Update main.py

main.py

@@ -1,192 +1,3 @@
-# # Import necessary libraries
-# from fastapi import FastAPI, HTTPException
-# from pydantic import BaseModel
-# import gspread
-# from google.oauth2.service_account import Credentials
-# import pandas as pd
-# from collections import defaultdict
-# import os
-
-# # Initialize the FastAPI app
-# app = FastAPI()
-
-# # Step 1: Define a function to get Google Sheets API credentials
-# def get_credentials():
-#     """Get Google Sheets API credentials from environment variables."""
-#     try:
-#         # Construct the service account info dictionary
-#         service_account_info = {
-#             "type": os.getenv("SERVICE_ACCOUNT_TYPE"),
-#             "project_id": os.getenv("PROJECT_ID"),
-#             "private_key_id": os.getenv("PRIVATE_KEY_ID"),
-#             "private_key": os.getenv("PRIVATE_KEY").replace('\\n', '\n'),
-#             "client_email": os.getenv("CLIENT_EMAIL"),
-#             "client_id": os.getenv("CLIENT_ID"),
-#             "auth_uri": os.getenv("AUTH_URI"),
-#             "token_uri": os.getenv("TOKEN_URI"),
-#             "auth_provider_x509_cert_url": os.getenv("AUTH_PROVIDER_X509_CERT_URL"),
-#             "client_x509_cert_url": os.getenv("CLIENT_X509_CERT_URL"),
-#             "universe_domain": os.getenv("UNIVERSE_DOMAIN")
-#         }
-#         scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
-#         creds = Credentials.from_service_account_info(service_account_info, scopes=scope)
-#         return creds
-
-#     except Exception as e:
-#         print(f"Error getting credentials: {e}")
-#         return None
-
-# # Step 2: Authorize gspread using the credentials
-# creds = get_credentials()
-# client = gspread.authorize(creds)
-
-# # Input the paths and coaching code
-# journal_file_path = ''
-# panic_button_file_path = ''
-# test_file_path = ''
-# coachingCode = '1919'
-
-# if coachingCode == '1919':
-#     journal_file_path = 'https://docs.google.com/spreadsheets/d/1EFf2lr4A10nt4RhIqxCD_fxe-l3sXH09II0TEkMmvhA/edit?usp=drive_link'
-#     panic_button_file_path = 'https://docs.google.com/spreadsheets/d/1nFZGkCvRV6qS-mhsORhX3dxI0JSge32_UwWgWKl3eyw/edit?usp=drive_link'
-#     test_file_path = 'https://docs.google.com/spreadsheets/d/13PUHySUXWtKBusjugoe7Dbsm39PwBUfG4tGLipspIx4/edit?usp=drive_link'
-
-# # Step 3: Open Google Sheets using the URLs
-# journal_file = client.open_by_url(journal_file_path).worksheet('Sheet1')
-# panic_button_file = client.open_by_url(panic_button_file_path).worksheet('Sheet1')  # Fixed missing part
-# test_file = client.open_by_url(test_file_path).worksheet('Sheet1')
-
-# # Step 4: Convert the sheets into Pandas DataFrames
-# journal_df = pd.DataFrame(journal_file.get_all_values())
-# panic_button_df = pd.DataFrame(panic_button_file.get_all_values())
-# test_df = pd.DataFrame(test_file.get_all_values())
-
-# # Label the columns manually since there are no headers
-# journal_df.columns = ['user_id', 'productivity_yes_no', 'productivity_rate']
-# panic_button_df.columns = ['user_id', 'panic_button']
-
-# # Initialize a list for the merged data
-# merged_data = []
-
-# # Step 5: Group panic buttons by user_id and combine into a single comma-separated string
-# panic_button_grouped = panic_button_df.groupby('user_id')['panic_button'].apply(lambda x: ','.join(x)).reset_index()
-
-# # Merge journal and panic button data
-# merged_journal_panic = pd.merge(journal_df, panic_button_grouped, on='user_id', how='outer')
-
-# # Step 6: Process the test data
-# test_data = []
-# for index, row in test_df.iterrows():
-#     user_id = row[0]
-#     i = 1
-#     while i < len(row) and pd.notna(row[i]):  # Process chapter and score pairs
-#         chapter = row[i].lower().strip()
-#         score = row[i + 1]
-#         if pd.notna(score):
-#             test_data.append({'user_id': user_id, 'test_chapter': chapter, 'test_score': score})
-#         i += 2
-
-# # Convert the processed test data into a DataFrame
-# test_df_processed = pd.DataFrame(test_data)
-
-# # Step 7: Merge the journal+panic button data with the test data
-# merged_data = pd.merge(merged_journal_panic, test_df_processed, on='user_id', how='outer')
-
-# # Step 8: Drop rows where all data (except user_id and test_chapter) is missing
-# merged_data_cleaned = merged_data.dropna(subset=['productivity_yes_no', 'productivity_rate', 'panic_button', 'test_chapter'], how='all')
-
-# # Group the merged DataFrame by user_id
-# df = pd.DataFrame(merged_data_cleaned)
-
-# # Function to process panic button counts and test scores
