Update README.md

README.md

@@ -19,6 +19,308 @@ tags:
 
 *Omni-Reasoner-2B* is based on Qwen2VL and is designed for mathematical and content-based explanations. It excels in providing detailed reasoning about content and solving math problems with proper content formatting. This model integrates a conversational approach with visual and textual understanding to handle multi-modal tasks effectively.
 
+# **Use it with Transformers**
+
+*Before using, ensure that the required libraries are successfully installed in the environment.*
+
+    !pip install gradio spaces transformers accelerate numpy requests torch torchvision qwen-vl-utils av ipython reportlab fpdf python-docx pillow huggingface_hub
+    
+*ChemQwen With Inference Documentation, **Before using, make sure that the `hf_token` is provided in the login field in the code below.***
+
+```python
+
+# Authenticate with Hugging Face
+from huggingface_hub import login
+
+# Log in to Hugging Face using the provided token
+hf_token = '----xxxxx----'
+login(hf_token)
+
+# Demo
+import gradio as gr
+import spaces
+from transformers import Qwen2VLForConditionalGeneration, AutoProcessor, TextIteratorStreamer
+from qwen_vl_utils import process_vision_info
+import torch
+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.pdfbase import pdfmetrics
+from reportlab.pdfbase.ttfonts import TTFont
+import docx
+from docx.enum.text import WD_ALIGN_PARAGRAPH
+
+# Define model options
+MODEL_OPTIONS = {
+    "Omni-Reasoner": "prithivMLmods/Omni-Reasoner-2B",
+}
+
+# Preload models and processors into CUDA
+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.float16
+    ).to("cuda").eval()
+    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}")
+
+@spaces.GPU
+def qwen_inference(model_name, media_input, text_input=None):
+    """Handles inference for the selected model."""
+    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",
+    ).to("cuda")
+
+    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."""
+    # Remove LaTeX delimiters and convert to plain text
+    plain_text = output_text.replace("\\(", "").replace("\\)", "").replace("\\[", "").replace("\\]", "")
+    return plain_text
+
+def generate_document(media_path, output_text, file_format, 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_size, line_spacing, alignment, image_size)
+    elif file_format == "docx":
+        return generate_docx(media_path, plain_text, font_size, line_spacing, alignment, image_size)
+
+def generate_pdf(media_path, plain_text, 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"].fontSize = int(font_size)
+    styles["Normal"].leading = int(font_size) * line_spacing
+    styles["Normal"].alignment = {
+        "Left": 0,
+        "Center": 1,
+        "Right": 2,
+        "Justified": 4
+    }[alignment]
+
+    story = []
+
+    # Add image with size adjustment
+    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))
+
+    # Add plain text output
+    text = Paragraph(plain_text, styles["Normal"])
+    story.append(text)
+
+    doc.build(story)
+    return filename
+
+def generate_docx(media_path, plain_text, font_size, line_spacing, alignment, image_size):
+    """Generates a DOCX document."""
+    filename = f"output_{uuid.uuid4()}.docx"
+    doc = docx.Document()
+
+    # Add image with size adjustment
+    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()
+
+    # Add plain text output
+    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.size = docx.shared.Pt(int(font_size))
+
+    doc.save(filename)
+    return filename
+
+# CSS for output styling
+css = """
+  #output {
+    height: 500px;
+    overflow: auto;
+    border: 1px solid #ccc;
+  }
+.submit-btn {
+    background-color: #cf3434  !important;
+    color: white !important;
+}
+.submit-btn:hover {
+    background-color: #ff2323 !important;
+}
+.download-btn {
+    background-color: #35a6d6 !important;
+    color: white !important;
+}
+.download-btn:hover {
+    background-color: #22bcff !important;
+}
+"""
+
+# Gradio app setup
+with gr.Blocks(css=css) as demo:
+    gr.Markdown("# ChemQwen Chemical Identifier")
+
+    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="Omni-Reasoner"
+                )
+                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", elem_classes="submit-btn")
+
+            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]
+        )
+
+        # Add examples directly usable by clicking
+        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"
+                )
+                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", elem_classes="download-btn")
+
+        get_document_btn.click(
+            generate_document, [input_media, output_text, file_format, font_size, line_spacing, alignment, image_size], gr.File(label="Download Document")
+        )
+
+demo.launch(debug=True)
+```
+
 # **Key Enhancements**
 1. **Advanced Reasoning Capabilities**: 
    - Enhanced ability to perform long-form reasoning for complex mathematical and content-based queries.