Update main.py

main.py

@@ -1,214 +1,214 @@
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-import json
-import openai
-from typing import List, Dict, Any, Optional
-
-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: int
-    hours_of_study_weekends: int
-    revision_days: str
-    test_days: str
-    class_std: str
-    months_of_study: int
-    completed_phy_chapters: List[str]
-    completed_chem_chapters: List[str]
-    completed_maths_chapters: List[str]
-    startphyRoadmapFrom: str
-    startchemRoadmapFrom: str
-    startmathRoadmapFrom: str
-    userleft_topic_phy: Dict[str, Any]
-    userleft_topic_chem: List[Any]
-    userleft_topic_maths: List[Any]
-    total_time: int
-
-output_structure = """
-{
-  "schedule": [
-          "days": [
-            {
-              "dayNumber": "string",
-              "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)
-
-        # Get topics within timeframe
-        physics_topics = get_topics_within_timeframe(phy, input_data.total_time, input_data.startphyRoadmapFrom)
-        chemistry_topics = get_topics_within_timeframe(chem, input_data.total_time, input_data.startchemRoadmapFrom)
-        mathematics_topics = get_topics_within_timeframe(maths, input_data.total_time, input_data.startmathRoadmapFrom)
-
-        physics_topics.insert(0, input_data.userleft_topic_phy)
-        chemistry_topics.extend(input_data.userleft_topic_chem)
-        mathematics_topics.extend(input_data.userleft_topic_maths)
-
-        # 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.
-        """
-
-        # 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
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import json
+import openai
+from typing import List, Dict, Any, Optional
+
+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: int
+    hours_of_study_weekends: int
+    revision_days: str
+    test_days: str
+    class_std: str
+    months_of_study: int
+    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[str]
+    userleft_topic_chem: List[str]
+    userleft_topic_maths: List[str]
+    total_time: int
+
+output_structure = """
+{
+  "schedule": [
+          "days": [
+            {
+              "dayNumber": "string",
+              "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)
+
+        # Get topics within timeframe
+        physics_topics = get_topics_within_timeframe(phy, input_data.total_time, input_data.startphyRoadmapFrom)
+        chemistry_topics = get_topics_within_timeframe(chem, input_data.total_time, input_data.startchemRoadmapFrom)
+        mathematics_topics = get_topics_within_timeframe(maths, input_data.total_time, input_data.startmathRoadmapFrom)
+
+        physics_topics.insert(0, input_data.userleft_topic_phy)
+        chemistry_topics.extend(input_data.userleft_topic_chem)
+        mathematics_topics.extend(input_data.userleft_topic_maths)
+
+        # 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.
+        """
+
+        # 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)