-# def process_group(group):
-#     # Panic button counts
-#     panic_button_series = group['panic_button'].dropna()
-#     panic_button_dict = panic_button_series.value_counts().to_dict()
-
-#     # Test scores aggregation
-#     test_scores = group[['test_chapter', 'test_score']].dropna()
-#     test_scores['test_score'] = pd.to_numeric(test_scores['test_score'], errors='coerce')
-
-#     # Create the test_scores_dict excluding NaN values
-#     test_scores_dict = test_scores.groupby('test_chapter')['test_score'].mean().dropna().to_dict()
-
-#     return pd.Series({
-#         'productivity_yes_no': group['productivity_yes_no'].iloc[0],
-#         'productivity_rate': group['productivity_rate'].iloc[0],
-#         'panic_button': panic_button_dict,
-#         'test_scores': test_scores_dict
-#     })
-
-# # Apply the group processing function
-# merged_df = df.groupby('user_id').apply(process_group).reset_index()
-
-# # Step 9: Calculate potential score
-# # Panic button weightages
-# academic_weights = {'BACKLOGS': -5, 'MISSED CLASSES': -4, 'NOT UNDERSTANDING': -3, 'BAD MARKS': -3, 'LACK OF MOTIVATION': -3}
-# non_academic_weights = {'EMOTIONAL FACTORS': -3, 'PROCRASTINATE': -2, 'LOST INTEREST': -4, 'LACK OF FOCUS': -2, 'GOALS NOT ACHIEVED': -2, 'LACK OF DISCIPLINE': -2}
-
-# # Max weighted panic score
-# max_weighted_panic_score = sum([max(academic_weights.values()) * 3, max(non_academic_weights.values()) * 3])
-
-# # Function to calculate potential score
-# def calculate_potential_score(row):
-#     # Test score normalization (70% weightage)
-#     if row['test_scores']:  # Check if test_scores is not empty
-#         avg_test_score = sum(row['test_scores'].values()) / len(row['test_scores'])
-#         test_score_normalized = (avg_test_score / 40) * 70  # Scale test score to 70
-#     else:
-#         test_score_normalized = 0  # Default value for users with no test scores
-
-#     # Panic score calculation (20% weightage)
-#     student_panic_score = 0
-#     if row['panic_button']:  # Ensure panic_button is not NaN or empty
-#         for factor, count in row['panic_button'].items():
-#             if factor in academic_weights:
-#                 student_panic_score += academic_weights[factor] * count
-#             elif factor in non_academic_weights:
-#                 student_panic_score += non_academic_weights[factor] * count
-#     else:
-#         student_panic_score = 0  # Default if no panic button issues
-
-#     # Panic score normalized to 20
-#     panic_score = 20 * (1 - (student_panic_score / max_weighted_panic_score) if max_weighted_panic_score != 0 else 1)
-
-#     # Journal score calculation (10% weightage)
-#     if pd.notna(row['productivity_yes_no']) and row['productivity_yes_no'] == 'Yes':
-#         if pd.notna(row['productivity_rate']):
-#             journal_score = (float(row['productivity_rate']) / 10) * 10  # Scale journal score to 10
-#         else:
-#             journal_score = 0  # Default if productivity_rate is missing
-#     elif pd.notna(row['productivity_yes_no']) and row['productivity_yes_no'] == 'No':
-#         if pd.notna(row['productivity_rate']):
-#             journal_score = (float(row['productivity_rate']) / 10) * 5  # Scale journal score to 5 if "No"
-#         else:
-#             journal_score = 0  # Default if productivity_rate is missing
-#     else:
-#         journal_score = 0  # Default if productivity_yes_no is missing
-
-#     # Total score based on new weightages
-#     total_potential_score = test_score_normalized + panic_score + journal_score
-#     return total_potential_score
-
-# # Apply potential score calculation to the dataframe
-# merged_df['potential_score'] = merged_df.apply(calculate_potential_score, axis=1)
-# merged_df['potential_score'] = merged_df['potential_score'].round(2)
-
-# # Step 10: Sort by potential score
-# sorted_df = merged_df[['user_id', 'potential_score']].sort_values(by='potential_score', ascending=False)
-
-# # Step 11: Define API endpoint to get the sorted potential scores
-# @app.get("/sorted-potential-scores")
-# async def get_sorted_potential_scores():
-#     try:
-#         result = sorted_df.to_dict(orient="records")
-#         return {"sorted_scores": result}
-#     except Exception as e:
-#         raise HTTPException(status_code=500, detail=str(e))
-
-
 # Import necessary libraries
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
@@ -229,75 +40,97 @@ def get_credentials():
 creds = get_credentials()
 client = gspread.authorize(creds)
 
