Update teacher_function.py

teacher_function.py

@@ -1,3 +1,4 @@
+from mistralai import Mistral
 from ai71 import AI71
 from PyPDF2 import PdfReader
 from pdf2image import convert_from_path
@@ -19,23 +20,18 @@ def extract_text_from_pdf(pdf_file):
     return text
 
 def generate_questions_from_text(text, no_of_questions, marks_per_part, no_parts):
-    ai71 = AI71(AI71_API_KEY)
-    messages = [
+    chat_response = client.chat.complete(
+            model=MODEL,
+            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)
+        )
+    response_content = chat_response.choices[0].message.content
+    return jsonify({"response": response_content})
+    
 
 def extract_text_from_image(image_path):
     # Load the image
@@ -69,23 +65,16 @@ guidelines:
 Note:Provide only marks for each answers. dont provide anything other than that.
 Format:
 1.Question no: Marks,etc"""
-
-    messages = [
+    chat_response = client.chat.complete(
+            model=MODEL,
+            messages =[
         {"role": "system", "content": "You are a strict 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
-    print(response_content)
-
-    return response_content
+        )
+    response_content = chat_response.choices[0].message.content
+    return jsonify({"response": response_content})
+    
 
 def generate_student_report(name, age, cgpa, course, assigned_test, ai_test, interests, difficulty, courses_taken):
     prompt = f"""
@@ -99,25 +88,19 @@ def generate_student_report(name, age, cgpa, course, assigned_test, ai_test, int
     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=[
+    chat_response = client.chat.complete(
+            model=MODEL,
+            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
+        )
+    response_content = chat_response.choices[0].message.content
+    return jsonify({"response": response_content})
+    
 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",
+    chat_response = client.chat.complete(
+            model=MODEL,
             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"
@@ -127,26 +110,25 @@ def generate_timetable_module(data,hours_per_day,days_per_week,semester_end_date
                                             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.")
-
+    response_content = chat_response.choices[0].message.content
+    return jsonify({"response": response_content})
+    
+   
 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",
+    chat_response = client.chat.complete(
+            model=MODEL,
             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
+            ]
+        )
+    response_content = chat_response.choices[0].message.content
+    return jsonify({"response": response_content})
+    
 
 def generate_timetable_weak(clustered_subjects, hours_per_day):
     prompt = (
@@ -155,14 +137,14 @@ def generate_timetable_weak(clustered_subjects, hours_per_day):
         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",
+    chat_response = client.chat.complete(
+            model=MODEL,
             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
+            ]
+        )
+    response_content = chat_response.choices[0].message.content
+    return jsonify({"response": response_content})
+    
+