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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch
import numpy as np
import cv2
from PIL import Image
import pytesseract
from sentence_transformers import SentenceTransformer, util
import io
from typing import List
def extract_text_from_image(filepath: str, languages: List[str]):
image = Image.open(filepath)
return pytesseract.image_to_string(image=image, lang=', '.join(languages))
# tess.pytesseract.tesseract_cmd = r"tesseract"
import requests
API_URL = "https://api-inference.huggingface.co/models/openai-community/gpt2"
headers = {"Authorization": "hf_TsCTtXxnvpmhFKABqKmcVLyLEhjQPsITSVx"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
# output = query({
# "inputs": "Can you please let us know more details about your ",
# })
def generate_response(prompt):
# Generate response from the API
response = query({"inputs":prompt})
return response[0]['generated_text']
def get_embedding(text):
return model1.encode(text, convert_to_tensor=True)
def calculate_similarity(text1, text2):
embedding1 = get_embedding(text1)
embedding2 = get_embedding(text2)
similarity = util.pytorch_cos_sim(embedding1, embedding2)
return similarity.item()
def get_grade(similarity_score):
if similarity_score >= 0.9:
return 5
elif similarity_score >= 0.8:
return 4
elif similarity_score >= 0.7:
return 3
elif similarity_score >= 0.6:
return 2
else:
return 1
def evaluate_answer(image,languages):
student_answer = extract_text_from_image(image,languages)
model_answer = "The process of photosynthesis helps plants produce glucose using sunlight."
similarity_score = calculate_similarity(student_answer, model_answer)
grade = get_grade(similarity_score)
feedback = f"Student's answer: {student_answer}\nTeacher's answer: {model_answer}"
return grade, similarity_score * 100, feedback
def generate_response(prompt):
# Generate response from the new model using the pipeline
response = pipe(prompt, max_length=150, temperature=0.7)
return response[0]['generated_text']
def gradio_interface(image, languages: List[str]):
grade, similarity_score, feedback = evaluate_answer(image,languages)
response = generate_response(prompt)
return grade, similarity_score, response
# # Define Gradio interface
# interface = gr.Interface(
# fn=gradio_interface,
# inputs=[gr.Image(type="pil"), gr.Textbox(lines=2, placeholder="Enter your prompt here")],
# outputs=[gr.Label(), gr.Label(), gr.Textbox(), gr.Textbox()],
# live=True
# )
language_choices = pytesseract.get_languages()
interface = gr.Interface(
fn=gradio_interface,
inputs=[
gr.Image(type="filepath", label="Input"),
gr.CheckboxGroup(language_choices, type="value", value=['eng'], label='language')
],
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.",
live=True
)
if __name__ == "__main__":
interface.launch()
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