-# Step 3: Define function to set paths based on coachingCode
-def get_sheet_paths(coachingCode: str):
-    if coachingCode == '1919':
-        journal_file_path = 'https://docs.google.com/spreadsheets/d/1EFf2lr4A10nt4RhIqxCD_fxe-l3sXH09II0TEkMmvhA/edit?usp=drive_link'
-        panic_button_file_path = 'https://docs.google.com/spreadsheets/d/1nFZGkCvRV6qS-mhsORhX3dxI0JSge32_UwWgWKl3eyw/edit?usp=drive_link'
-        test_file_path = 'https://docs.google.com/spreadsheets/d/13PUHySUXWtKBusjugoe7Dbsm39PwBUfG4tGLipspIx4/edit?usp=drive_link'
-    else:
-        # Handle cases for other coaching codes, set default or raise an error
-        raise HTTPException(status_code=404, detail="Coaching code not found")
-
-    return journal_file_path, panic_button_file_path, test_file_path
-
-# Step 4: Define function to fetch and process data from Google Sheets
-def fetch_and_process_data(journal_file_path, panic_button_file_path, test_file_path):
-    # Open Google Sheets using the URLs
-    journal_file = client.open_by_url(journal_file_path).worksheet('Sheet1')
-    panic_button_file = client.open_by_url(panic_button_file_path).worksheet('Sheet1')
-    test_file = client.open_by_url(test_file_path).worksheet('Sheet1')
-
-    # Convert the sheets into Pandas DataFrames
-    journal_df = pd.DataFrame(journal_file.get_all_values())
-    panic_button_df = pd.DataFrame(panic_button_file.get_all_values())
-    test_df = pd.DataFrame(test_file.get_all_values())
-
-    # Label the columns manually since there are no headers
-    journal_df.columns = ['user_id', 'productivity_yes_no', 'productivity_rate']
-    panic_button_df.columns = ['user_id', 'panic_button']
-
-    # Initialize a list for the merged data
-    merged_data = []
-
-    # Group panic buttons by user_id and combine into a single comma-separated string
-    panic_button_grouped = panic_button_df.groupby('user_id')['panic_button'].apply(lambda x: ','.join(x)).reset_index()
-
-    # Merge journal and panic button data
-    merged_journal_panic = pd.merge(journal_df, panic_button_grouped, on='user_id', how='outer')
+# Input the paths and coaching code
+journal_file_path = ''
+panic_button_file_path = ''
+test_file_path = ''
+coachingCode = '1919'
+
+if coachingCode == '1919':
+    journal_file_path = 'https://docs.google.com/spreadsheets/d/1EFf2lr4A10nt4RhIqxCD_fxe-l3sXH09II0TEkMmvhA/edit?usp=drive_link'
+    panic_button_file_path = 'https://docs.google.com/spreadsheets/d/1nFZGkCvRV6qS-mhsORhX3dxI0JSge32_UwWgWKl3eyw/edit?usp=drive_link'
+    test_file_path = 'https://docs.google.com/spreadsheets/d/13PUHySUXWtKBusjugoe7Dbsm39PwBUfG4tGLipspIx4/edit?usp=drive_link'
+
+# Step 3: Open Google Sheets using the URLs
+journal_file = client.open_by_url(journal_file_path).worksheet('Sheet1')
+panic_button_file = client.open_by_url(panic_button_file_path).worksheet('Sheet1')  # Fixed missing part
+test_file = client.open_by_url(test_file_path).worksheet('Sheet1')
+
+# Step 4: Convert the sheets into Pandas DataFrames
+journal_df = pd.DataFrame(journal_file.get_all_values())
+panic_button_df = pd.DataFrame(panic_button_file.get_all_values())
+test_df = pd.DataFrame(test_file.get_all_values())
+
+# Label the columns manually since there are no headers
+journal_df.columns = ['user_id', 'productivity_yes_no', 'productivity_rate']
+panic_button_df.columns = ['user_id', 'panic_button']
+
+# Initialize a list for the merged data
+merged_data = []
+
+# Step 5: Group panic buttons by user_id and combine into a single comma-separated string
+panic_button_grouped = panic_button_df.groupby('user_id')['panic_button'].apply(lambda x: ','.join(x)).reset_index()
+
+# Merge journal and panic button data
+merged_journal_panic = pd.merge(journal_df, panic_button_grouped, on='user_id', how='outer')
+
+# Step 6: Process the test data
+test_data = []
+for index, row in test_df.iterrows():
+    user_id = row[0]
+    i = 1
+    while i < len(row) and pd.notna(row[i]):  # Process chapter and score pairs
+        chapter = row[i].lower().strip()
+        score = row[i + 1]
+        if pd.notna(score):
+            test_data.append({'user_id': user_id, 'test_chapter': chapter, 'test_score': score})
+        i += 2
+
+# Convert the processed test data into a DataFrame
+test_df_processed = pd.DataFrame(test_data)
+
+# Step 7: Merge the journal+panic button data with the test data
+merged_data = pd.merge(merged_journal_panic, test_df_processed, on='user_id', how='outer')
+
+# Step 8: Drop rows where all data (except user_id and test_chapter) is missing
+merged_data_cleaned = merged_data.dropna(subset=['productivity_yes_no', 'productivity_rate', 'panic_button', 'test_chapter'], how='all')
+
+# Group the merged DataFrame by user_id
+df = pd.DataFrame(merged_data_cleaned)
+
+# Function to process panic button counts and test scores
+def process_group(group):
+    # Panic button counts
+    panic_button_series = group['panic_button'].dropna()
+    panic_button_dict = panic_button_series.value_counts().to_dict()
 
