| # 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": f"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}) | |
| # # Check if the response contains the expected format | |
| # if isinstance(response, list) and len(response) > 0 and 'generated_text' in response[0]: | |
| # return response[0]['generated_text'] | |
| # else: | |
| # # Log the response if something unexpected is returned | |
| # print("Unexpected response format:", response) | |
| # return "Sorry, I couldn't generate a response." | |
| # # 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}" | |
| # prompt=f"the student got grades: {grade} when Student's answer is: {student_answer} and Teacher's answer is: {model_answer}. justify the grades given to student" | |
| # return grade, similarity_score * 100, feedback, prompt | |
| # # Main interface function for Gradio | |
| # def gradio_interface(image, languages: List[str], prompt=""): | |
| # grade, similarity_score, feedback,prompt = 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() | |
| import os | |
| from groq import Groq | |
| 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') | |
| # 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) | |
| # 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}" | |
| 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, similarity_score * 100, feedback, prompt | |
| # Main interface function for Gradio | |
| async def gradio_interface(image, languages: List[str], prompt="", history=[]): | |
| grade, similarity_score, feedback, prompt = evaluate_answer(image, languages) | |
| response = "" | |
| async for result in chat_groq(prompt, history): | |
| response = result # Get the Groq response | |
| 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.queue() | |
| interface.launch() | |