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app.py

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+# Standard Python imports
+import os
+import re
+import json
+from typing import List, Dict, Any
+
+# Data processing and visualization
+from PIL import Image
+from tqdm import tqdm
+from tqdm.notebook import tqdm
+
+# Deep Learning & ML
+import torch
+from transformers import (
+   AutoProcessor,
+   AutoModelForVision2Seq,
+   AutoTokenizer,
+   AutoModelForCausalLM,
+   TextStreamer,
+   Idefics3ForConditionalGeneration,
+   BitsAndBytesConfig
+
+)
+
+from unsloth import FastVisionModel
+
+# Dataset handling
+from datasets import load_from_disk
+
+# API & Authentication
+from huggingface_hub import login
+
+# UI & Environment
+import gradio as gr
+from dotenv import load_dotenv
+
+# Available models
+MODELS = {
+    "Blood Cell Classifier with Llama-3.2": "laurru01/Llama-3.2-11B-Vision-Instruct-ft-PeripherallBloodCells",
+    "Blood Cell Classifier with Qwen2-VL": "laurru01/Qwen2-VL-2B-Instruct-ft-bloodcells-big",
+    "Blood Cell Classifier with SmolVLM": "laurru01/SmolVLM-Instruct-ft-PeripherallBloodCells",
+}
+
+# Global dictionary to store loaded models
+loaded_models = {}
+
+def initialize_models():
+    """Preload all models during startup"""
+    print("Initializing models...")
+    for model_name, model_path in MODELS.items():
+        print(f"Loading {model_name}...")
+        try:
+            if "SmolVLM" in model_name:
+                # Carga específica para SmolVLM
+                base_model = Idefics3ForConditionalGeneration.from_pretrained(
+                    "HuggingFaceTB/SmolVLM-Instruct",
+                    device_map="auto",
+                    torch_dtype=torch.bfloat16,
+                    load_in_4bit=True,
+                    max_memory={0: "12GB"}
+                )
+                base_model.load_adapter(model_path)
+                processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM-Instruct")
+                
+                loaded_models[model_name] = {
+                    "model": base_model,
+                    "processor": processor,
+                    "type": "smolvlm"
+                }
+            else:
+                # Carga original para Llama y Qwen (sin cambios)
+                model, tokenizer = FastVisionModel.from_pretrained(
+                    model_name=model_path,
+                    load_in_4bit=True,
+                    use_gradient_checkpointing="unsloth"
+                )
+                FastVisionModel.for_inference(model)
+                processor = AutoProcessor.from_pretrained(model_path)
+
+                loaded_models[model_name] = {
+                    "model": model,
+                    "tokenizer": tokenizer,
+                    "processor": processor,
+                    "type": "standard"
+                }
+            print(f"Successfully loaded {model_name}")
+
+        except Exception as e:
+            print(f"Error loading {model_name}: {str(e)}")
+
+    print("Model initialization complete")
+
+def extract_cell_type(text):
+    """Extract cell type from generated description"""
+    cell_types = ['neutrophil', 'lymphocyte', 'monocyte', 'eosinophil', 'basophil']
+    text_lower = text.lower()
+    for cell_type in cell_types:
+        if cell_type in text_lower:
+            return cell_type.capitalize()
+    return "Unidentified Cell Type"
+
+@torch.no_grad()
+def generate_description_standard(model, tokenizer, image):
+    """Generate description using standard models (Llama and Qwen)"""
+    messages = [{
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "text", "text": "As a hematologist, carefully identify the type of blood cell in this image and describe its key characteristics."}
+    ]}]
+
+    input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
+    inputs = tokenizer(image, input_text, add_special_tokens=False, return_tensors="pt").to("cuda")
+
+    text_streamer = TextStreamer(tokenizer, skip_prompt=True)
+    output = model.generate(
+        **inputs,
+        streamer=text_streamer,