-    # Process the test data
-    test_data = []
-    for index, row in test_df.iterrows():
-        user_id = row[0]
-        i = 1
-        while i < len(row) and pd.notna(row[i]):  # Process chapter and score pairs
-            chapter = row[i].lower().strip()
-            score = row[i + 1]
-            if pd.notna(score):
-                test_data.append({'user_id': user_id, 'test_chapter': chapter, 'test_score': score})
-            i += 2
+    # Test scores aggregation
+    test_scores = group[['test_chapter', 'test_score']].dropna()
+    test_scores['test_score'] = pd.to_numeric(test_scores['test_score'], errors='coerce')
 
-    # Convert the processed test data into a DataFrame
-    test_df_processed = pd.DataFrame(test_data)
+    # Create the test_scores_dict excluding NaN values
+    test_scores_dict = test_scores.groupby('test_chapter')['test_score'].mean().dropna().to_dict()
 
-    # Merge the journal+panic button data with the test data
-    merged_data = pd.merge(merged_journal_panic, test_df_processed, on='user_id', how='outer')
+    return pd.Series({
+        'productivity_yes_no': group['productivity_yes_no'].iloc[0],
+        'productivity_rate': group['productivity_rate'].iloc[0],
+        'panic_button': panic_button_dict,
+        'test_scores': test_scores_dict
+    })
 
-    # Drop rows where all data (except user_id and test_chapter) is missing
-    merged_data_cleaned = merged_data.dropna(subset=['productivity_yes_no', 'productivity_rate', 'panic_button', 'test_chapter'], how='all')
+# Apply the group processing function
+merged_df = df.groupby('user_id').apply(process_group).reset_index()
 
