR"],
-                    ["examples/3.png", "Describe the photo", "Qwen2VL Base"],
-                    ["examples/4.png", "summarize and solve the problem", "Math Prase"],
-                ],
-                inputs=[input_media, text_input, model_choice],
-                outputs=[output_text, plain_text_output],
-                fn=lambda img, question, model: qwen_inference(model, img, question),
-                cache_examples=False,
-            )
-
-        with gr.Row():
-            with gr.Column():
-                line_spacing = gr.Dropdown(
-                    choices=[0.5, 1.0, 1.15, 1.5, 2.0, 2.5, 3.0],
-                    value=1.5,
-                    label="Line Spacing"
-                )
-                font_size = gr.Dropdown(
-                    choices=["8", "10", "12", "14", "16", "18", "20", "22", "24"],
-                    value="18",
-                    label="Font Size"
-                )
-                font_choice = gr.Dropdown(
-                    choices=[
-                        "DejaVuMathTeXGyre.ttf", 
-                        "FiraCode-Medium.ttf", 
-                        "InputMono-Light.ttf",
-                        "JetBrainsMono-Thin.ttf", 
-                        "ProggyCrossed Regular Mac.ttf", 
-                        "SourceCodePro-Black.ttf", 
-                        "arial.ttf", 
-                        "calibri.ttf", 
-                        "mukta-malar-extralight.ttf", 
-                        "noto-sans-arabic-medium.ttf", 
-                        "times new roman.ttf",
-                        "ANGSA.ttf",
-                        "Book-Antiqua.ttf",
-                        "CONSOLA.TTF",
-                        "COOPBL.TTF",
-                        "Rockwell-Bold.ttf",
-                        "Candara Light.TTF",
-                        "Carlito-Regular.ttf Carlito-Regular.ttf",
-                        "Castellar.ttf",
-                        "Courier New.ttf",
-                        "LSANS.TTF",
-                        "Lucida Bright Regular.ttf",
-                        "TRTempusSansITC.ttf",
-                        "Verdana.ttf",
-                        "bell-mt.ttf",
-                        "eras-itc-light.ttf",
-                        "fonnts.com-aptos-light.ttf",
-                        "georgia.ttf",
-                        "segoeuithis.ttf",
-                        "youyuan.TTF",
-                        "TfPonetoneExpanded-7BJZA.ttf",
-                    ],
-                    value="youyuan.TTF",
-                    label="Font Choice"
-                )
-                alignment = gr.Dropdown(
-                    choices=["Left", "Center", "Right", "Justified"],
-                    value="Justified",
-                    label="Text Alignment"
-                )
-                image_size = gr.Dropdown(
-                    choices=["Small", "Medium", "Large"],
-                    value="Small",
-                    label="Image Size"
-                )
-                file_format = gr.Radio(["pdf", "docx"], label="File Format", value="pdf")
-                get_document_btn = gr.Button(value="Get Document")
-
-        get_document_btn.click(
-            generate_document, [input_media, output_text, file_format, font_choice, font_size, line_spacing, alignment, image_size], gr.File(label="Download Document")
-        )
+import torch
 
-demo.launch(debug=True)
+# Load the pre-trained MobileNetV2 model
+model = models.mobilenet_v2(pretrained=True)
+model.eval()
+
+# Image transformation for input
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+def classify_image(image):
+    # Apply transformations
+    img_tensor = transform(image).unsqueeze(0)
+    
+    # Perform inference
+    with torch.no_grad():
+        outputs = model(img_tensor)
+        _, predicted_class = torch.max(outputs, 1)
+        
+    return predicted_class.item()
+
+# Gradio interface
+interface = gr.Interface(fn=classify_image, inputs=gr.Image(type="pil"), outputs="text")
+interface.launch()