app.py

# 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()







import gradio as gr
from transformers import pipeline
import pytesseract
from sentence_transformers import SentenceTransformer, util
from PIL import Image
from typing import List
import requests

# Initialize sentence transformer model
model1 = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')

# Hugging Face API details
API_URL = "https://api-inference.huggingface.co/models/openai-community/gpt2"
headers = {"Authorization": "Bearer hf_TsCTtXxnvpmhFKABqKmcVLyLEhjQPsITSVx"}

# Function to interact with Hugging Face API for GPT-2
def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

# Function to generate text response from GPT-2 model using Hugging Face API
def generate_response(prompt):
    response = query({"inputs": prompt})
    return response[0]['generated_text']

# Extract text from an image using Tesseract
def extract_text_from_image(filepath: str, languages: List[str]):
    image = Image.open(filepath)
    lang_str = '+'.join(languages)  # Join languages for Tesseract
    return pytesseract.image_to_string(image=image, lang=lang_str)

# Function to get embeddings for text using SentenceTransformer
def get_embedding(text):
    return model1.encode(text, convert_to_tensor=True)

# Calculate similarity between two texts using cosine similarity
def calculate_similarity(text1, text2):
    embedding1 = get_embedding(text1)
    embedding2 = get_embedding(text2)
    similarity = util.pytorch_cos_sim(embedding1, embedding2)
    return similarity.item()

# Assign grades based on similarity score
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

# Function to evaluate student's answer by comparing it to a model answer
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

# Main interface function for Gradio
def gradio_interface(image, languages: List[str], prompt):
    grade, similarity_score, feedback = evaluate_answer(image, languages)
    response = generate_response(prompt)
    return grade, similarity_score, feedback, response

# Get available Tesseract languages
language_choices = pytesseract.get_languages()

# Define Gradio interface
interface = gr.Interface(
    fn=gradio_interface,
    inputs=[
        gr.Image(type="filepath", label="Input"), 
        gr.CheckboxGroup(language_choices, type="value", value=['eng'], label='language'),
        gr.Textbox(lines=2, placeholder="Enter your prompt here", label="Prompt")
    ],
    outputs=[
        gr.Text(label="Grade"), 
        gr.Number(label="Similarity Score (%)"), 
        gr.Text(label="Feedback"), 
        gr.Text(label="Generated Response")
    ],
    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()