Update app.py

app.py

@@ -1,6 +1,5 @@
 import os
 import streamlit as st
-import requests
 from PIL import Image
 import numpy as np
 from transformers import AutoProcessor, AutoModelForPreTraining
@@ -12,23 +11,23 @@ token = os.getenv("HF_Token")
 login(token)
 
 # Initialize the Hugging Face model
-tokenizer = AutoProcessor.from_pretrained("google/paligemma-3b-mix-224")
+processor = AutoProcessor.from_pretrained("google/paligemma-3b-mix-224")
 model = AutoModelForPreTraining.from_pretrained("google/paligemma-3b-mix-224")
 
 # Function to transcribe handwritten notes using Hugging Face model
 def transcribe_handwriting(image):
-    # Convert image to array and preprocess
-    image = Image.convert("RGB")
-    image = np.array(image)
+    # Convert image to RGB and numpy array
+    image = image.convert("RGB")
+    image_array = np.array(image)
 
     # Prepare input for the model
-    inputs = tokenizer(image, return_tensors="pt")
-    
+    inputs = processor(image_array, return_tensors="pt")
+
     # Generate output
     with torch.no_grad():
         outputs = model.generate(**inputs, max_length=512)
-    
-    transcription = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    transcription = processor.decode(outputs[0], skip_special_tokens=True)
     return transcription
 
 # Set Streamlit page configuration
@@ -45,14 +44,14 @@ st.title("Doctor's Prescription Reader 💊")
 uploaded_file = st.file_uploader("Upload Prescription Image", type=["jpg", "jpeg", "png"])
 
 if uploaded_file is not None:
- # Open the image using PIL
+    # Open the image using PIL
     image = Image.open(uploaded_file)
 
     # Display uploaded image
-    st.image(uploaded_file, caption="Uploaded Prescription", use_column_width=True)
+    st.image(image, caption="Uploaded Prescription", use_column_width=True)
 
     with st.spinner("Transcribing handwriting..."):
         # Transcribe handwritten notes
-        extracted_text = transcribe_handwriting(uploaded_file)
+        extracted_text = transcribe_handwriting(image)
         st.subheader("Transcribed Text from Prescription:")
-        st.text(extracted_text)
+        st.text(extracted_text)