Update app.py

app.py

@@ -5,42 +5,26 @@ import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 from easyocr import Reader
 
-# Load the OCR model and text explanation model (GPT-2 as an example)
-ocr_reader = Reader(['en'])
-text_generator = AutoModelForCausalLM.from_pretrained("gpt2")
-text_tokenizer = AutoTokenizer.from_pretrained("gpt2")
 
-# Define a function to extract text from an image
+ocr_reader = Reader(['en'])
+text_generator = AutoModelForCausalLM.from_pretrained("gpt3")
+text_tokenizer = AutoTokenizer.from_pretrained("gpt3")
 def extract_text(image):
     return ocr_reader.readtext(image)
-
-# Define a function to explain the extracted text
 def explain_text(text):
-    # Generate an explanation using the text generation model (GPT-2)
     input_ids = text_tokenizer.encode(text, return_tensors="pt")
-    explanation_ids = text_generator.generate(input_ids, max_length=50, num_return_sequences=1)
+    explanation_ids = text_generator.generate(input_ids, max_length=100, num_return_sequences=1)
     explanation = text_tokenizer.decode(explanation_ids[0], skip_special_tokens=True)
     return explanation
 
-# Create a Streamlit layout
 st.title("Text Classification Model")
-
-# Allow users to upload an image
 uploaded_file = st.file_uploader("Upload an image:")
 
-# Extract text from the uploaded image
 if uploaded_file is not None:
-    # Read the uploaded image
     image = Image.open(uploaded_file)
-
-    # Extract text from the image
     ocr_results = extract_text(image)
     extracted_text = " ".join([res[1] for res in ocr_results])
-
-    # Explain the extracted text
     explanation = explain_text(extracted_text)
-
-    # Display the extracted text and explanation
     st.markdown("**Extracted text:**")
     st.markdown(extracted_text)