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from ai71 import AI71
from PyPDF2 import PdfReader
from pdf2image import convert_from_path
import cv2
import numpy as np
import pytesseract
AI71_API_KEY = "api71-api-652e5c6c-8edf-41d0-9c34-28522b07bef9"
def extract_text_from_pdf(pdf_file):
text = ""
reader = PdfReader(pdf_file)
for page in reader.pages:
text += page.extract_text()
return text
def generate_questions_from_text(text, no_of_questions, marks_per_part, no_parts):
ai71 = AI71(AI71_API_KEY)
messages = [
{"role": "system", "content": "You are a teaching assistant"},
{"role": "user",
"content": f"Give your own {no_of_questions} questions under each part for {no_parts} parts with {marks_per_part} marks for each part. Note that all questions must be from the topics of {text}"}
]
questions = []
for chunk in ai71.chat.completions.create(
model="tiiuae/falcon-180b-chat",
messages=messages,
stream=True,
):
if chunk.choices[0].delta.content:
questions.append(chunk.choices[0].delta.content)
return "".join(questions)
def extract_text_from_image(image_path):
img = cv2.imread(image_path)
text = pytesseract.image_to_string(img)
return text
def evaluate(question, answer, max_marks):
prompt = f"""Questions: {question}
Answer: {answer}.
Evaluate above questions one by one(if there are multiple) by provided answers and assign marks out of {max_marks}. No need overall score. Note that as maximum mark increases, the size of the answer must be large enough to get good marks. Give ouput in format below:
assigned marks:
total marks:
Note that you should not display total marks"""
messages = [
{"role": "system", "content": "You are an answer evaluator"},
{"role": "user", "content": prompt}
]
response_content = ""
for chunk in AI71(AI71_API_KEY).chat.completions.create(
model="tiiuae/falcon-180b-chat",
messages=messages,
stream=True
):
if chunk.choices[0].delta.content:
response_content += chunk.choices[0].delta.content
return response_content
def generate_student_report(name, age, cgpa, course, assigned_test, ai_test, interests, difficulty, courses_taken):
prompt = f"""
Name: {name}
Age: {age}
CGPA: {cgpa}
Course: {course}
Assigned Test Score: {assigned_test}
AI generated Test Score: {ai_test}
Interests: {interests}
Difficulty in: {difficulty}
Courses Taken: {courses_taken}
Use the above student data to generate a neat personalized report and suggested teaching methods."""
client = AI71(AI71_API_KEY)
response = client.chat.completions.create(
model="tiiuae/falcon-180B-chat",
messages=[
{"role": "system", "content": "You are a student report generator."},
{"role": "user", "content": prompt}
]
)
report = response.choices[0].message.content if response.choices and response.choices[
0].message else "No report generated."
print(report)
return report
def generate_timetable_module(data,hours_per_day,days_per_week,semester_end_date,subjects):
response = AI71(AI71_API_KEY).chat.completions.create(
model="tiiuae/falcon-180B-chat",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": f"Create a timetable starting from Monday based on the following inputs:\n"
f"- Number of hours per day: {hours_per_day}\n"
f"- Number of days per week: {days_per_week}\n"
f"- Semester end date: {semester_end_date}\n"
f"- Subjects: {', '.join(subjects)}\n"}
]
)
# Access the response content correctly
return( response.choices[0].message.content if response.choices and response.choices[0].message else "No timetable generated.")
def cluster_topics(academic_topics):
prompt = (
"Please cluster the following academic topics into their respective subjects such as Mathematics, Physics, etc.: "
+ ", ".join(academic_topics))
response = ""
for chunk in AI71(AI71_API_KEY).chat.completions.create(
model="tiiuae/falcon-180b-chat",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt},
],
stream=True,
):
if chunk.choices[0].delta.content:
response += chunk.choices[0].delta.content
return response
def generate_timetable_weak(clustered_subjects, hours_per_day):
prompt = (
f"Using the following subjects and topics:\n{clustered_subjects}\n"
f"Generate a special class timetable for {hours_per_day} hours per day.\n"
f"Also provide reference books and methods to teach the slow learners for each subject"
)
response = ""
for chunk in AI71(AI71_API_KEY).chat.completions.create(
model="tiiuae/falcon-180b-chat",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt},
],
stream=True,
):
if chunk.choices[0].delta.content:
response += chunk.choices[0].delta.content
return response
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