Update teacher_function.py

teacher_function.py

@@ -1,158 +1,158 @@
-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-20725a9d-46d6-4baf-9e26-abfca35ab242"
-
-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 extract_text_from_pdf(pdf_path):
-    images = convert_from_path(pdf_path)
-    final_text = ""
-    for image in images:
-        image_cv = np.array(image)
-        image_cv = cv2.cvtColor(image_cv, cv2.COLOR_RGB2BGR)
-        text = pytesseract.image_to_string(image_cv)
-        final_text += text
-    return final_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:
-description: 
-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.replace('\n','<br>')
-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
-
+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 extract_text_from_pdf(pdf_path):
+    images = convert_from_path(pdf_path)
+    final_text = ""
+    for image in images:
+        image_cv = np.array(image)
+        image_cv = cv2.cvtColor(image_cv, cv2.COLOR_RGB2BGR)
+        text = pytesseract.image_to_string(image_cv)
+        final_text += text
+    return final_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:
+description: 
+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.replace('\n','<br>')
+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
+