Update main.py

main.py

@@ -1,225 +1,3 @@
-# from fastapi import FastAPI, HTTPException
-# from pydantic import BaseModel
-# import json
-# import openai
-# from typing import List, Dict, Any, Optional
-# import os
-
-# app = FastAPI()
-
-# class RoadmapInput(BaseModel):
-#     overall_study_pattern: str
-#     memorization_study_pattern: str
-#     problem_solving_study_pattern: str
-#     visualization_study_pattern: str
-#     obstacle_study_pattern: str
-#     new_topic_approach: str
-#     old_topic_approach: str
-#     topic_ratio: str
-#     hours_of_study: str
-#     hours_of_study_weekends: str
-#     revision_days: str
-#     test_days: str
-#     completed_phy_chapters: List[str]
-#     completed_chem_chapters: List[str]
-#     completed_maths_chapters: List[str]
-#     startphyRoadmapFrom: str
-#     startchemRoadmapFrom: str
-#     startmathRoadmapFrom: str
-#     userleft_topic_phy: List[dict]
-#     userleft_topic_chem: List[dict]
-#     userleft_topic_maths: List[dict]
-
-# output_structure = """
-# {
-#   "schedule": [
-#           "days": [
-#             {
-#               "dayNumber": int,
-#               "subjects": [
-#                 {
-#                   "name": "string",
-#                   "tasks": [
-#                     {
-#                       "type": "string",
-#                       "topic": "string",
-#                       "time": "string"
-#                     }
-#                   ]
-#                 }
-#               ]
-#             }
-#           ]
-#   ]
-# }
-# """
-
-# # Function to remove completed chapters from each subject
-# def remove_completed_chapters(subject_data, completed_chapters):
-#     subject_data["chapters"] = [chapter for chapter in subject_data["chapters"]
-#                                 if chapter["chapter"] not in completed_chapters]
-#     return subject_data
-
-# # Function to get topics within timeframe
-# def get_topics_within_timeframe(subject, allowed_time, start_subtopic):
-#     collected_subtopics = []
-#     time_spent = 0
-#     start_collecting = False
-
-#     if 'chapters' not in subject:
-#         print(f"Error: 'chapters' key not found in the subject: {subject}")
-#         return collected_subtopics
-
-#     for chapter in subject["chapters"]:
-#         if 'subtopics' not in chapter:
-#             print(f"Error: 'subtopics' key not found in the chapter: {chapter}")
-#             continue
-
-#         for subtopic in chapter["subtopics"]:
-#             if (subtopic["subtopic"] == start_subtopic):
-#                 start_collecting = True
-#                 continue
-
-#             if start_collecting and subtopic["subtopic_hours"]:
-#                 time_spent += float(subtopic["subtopic_hours"])
-#                 if time_spent <= allowed_time:
-#                     collected_subtopics.append({
-#                         "chapter": chapter["chapter"],
-#                         "subtopic": subtopic["subtopic"],
-#                         "subtopic_hours": subtopic["subtopic_hours"]
-#                     })
-#                 else:
-#                     break
-#         if time_spent > allowed_time:
-#             break
-
-#     return collected_subtopics
-
-
-# @app.post("/generate_roadmap")
-# async def generate_roadmap(input_data: RoadmapInput):
-#     try:
-#         # Load JSON data for each subject
-#         with open('Physics.json', 'r', encoding='utf-8') as file:
-#             phy = json.load(file)
-
-#         with open('Chemistry.json', 'r', encoding='utf-8') as file:
-#             chem = json.load(file)
-
-#         with open('Maths.json', 'r', encoding='utf-8') as file:
-#             maths = json.load(file)
-
-       
-
-#         # Remove completed chapters
-#         phy = remove_completed_chapters(phy, input_data.completed_phy_chapters)
-#         chem = remove_completed_chapters(chem, input_data.completed_chem_chapters)
-#         maths = remove_completed_chapters(maths, input_data.completed_maths_chapters)
-#         total_time=10;
-#         # Get topics within timeframe
-#         physics_topics = get_topics_within_timeframe(phy, total_time, input_data.startphyRoadmapFrom)
-#         chemistry_topics = get_topics_within_timeframe(chem, total_time, input_data.startchemRoadmapFrom)
-#         mathematics_topics = get_topics_within_timeframe(maths, total_time, input_data.startmathRoadmapFrom)
-     
-#         for topic in reversed(input_data.userleft_topic_phy):  
-#             physics_topics.insert(0, topic)
-#         for topic in reversed(input_data.userleft_topic_chem): 
-#             chemistry_topics.insert(0, topic)
-#         for topic in reversed(input_data.userleft_topic_maths):  
-#             mathematics_topics.insert(0, topic)
-        
-#         print(physics_topics)
-#         print(chemistry_topics)
-#         print(mathematics_topics)
-
-#         # User persona string
-#         userPersona = f"""
-#         You are required to generate a highly personalized roadmap for a student studying Physics, Chemistry, and Mathematics for the JEE Main exam.
-#         The roadmap should be tailored based on the following student-specific details:
-
-#         1. *Study Preferences:*
-#            - Study Pattern: {input_data.overall_study_pattern}
-#            - Memorization Approach: {input_data.memorization_study_pattern}
-#            - Problem-Solving Approach: {input_data.problem_solving_study_pattern}
-#            - Visualization Approach: {input_data.visualization_study_pattern}
-
-#         2. *Handling Challenges:*
-#            - If unable to understand a topic: {input_data.obstacle_study_pattern}
-#            - Approach to New Topics: {input_data.new_topic_approach}
-#            - Approach to Previously Encountered Topics: {input_data.old_topic_approach}
-
-#         3. *Study Hours:*
-#            - Weekdays: {input_data.hours_of_study} hours/day
-#            - Weekends: {input_data.hours_of_study_weekends} hours/day
-#            - Time Allocation Ratio (Physics:Chemistry:Mathematics): {input_data.topic_ratio}
-#            - By weekdays I mean day 1, day 2 , day 3 , day 4 , day 5
-#            - By weekends I mean day 6 , day 7
-#         """
-
-#         # System prompt
-#         sys_prompt = f"""
-#         You are required to generate a highly personalized roadmap for a student studying Physics, Chemistry, and Mathematics for the JEE Main exam.
-#         The roadmap should be tailored based on the following student-specific details:
-
-#         The roadmap must be provided in the following format:
-#         {output_structure}
-
-#         Do not include anything other than the roadmap, and ensure the focus remains strictly on the subjects {physics_topics}, {chemistry_topics}, and {mathematics_topics} and associated chapters.
-#         MAKE SURE THAT YOU MAKE THE ROADMAP FOR ALL THE THREE CHAPTERS EACH OF PHYSICS , CHEMISTRY AND MATHS TO COMPLETE THOSE CHAPTERS WITH 4 ASPECTS i.e "CONCEPT UNDERSTANDING","QUESTION PRACTICE","REVISION","TEST". ALSO INCLUDE TIME FOR EACH TASK THAT YOU GENERATE
-#         MAKE SURE THAT WE FIRST COMPLETE 1) CONCEPT UNDERSTANDING , 2) QUESTION PRACTICE FOR EVERY SUBTOPIC AND THEN REVISION AND TEST FOR WHOLE CHAPTER TOGETHER.
-#         MAKE SURE THAT WE INCLUDE EACH SUBTOPIC OF EACH CHAPTER FROM {physics_topics},{chemistry_topics} and {mathematics_topics} IS FINISHED
-#         YOU ARE NOT CONSTRAINED TO CREATE A ROADMAP FOR ONLY 'X' NUMBER OF DAYS , YOU CAN EXTEND TILL THE TOPICS ARE FINISHED BUT ONLY STICK TO THE TIMEFRAME ALLOTED FOR EACH SUBJECT AND DO NOT GO ABOVE OR BELOW THAT TIME FRAME.
-#         """
-#         openai.api_key=os.getenv("KEY")
-#         # First LLM call
-#         response = openai.ChatCompletion.create(
-#             model="gpt-4o-mini",
-#             messages=[
-#                 {
-#                     "role": "system",
-#                     "content": sys_prompt + "MAKE SURE YOU VERY VERY STRICTLY FOLLOW THE JSON STRUCTURE BECAUSE I WILL PARSE YOUR OUTPUT TO JSON"
-#                 },
-#                 {
-#                     "role": "user",
-#                     "content": userPersona
-#                 }
-#             ]
-#         )
-
-#         answer = response['choices'][0]['message']['content'].strip()
-
-#         # Calculate provided subtopics
-#         provided_subtopics = physics_topics + chemistry_topics + mathematics_topics
-
-#         # Second LLM call
-#         response = openai.ChatCompletion.create(
-#             model="gpt-4o-mini",
-#             messages=[
-#                 {
-#                     "role": "system",
-#                     "content": f'''
-#                     you created a very good roadmap {answer} but you make sure that you dont forget any subtopics from  {provided_subtopics}. ensure that the style is same as the previous roadmap.
-#                     MAKE SURE YOU VERY VERY STRICTLY FOLLOW THE JSON STRUCTURE BECAUSE I WILL PARSE YOUR OUTPUT TO JSON.
-#                     DO not include json at the top of the answer
-#                     '''
-#                 },
-#                 {
-#                     "role": "user",
-#                     "content": "Generate"
-#                 }
-#             ]
-#         )
-
-#         answer = response['choices'][0]['message']['content'].strip()
-#         parsed_json = json.loads(answer)
-
-#         return parsed_json
-#     except Exception as e:
-#         raise HTTPException(status_code=500, detail=str(e))
-
-# if __name__ == "__main__":
-#     import uvicorn
-#     uvicorn.run(app, host="0.0.0.0", port=8000)
 import json
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
@@ -342,6 +120,7 @@ async def generate_roadmap(study_input: StudyInput):
     MAKE SURE THAT WE FIRST COMPLETE 1) CONCEPT UNDERSTANDING , 2) QUESTION PRACTICE FOR EVERY SUBTOPIC AND THEN REVISION AND TEST FOR WHOLE CHAPTER TOGETHER.
     MAKE SURE THAT WE INCULDE EACH SUBTOPIC OF EACH CHAPTER FROM {phy},{chem} and {maths} IS FINISHED
     YOU ARE NOT CONSTRAINED TO CREATE A ROADMAP FOR ONLY 'X' NUMBER OF DAYS , YOU CAN EXTEND TILL THE TOPICS ARE FINISHED BUT ONLY STICK TO THE TIMEFRAME ALLOTED FOR EACH SUBJECT AND DO NOT GO ABOVE OR BELOW THAT TIME FRAME.
+    Make sure you make the roadmap for 7-10 days only.
     """
 
     # Make OpenAI API call