+        max_new_tokens=1024,
+        use_cache=True,
+        temperature=1.5,
+        min_p=0.1
+    )
+
+    raw_output = tokenizer.decode(output[0], skip_special_tokens=True)
+    if "The provided image" in raw_output:
+        start_idx = raw_output.find("assistant")
+        cleaned_output = raw_output[start_idx:]
+    else:
+        cleaned_output = raw_output
+
+    return cleaned_output.strip()
+
+@torch.no_grad()
+def generate_description_smolvlm(model, processor, image):
+    """Generate description using SmolVLM model with memory-efficient settings"""
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+    
+    # Redimensionar a un tamaño más pequeño para reducir memoria
+    max_size = 192
+    image.thumbnail((max_size, max_size), Image.Resampling.LANCZOS)
+    
+    sample = [{
+        "role": "user",
+        "content": [
+            {"type": "image", "image": image},
+            {"type": "text", "text": "As a hematologist, carefully identify the type of blood cell in this image and describe its key characteristics."}
+        ]
+    }]
+
+    text_input = processor.apply_chat_template(
+        sample, 
+        add_generation_prompt=True
+    )
+
+    try:
+        torch.cuda.empty_cache()
+        
+        with torch.cuda.amp.autocast():
+            model_inputs = processor(
+                text=text_input,
+                images=[[image]],
+                return_tensors="pt",
+            ).to("cuda")
+
+            generated_ids = model.generate(
+                **model_inputs,
+                max_new_tokens=256,
+                do_sample=True,
+                temperature=0.7,
+                top_p=0.9,
+                repetition_penalty=1.5,
+                no_repeat_ngram_size=3,
+                num_beams=2,
+                length_penalty=1.0,
+                early_stopping=True,
+                use_cache=True,
+                pad_token_id=processor.tokenizer.pad_token_id,
+            )
+
+            response_ids = generated_ids[0][len(model_inputs.input_ids[0]):]
+            output_text = processor.decode(
+                response_ids,
+                skip_special_tokens=True,
+                clean_up_tokenization_spaces=True
+            ).strip()
+
+            if len(set(output_text.split())) < 5:
+                output_text = "Error: Generated response was too repetitive. Please try again."
+
+        del model_inputs, generated_ids, response_ids
+        torch.cuda.empty_cache()
+
+        return output_text
+
+    except Exception as e:
+        torch.cuda.empty_cache()
+        raise e
+
+def analyze_cell(image, model_name):
+    """Main function to analyze cell images"""
+    if not isinstance(image, Image.Image):
+        return "Invalid image format. Please upload a valid image.", "", None
+
+    try:
+        if model_name not in loaded_models:
+            return f"Model {model_name} not loaded.", "", None
+
+        model_components = loaded_models[model_name]
+        
+        if model_components["type"] == "smolvlm":
+            description = generate_description_smolvlm(
+                model_components["model"],
+                model_components["processor"],
+                image
+            )
+        else:
+            description = generate_description_standard(
+                model_components["model"],
+                model_components["tokenizer"],
+                image
+            )
+
+        cell_type = extract_cell_type(description)
+        return cell_type, description, image
+    
+    except Exception as e:
+        return f"Error occurred: {str(e)}", "", None
+
+# Initialize all models before starting the interface
+initialize_models()
+
+# Gradio Interface
+with gr.Blocks() as iface:
+    gr.HTML("<h1>Blood Cell Analyzer</h1>")
+    gr.HTML("<p>Upload a microscopic blood cell image for instant classification and detailed analysis</p>")
+
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(
+                label="Upload Blood Cell Image",
+                type="pil",
+                sources=["upload"]
+            )
+            model_dropdown = gr.Dropdown(
