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PIL import Image +import torch +import requests +from transformers import BlipProcessor, BlipForQuestionAnswering,BlipImageProcessor, AutoProcessor +from transformers import BlipConfig +from datasets import load_dataset +from torch.utils.data import DataLoader +from tqdm.notebook import tqdm + +import numpy as np +import matplotlib.pyplot as plt +from IPython.display import display + +text_processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base") +image_processor = BlipImageProcessor.from_pretrained("Salesforce/blip-vqa-base") +model = BlipForQuestionAnswering.from_pretrained(r"blip_model_v2_epo89" ) + + +def preprocess_image(image): + # Your image preprocessing logic here... + # Example: Resize image to 128x128 pixels + image = image.resize((128, 128)) + image_encoding = image_processor(image, + do_resize=True, + size=(128, 128), + return_tensors="pt") + return image_encoding["pixel_values"][0] + +def preprocess_text(text, max_length=32): + # Your text preprocessing logic here... + encoding = text_processor( + None, + text, + padding="max_length", + truncation=True, + max_length=max_length, + return_tensors="pt" + ) + + for k, v in encoding.items(): + encoding[k] = v.squeeze() + return encoding + +def predict(image, question): + # Preprocess image + pixel_values = preprocess_image(image).unsqueeze(0) + + # Preprocess text + encoding = preprocess_text(question) + + # Print shapes for debugging + #print("Pixel Values Shape:", pixel_values.shape) + #print("Input IDs Shape:", encoding['input_ids'].unsqueeze(0).shape) + + # Perform prediction using your model + # Example: Replace this with your actual prediction logic + model.eval() + outputs = model.generate(pixel_values=pixel_values, input_ids=encoding['input_ids'].unsqueeze(0)) + + prediction_result = text_processor.decode(outputs[0], skip_special_tokens=True) + + return prediction_result + +import streamlit as st +from PIL import Image + +def predict(image, text_input): + # Add your prediction logic here + # Replace the following line with your actual prediction code + return f"Prediction for image and text: {text_input}" + +def main(): + # Set page title and configure page layout + st.set_page_config( + page_title="PathoAgent", + page_icon=":microscope:", + layout="wide" + ) + + # Add header with styled text + st.title(":microscope: PathoAgent") + st.markdown( + """ + + """, + unsafe_allow_html=True + ) + + st.markdown("

Medical Image Analysis for Pathology

", unsafe_allow_html=True) + st.markdown("
", unsafe_allow_html=True) + + # Navigation bar + nav_option = st.sidebar.radio("Navigation", ["Home", "Sample Images", "Upload Image"]) + + if nav_option == "Home": + home() + elif nav_option == "Sample Images": + sample_images() + elif nav_option == "Upload Image": + upload_image() + +def home(): + st.header("Welcome to PathoAgent!") + st.write( + "PathoAgent is an AI-powered medical image analysis tool designed for pathology diagnostics. " + "It empowers healthcare professionals with accurate predictions and insights from medical images. " + "Choose an option from the sidebar to get started." + ) + + st.header("About PathoAgent") + st.write( + "PathoAgent leverages advanced VQA algorithms to analyze medical images related to pathology. " + "Whether you want to upload your own images or use our sample images, PathoAgent provides predictions for pathology-related questions. " + "Explore the features and capabilities to enhance your diagnostic process." + ) + +def sample_images(): + st.header("Sample Images") + + # Sample images + sample_images = { + "Sample 1": "/Users/sayedraheel/Desktop/AI VQA Project/Final VQA/Test/Tryal/img_0002.jpg", + } + + # Button to load sample images + if st.button("Load Sample Images"): + for sample_name, sample_path in sample_images.items(): + st.subheader(sample_name) + sample_image = Image.open(sample_path).convert('RGB') + st.image(sample_image, caption=f"{sample_name} Image.", use_column_width=True) + + # Text input for each sample image + text_input = st.text_area(f"Input Question for {sample_name}:") + + # Predict button for each sample image + if st.button(f"Predict {sample_name}"): + if text_input: + # Perform prediction + prediction_result = predict(sample_image, text_input) + + # Display input text + st.subheader(f"Input Question for {sample_name}:") + st.write(text_input) + + # Display prediction result + st.subheader(f"Prediction Result for {sample_name}:") + st.write(prediction_result) + +def upload_image(): + st.header("Upload Image") + + # Image upload + uploaded_file = st.file_uploader("Choose a file", type=["jpg", "png", "jpeg"]) + + # Text input + st.subheader("Input Question") + text_input = st.text_area("Enter text here:") + + # Display uploaded image + if uploaded_file is not None: + image = Image.open(uploaded_file).convert('RGB') + st.image(image, caption="Uploaded Image.", use_column_width=True) + + # Predict button + if st.button("Predict"): + if uploaded_file is not None and text_input: + # Perform prediction + prediction_result = predict(image, text_input) + + # Display input text + st.subheader("Input Question:") + st.write(text_input) + + # Display prediction result + st.subheader("Prediction Result:") + st.write(prediction_result) + +if __name__ == "__main__": + main() diff --git a/blip_model_v2_epo89/config.json b/blip_model_v2_epo89/config.json new file mode 100644 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