app.py

import os
from groq import Groq
import gradio as gr 
import pytesseract
from sentence_transformers import SentenceTransformer, util
from PIL import Image
from typing import List
import torch
from transformers import BertTokenizer, BertModel
import torch.nn.functional as F

# Load pre-trained BERT model and tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
sentence_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')

# Initialize Groq client
client = Groq(api_key=os.environ.get("GROQ_API_KEY"))


# System prompt for Groq
system_prompt = {
    "role": "system",
    "content": "You are a useful assistant. You reply with efficient answers."
}

# Function to interact with Groq for generating response
async def chat_groq(message, history):
    messages = [system_prompt]

    for msg in history:
        messages.append({"role": "user", "content": str(msg[0])})
        messages.append({"role": "assistant", "content": str(msg[1])})

    messages.append({"role": "user", "content": str(message)})

    response_content = ''
    
    stream = client.chat.completions.create(
        model="llama3-70b-8192",
        messages=messages,
        max_tokens=1024,
        temperature=1.3,
        stream=True
    )

    for chunk in stream:
        content = chunk.choices[0].delta.content
        if content:
            response_content += chunk.choices[0].delta.content
        yield response_content

# 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)


# Assign badges based on the grade
def assign_badge(grade):
    if grade == 5:
        return "Gold Badge 🌟"
    elif grade == 4:
        return "Silver Badge 🥈"
    elif grade == 3:
        return "Bronze Badge 🥉"
    else:
        return "Keep Improving Badge 💪"


# Categorize feedback into clarity, completeness, and accuracy
def detailed_feedback(similarity_score):
    if similarity_score >= 0.9:
        return {"Clarity": "Excellent", "Completeness": "Complete", "Accuracy": "Accurate"}
    elif similarity_score >= 0.8:
        return {"Clarity": "Good", "Completeness": "Almost Complete", "Accuracy": "Mostly Accurate"}
    elif similarity_score >= 0.7:
        return {"Clarity": "Fair", "Completeness": "Partial", "Accuracy": "Some Errors"}
    else:
        return {"Clarity": "Needs Improvement", "Completeness": "Incomplete", "Accuracy": "Inaccurate"}

# 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 get BERT embeddings
def get_bert_embedding(text):
    inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
    embeddings = outputs.last_hidden_state.mean(dim=1)
    return embeddings

# Function to calculate cosine similarity
def calculate_cosine_similarity(embedding1, embedding2):
    similarity = F.cosine_similarity(embedding1, embedding2)
    return similarity.item()

# Function to calculate sentence embedding similarity
def calculate_sentence_similarity(text1, text2):
    embedding1 = sentence_model.encode(text1, convert_to_tensor=True)
    embedding2 = sentence_model.encode(text2, convert_to_tensor=True)
    return util.pytorch_cos_sim(embedding1, embedding2).item()

# Function to compare logic of student and teacher answers
def compare_answers(student_answer, teacher_answer):
    bert_similarity = calculate_cosine_similarity(get_bert_embedding(student_answer), get_bert_embedding(teacher_answer))
    sentence_similarity = calculate_sentence_similarity(student_answer, teacher_answer)
    # Combine scores with weights to emphasize one method over another if needed
    final_similarity = (0.5 * bert_similarity + 0.5 * sentence_similarity)
    return final_similarity

# Function to extract keywords from the model answer (simple keyword extraction)
def extract_keywords(text):
    # Split text into words and count them
    return set(text.lower().split())

# Adjust grading based on key terms present in student answer
def check_keywords(student_answer, model_answer):
    student_keywords = extract_keywords(student_answer)
    teacher_keywords = extract_keywords(model_answer)
    keyword_overlap = len(student_keywords.intersection(teacher_keywords))
    return keyword_overlap / len(teacher_keywords)  # Ratio of matching keywords

# Function to evaluate student's answer by comparing it to a model answer
def evaluate_answer(image, languages, model_answer):
    student_answer = extract_text_from_image(image, languages)
    semantic_similarity = compare_answers(student_answer, model_answer)
    keyword_similarity = check_keywords(student_answer, model_answer)
    
    # Combine semantic similarity with keyword presence check
    combined_similarity = (semantic_similarity + keyword_similarity) / 2
    grade = get_grade(combined_similarity)
    feedback = f"Student's answer: {student_answer}\nTeacher's answer: {model_answer}"
    badge = assign_badge(grade)
    detailed_feedback_msg = detailed_feedback(combined_similarity)
    prompt = f"The student got grade: {grade} when the student's answer is: {student_answer} and the teacher's answer is: {model_answer}. Justify the grade given to the student."
    return grade, combined_similarity * 100, feedback, badge, detailed_feedback_msg, prompt

# The rest of your existing code...


# Main interface function for Gradio
async def gradio_interface(image, languages: List[str], model_answer="The process of photosynthesis helps plants produce glucose using sunlight.", prompt="", history=[]):
    grade, similarity_score, feedback, badge, detailed_feedback_msg, prompt = evaluate_answer(image, languages, model_answer)
    response = ""
    async for result in chat_groq(prompt, history):
        response = result  # Get the Groq response
    return grade, similarity_score, feedback, badge, detailed_feedback_msg, 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 model answer here", label="Model Answer"),
        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.HTML(label="Visual Feedback"), 
        gr.Text(label="Badge"), 
        gr.JSON(label="Detailed Feedback"), 
        gr.Text(label="Generated Response")
    ],
    title="Enhanced Automated Grading System",
    description="Upload an image of your answer sheet to get a grade from 1 to 5, similarity score, visual feedback, badge, and detailed feedback based on the model answer.",
    live=True
)

if __name__ == "__main__":
    interface.queue()
    interface.launch()