Create app.py

app.py

@@ -0,0 +1,87 @@
+import streamlit as st
+import torch
+from transformers import DistilBertForSequenceClassification, DistilBertTokenizer
+from PIL import Image
+import numpy as np
+from io import BytesIO
+
+# Load the Fastai Learner model
+learn = torch.load('model.pkl', map_location=torch.device('cpu'))
+learn.eval()
+
+# Load the X-ray detection model
+learn_xray = torch.load("xraydet.pkl", map_location=torch.device('cpu'))
+learn_xray.eval()
+
+# Load the DistilBERT model and tokenizer
+model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
+tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+
+# Define language constants
+UZBEK = 'uz'
+ENGLISH = 'en'
+RUSSIAN = 'ru'
+
+uz_welcome = '''- Assalomu alaykum, Men dasturchi Abdrurasulov Mirsaid tomonidan tarbiyalangan sun'iy intellektman. 
+- Bemorning o'pka rengin rasmiga qarab, bemorda o'pka yallig'lanishi bor yoki yo'q ekanligini aniqlab beraman.
+- Model aniqligi 98% ga teng.
+- Muhim: Men test rejimida ishlamoqdaman, iltimos, menga ishonib hulosa qilmang, malakalik shifokorga murojat qiling.
+- Boshlash uchun, iltimos bemor o'pkasining rengin rasmini yuboring. Muhim: rasm ko'rinishida, file emas.'''
+
+en_welcome = '''- Hello, I am a trained artificial intelligence by developer Abdurasulov Mirsaid.
+- Looking at the picture of the patient's lungs, I can determine whether the patient has pneumonia or not.
+- My model accuracy is 98 %.
+- Important: I am working in test mode, please do not have a conclusion based on my responce, consult a qualified doctor.
+- To begin, please send the XRay image of the patient's lungs. Note: as a picture, not a file. '''
+
+rus_welcome = '''- Здравствуйте, я обученный искусственный интеллект от разработчика Мирсаидa.
+- Глядя на снимок легких больного, я могу определить, есть ли у больного туберкулез или нет.
+- У меня действительно высокая точность (98 %).
+- Важно: я работаю в тестовом режиме, пожалуйста, не делайте вывод по моему ответу, обратитесь к квалифицированному врачу. 
+- Для начала отправьте рентгеновский снимок легких пациента. Примечание: как изображение, а не файл.'''
+
+# Define welcome messages
+WELCOME_MESSAGES = {
+    UZBEK: uz_welcome,
+    ENGLISH: en_welcome,
+    RUSSIAN: rus_welcome,
+}
+
+# Function to make predictions
+def predict_pneumonia(image):
+    img_array = np.array(image)
+    img_fastai = Image.fromarray(img_array).convert('RGB')
+    img_fastai = img_fastai.resize((224, 224))
+    img_fastai = np.array(img_fastai) / 255.0
+    img_fastai = torch.tensor(img_fastai).permute(2, 0, 1).unsqueeze(0).float()
+
+    pred_xray = learn_xray.predict(img_fastai)[0]
+    
+    if pred_xray == 1:
+        inputs = tokenizer(img_array, return_tensors="pt")
+        outputs = model(**inputs)
+        predicted_class_idx = torch.argmax(outputs.logits[0]).item()
+        if predicted_class_idx == 1:
+            return "Pneumonia Positive"
+        else:
+            return "Normal"
+    else:
+        return "Invalid X-ray image. Please upload a clear lung X-ray image."
+
+# Streamlit app
+def main():
+    st.title("Pneumonia Detection")
+    st.write("Upload a chest X-ray image to detect pneumonia.")
+
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+        if st.button('Predict'):
+            prediction = predict_pneumonia(image)
+            st.write(f"Prediction: {prediction}")
+
+if __name__ == '__main__':
+    main()
+