Upload Streamlit.py

Streamlit.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[5]:
+
+
+import streamlit as st
+from 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
+
+def main():
+    st.title("PathoAgent")
+
+    # Image upload
+    st.subheader("Upload Image")
+    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')
+        #resized_img = image.resize((10,10))
+        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()
+
+
+# streamlit run Streamlit.py