+                choices=list(MODELS.keys()),
+                value=list(MODELS.keys())[0],
+                label="Select Model Version"
+            )
+            submit_btn = gr.Button("Analyze Cell")
+
+        with gr.Column():
+            cell_type = gr.Textbox(label="Identified Cell Type")
+            description = gr.Textbox(label="Analysis Details", lines=8)
+            output_image = gr.Image(label="Analyzed Image")
+
+    submit_btn.click(
+        fn=analyze_cell,
+        inputs=[input_image, model_dropdown],
+        outputs=[cell_type, description, output_image]
+    )
+
+# Enhanced CSS with modern color scheme
+custom_css = """
+.container {
+    max-width: 1000px;
+    margin: auto;
+    padding: 30px;
+    background: linear-gradient(135deg, #f6f9fc 0%, #ffffff 100%);
+    border-radius: 20px;
+    box-shadow: 0 10px 20px rgba(0,0,0,0.05);
+}
+.title {
+    text-align: center;
+    color: #2d3436;
+    font-size: 3em;
+    font-weight: 700;
+    margin-bottom: 20px;
+    text-shadow: 2px 2px 4px rgba(0,0,0,0.1);
+}
+.subtitle {
+    text-align: center;
+    color: #636e72;
+    font-size: 1.2em;
+    margin-bottom: 40px;
+}
+.input-image {
+    border: 2px dashed #74b9ff;
+    border-radius: 15px;
+    padding: 20px;
+    transition: all 0.3s ease;
+}
+.input-image:hover {
+    border-color: #0984e3;
+    transform: translateY(-2px);
+}
+.model-dropdown {
+    background: #f8f9fa;
+    border-radius: 10px;
+    border: 1px solid #dfe6e9;
+    margin: 15px 0;
+}
+.submit-button {
+    background: linear-gradient(45deg, #0984e3, #74b9ff);
+    color: white;
+    border: none;
+    padding: 12px 25px;
+    border-radius: 10px;
+    font-weight: 600;
+    transition: all 0.3s ease;
+}
+.submit-button:hover {
+    transform: translateY(-2px);
+    box-shadow: 0 5px 15px rgba(9, 132, 227, 0.3);
+}
+.result-box {
+    background: white;
+    border-radius: 10px;
+    border: 1px solid #dfe6e9;
+    padding: 15px;
+    margin: 10px 0;
+}
+.output-image {
+    border-radius: 15px;
+    overflow: hidden;
+    box-shadow: 0 5px 15px rgba(0,0,0,0.1);
+}
+"""
+# Interface
+with gr.Blocks(css=custom_css) as iface:
+    gr.HTML("<h1 class='title'>Blood Cell Classifier</h1>")
+    gr.HTML("<p class='subtitle'>Upload a microscopic blood cell image for instant classification and detailed analysis</p>")
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(
+                label="Upload Blood Cell Image",
+                type="pil",
+                sources=["upload"],  # Only allow computer uploads
+                elem_classes="input-image"
+            )
+            model_dropdown = gr.Dropdown(
+                choices=list(MODELS.keys()),
+                value=list(MODELS.keys())[0],
+                label="Select Model Version",
+                elem_classes="model-dropdown"
+            )
+            submit_btn = gr.Button(
+                "Analyze Cell",
+                variant="primary",
+                elem_classes="submit-button"
+            )
+        with gr.Column():
+            cell_type = gr.Textbox(
+                label="Identified Cell Type",
+                elem_classes="result-box"
+            )
+            description = gr.Textbox(
+                label="Analysis Details",
+                lines=8,
+                elem_classes="result-box"
+            )
+            output_image = gr.Image(
+                label="Analyzed Image",
+                elem_classes="output-image"
+            )
+    submit_btn.click(
+        fn=analyze_cell,
+        inputs=[input_image, model_dropdown],
+        outputs=[cell_type, description, output_image]
+    )
+    gr.HTML("""
+        <div style="text-align: center; margin-top: 30px; padding: 20px;">
+            <p style="color: #636e72;">Developed by Laura Ruiz | MSc Bioinformatics and Biostatistics</p>
+            <a href="https://github.com/laurru01" target="_blank"
+               style="color: #0984e3; text-decoration: none; font-weight: 600;">
+               View on GitHub
+            </a>
+        </div>
+    """)
+
+# Launch the interface
+iface.launch()