-    # Group the merged DataFrame by user_id
-    df = pd.DataFrame(merged_data_cleaned)
+# Step 9: Calculate potential score
+# Panic button weightages
+academic_weights = {'BACKLOGS': -5, 'MISSED CLASSES': -4, 'NOT UNDERSTANDING': -3, 'BAD MARKS': -3, 'LACK OF MOTIVATION': -3}
+non_academic_weights = {'EMOTIONAL FACTORS': -3, 'PROCRASTINATE': -2, 'LOST INTEREST': -4, 'LACK OF FOCUS': -2, 'GOALS NOT ACHIEVED': -2, 'LACK OF DISCIPLINE': -2}
 
-    return df
+# Max weighted panic score
+max_weighted_panic_score = sum([max(academic_weights.values()) * 3, max(non_academic_weights.values()) * 3])
 
-# Step 5: Define function to calculate potential score
+# Function to calculate potential score
 def calculate_potential_score(row):
-    academic_weights = {'BACKLOGS': -5, 'MISSED CLASSES': -4, 'NOT UNDERSTANDING': -3, 'BAD MARKS': -3, 'LACK OF MOTIVATION': -3}
-    non_academic_weights = {'EMOTIONAL FACTORS': -3, 'PROCRASTINATE': -2, 'LOST INTEREST': -4, 'LACK OF FOCUS': -2, 'GOALS NOT ACHIEVED': -2, 'LACK OF DISCIPLINE': -2}
-    max_weighted_panic_score = sum([max(academic_weights.values()) * 3, max(non_academic_weights.values()) * 3])
-
     # Test score normalization (70% weightage)
     if row['test_scores']:  # Check if test_scores is not empty
         avg_test_score = sum(row['test_scores'].values()) / len(row['test_scores'])
@@ -337,39 +170,20 @@ def calculate_potential_score(row):
     total_potential_score = test_score_normalized + panic_score + journal_score
     return total_potential_score
 
-# Step 6: API endpoint to get sorted potential scores based on coachingCode
-@app.get("/sorted-potential-scores/{coachingCode}")
-async def get_sorted_potential_scores(coachingCode: str):
-    try:
-        # Get the appropriate file paths for the given coachingCode
-        journal_file_path, panic_button_file_path, test_file_path = get_sheet_paths(coachingCode)
-
-        # Fetch and process data from Google Sheets
-        df = fetch_and_process_data(journal_file_path, panic_button_file_path, test_file_path)
-
-        # Group the DataFrame by user_id and process
-        merged_df = df.groupby('user_id').apply(process_group).reset_index()
-
-        # Apply potential score calculation
-        merged_df['potential_score'] = merged_df.apply(calculate_potential_score, axis=1)
-        merged_df['potential_score'] = merged_df['potential_score'].round(2)
-
-        # Sort by potential score
-        sorted_df = merged_df.sort_values('potential_score', ascending=False)
-
-        # Convert DataFrame to dictionary
-        sorted_data = sorted_df[['user_id', 'potential_score']].to_dict(orient='records')
+# Apply potential score calculation to the dataframe
+merged_df['potential_score'] = merged_df.apply(calculate_potential_score, axis=1)
+merged_df['potential_score'] = merged_df['potential_score'].round(2)
 
-        return sorted_data
+# Step 10: Sort by potential score
+sorted_df = merged_df[['user_id', 'potential_score']].sort_values(by='potential_score', ascending=False)
 
+# Step 11: Define API endpoint to get the sorted potential scores
+@app.get("/sorted-potential-scores")
+async def get_sorted_potential_scores():
+    try:
+        result = sorted_df.to_dict(orient="records")
+        return {"sorted_scores": result}
     except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Error: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
 
-# Helper function to process group for merging test scores
-def process_group(group):
-    return pd.Series({
-        'productivity_yes_no': group['productivity_yes_no'].iloc[0],
-        'productivity_rate': group['productivity_rate'].iloc[0],
-        'panic_button': {row['panic_button']: 1 for index, row in group.iterrows() if pd.notna(row['panic_button'])},
-        'test_scores': {row['test_chapter']: float(row['test_score']) for index, row in group.iterrows() if pd.notna(row['test_chapter']) and pd.notna(row['test_score'])}
-    })