Delete app.py

app.py

@@ -1,107 +0,0 @@
-
-# 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