Update app.py

app.py

@@ -7,32 +7,14 @@ from PIL import Image
 import pytesseract as tess
 from sentence_transformers import SentenceTransformer, util
 import io
+from typing import List
 
-# save_directory = "spaces/Garvitj/grader"
+def extract_text_from_image(filepath: str, languages: List[str]):
+    image = Image.open(filepath)
+    return pytesseract.image_to_string(image=image, lang=', '.join(languages))
 
-# # Load the tokenizer from the saved directory
-# tokenizer = AutoTokenizer.from_pretrained(save_directory)
+# tess.pytesseract.tesseract_cmd = r"tesseract"
 
-# # Load the model from the saved directory
-# model = AutoModelForCausalLM.from_pretrained(
-#     save_directory,
-#     torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-#     device_map="auto" if torch.cuda.is_available() else None
-# )
-
-# # Move model to the appropriate device (CPU or CUDA)
-# device = "cuda" if torch.cuda.is_available() else "cpu"
-# model.to(device)
-
-# print(f"Model and tokenizer loaded from {save_directory}")
-
-tess.pytesseract.tesseract_cmd = r"tesseract"
-
-# Use a pipeline as a high-level helper
-# pipe = pipeline("text-generation", model="eachadea/vicuna-7b-1.1")
-
-# Initialize the pipeline with the Hugging Face API
-# pipe = pipeline("text-generation", model="eachadea/vicuna-7b-1.1", api_key="your_api_key")
 import requests
 
 API_URL = "https://api-inference.huggingface.co/models/openai-community/gpt2"
@@ -73,12 +55,6 @@ def get_grade(similarity_score):
     else:
         return 1
 
-def extract_text_from_image(image):
-    # Convert PIL image to RGB format
-    image = image.convert('RGB')
-    # Use pytesseract to extract text from the image
-    text = tess.image_to_string(image)
-    return text.strip()
 
 def evaluate_answer(image):
     student_answer = extract_text_from_image(image)
@@ -108,7 +84,7 @@ def gradio_interface(image, prompt):
 
 interface = gr.Interface(
     fn=gradio_interface,
-    inputs=gr.Image(type="pil", label="Upload your answer sheet"),
+    inputs=gr.Image(type="filepath",label="Upload your answer sheet"),
     outputs=[gr.Text(label="Grade"), gr.Number(label="Similarity Score (%)"), gr.Text(label="Feedback")],
     title="Automated Grading System",
     description="Upload an image of your answer sheet to get a grade from 1 to 5, similarity score, and feedback based on the model answer.",