Update app.py

app.py

@@ -3,49 +3,80 @@
 
 # In[5]:
 
+
 import streamlit as st
 from PIL import Image
 import torch
-from transformers import BlipProcessor, BlipForQuestionAnswering, BlipImageProcessor
+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
 
-# Load model and processors
 text_processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
 image_processor = BlipImageProcessor.from_pretrained("Salesforce/blip-vqa-base")
-model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base")
+model = BlipForQuestionAnswering.from_pretrained(r"blip_model_v2_epo89" )
+
 
-# Function to preprocess image
 def preprocess_image(image):
-    # Resize image to an appropriate size
-    image = image.resize((256, 256))
-    image_encoding = image_processor(image, return_tensors="pt")
+    # 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]
 
-# Function to preprocess text
 def preprocess_text(text, max_length=32):
-    encoding = text_processor(text, padding="max_length", truncation=True, max_length=max_length, return_tensors="pt")
+    # 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
 
-# Function to make predictions
 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()
-    with torch.no_grad():
-        pixel_values = preprocess_image(image).unsqueeze(0)
-        encoding = preprocess_text(question)
-        outputs = model(pixel_values=pixel_values, input_ids=encoding['input_ids'].unsqueeze(0))
+    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)
 
-    prediction_result = text_processor.decode(outputs[0][0], skip_special_tokens=True)
     return prediction_result
 
-# Streamlit app
 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(
         """
@@ -67,10 +98,11 @@ def main():
         """,
         unsafe_allow_html=True
     )
-
+    
     st.markdown("<div class='header'><h3 class='subheader'>Medical Image Analysis for Pathology</h3></div>", unsafe_allow_html=True)
     st.markdown("<hr style='border: 1px solid #ddd;'>", unsafe_allow_html=True)
 
+    # Navigation bar
     nav_option = st.sidebar.radio("Navigation", ["Home", "Sample Images", "Upload Image"])
     
     if nav_option == "Home":
@@ -80,7 +112,80 @@ def main():
     elif nav_option == "Upload Image":
         upload_image()
 
-# Other functions remain unchanged...
+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
+    example_image = {
+        "Sample 1": "img_0002.jpg",
+    }
+
+    # Button to load sample images
+    if st.button("Load Example Images"):
+        
+        sample_image = Image.open(example_image).convert('RGB')
+        st.image(sample_image, caption=f"Example Image", use_column_width=True)
+
+        # Text input for each sample image
+        text_input = st.text_area(f"Input Question:")
+
+        # Predict button for each sample image
+        if st.button(f"Predict"):
+            if text_input:
+                # Perform prediction
+                prediction_result = predict(sample_image, text_input)
+
+                # Display input text
+                st.subheader(f"Input Question:")
+                st.write(text_input)
+
+                # Display prediction result
+                st.subheader(f"Prediction Result:")
+                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()
+if __name__ == "__main__":
+    main()