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Dockerfile

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+# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+# # 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, Query
+# from pydantic import BaseModel
+# import gspread
+# from google.oauth2.service_account import Credentials
+# import pandas as pd
+# from collections import defaultdict
+# import os
+# from fastapi.middleware.cors import CORSMiddleware
+# # Initialize the FastAPI app
+# app = FastAPI()
+# app.add_middleware(
+#     CORSMiddleware,
+#     allow_origins=["*"],  # You can specify domains instead of "*" to restrict access
+#     allow_credentials=True,
+#     allow_methods=["*"],  # Allows all HTTP methods (POST, GET, OPTIONS, etc.)
+#     allow_headers=["*"],  # Allows all headers
+# )
+# # 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)
+
+# # Function to get file paths based on coaching code
+# def get_file_paths(coaching_code):
+#     if coaching_code == '1919':
+#         return {
+#             'journal': 'https://docs.google.com/spreadsheets/d/1EFf2lr4A10nt4RhIqxCD_fxe-l3sXH09II0TEkMmvhA/edit?usp=drive_link',
+#             'panic_button': 'https://docs.google.com/spreadsheets/d/1nFZGkCvRV6qS-mhsORhX3dxI0JSge32_UwWgWKl3eyw/edit?usp=drive_link',
+#             'test': 'https://docs.google.com/spreadsheets/d/13PUHySUXWtKBusjugoe7Dbsm39PwBUfG4tGLipspIx4/edit?usp=drive_link'
+#         }
+#     if coaching_code == '0946':
+#         return {
+#             'journal': 'https://docs.google.com/spreadsheets/d/1c1TkL7sOUvFn6UPz3gwp135UVjOou9u1weohWzpmx6I/edit?usp=drive_link',
+#             'panic_button': 'https://docs.google.com/spreadsheets/d/1RhbPQnNNBUthKKJyoW4q6x3uaWl1YSqmsFlfJ2THphE/edit?usp=drive_link',
+#             'test': 'https://docs.google.com/spreadsheets/d/1JO5wDkfl2fr2ZQenI8OEu48jkWm48veYN1Fsw5Ctkzw/edit?usp=drive_link'
+#         }
+# # 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
+
+# # Step 11: Define API endpoint to get the sorted potential scores
+# @app.get("/sorted-potential-scores")
+# async def get_sorted_potential_scores(coaching_code: str = Query(..., description="Coaching code to determine file paths")):
+#     try:
+#         file_paths = get_file_paths(coaching_code)
+#         if not file_paths:
+#             raise HTTPException(status_code=400, detail="Invalid coaching code")
+#         print("A");
+#         # Open Google Sheets using the URLs
+#         journal_file = client.open_by_url(file_paths['journal']).worksheet('Sheet1')
+#         panic_button_file = client.open_by_url(file_paths['panic_button']).worksheet('Sheet1')
+#         test_file = client.open_by_url(file_paths['test']).worksheet('Sheet1')
+#         print("B");
+#         # 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())
+#         print("C");
+#         # 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']
+#         print("D")
+#         # 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()
+#         print("E")
+#         # Merge journal and panic button data
+#         merged_journal_panic = pd.merge(journal_df, panic_button_grouped, on='user_id', how='outer')
+#         print("F")
+#         # 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
+#         print("G")
+#         # Convert the processed test data into a DataFrame
+#         test_df_processed = pd.DataFrame(test_data)
+#         print("H")
+#         # 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')
+#         print("I")
+#         # 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')
+#         print("J")
+#         # Group the merged DataFrame by user_id
+#         df = pd.DataFrame(merged_data_cleaned)
+#         print("K")
+#         # 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()
+#         print("L")
+#         # Calculate potential scores and sort
+#         merged_df['potential_score'] = merged_df.apply(calculate_potential_score, axis=1)
+#         merged_df['potential_score'] = merged_df['potential_score'].round(2)
+#         sorted_df = merged_df[['user_id', 'potential_score']].sort_values(by='potential_score', ascending=False)
+#         print("M")
+#         result = sorted_df.to_dict(orient="records")
+#         return {"sorted_scores": result}
+#     except Exception as e:
+#         raise HTTPException(status_code=500, detail=str(e))
+
+
+
+
+from fastapi import FastAPI, HTTPException, Query
+from pydantic import BaseModel
+import gspread
+from google.oauth2.service_account import Credentials
+import pandas as pd
+from collections import defaultdict
+import os
+from fastapi.middleware.cors import CORSMiddleware
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # You can specify domains instead of "*" to restrict access
+    allow_credentials=True,
+    allow_methods=["*"],  # Allows all HTTP methods (POST, GET, OPTIONS, etc.)
+    allow_headers=["*"],  # Allows all headers
+)
+
+# Model for request
+class CoachingCodeRequest(BaseModel):
+    coachingCode: str
+
+# Function to get 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
+
+
+# Select files based on coaching code
+def select_files(coaching_code):
+    creds = get_credentials()
+    client = gspread.authorize(creds)
+
+    if coaching_code == "1919":
+        journal_file = client.open_by_url('https://docs.google.com/spreadsheets/d/1EFf2lr4A10nt4RhIqxCD_fxe-l3sXH09II0TEkMmvhA/edit?gid=0#gid=0').worksheet('Sheet1')
+        panic_button_file = client.open_by_url('https://docs.google.com/spreadsheets/d/1nFZGkCvRV6qS-mhsORhX3dxI0JSge32_UwWgWKl3eyw/edit?gid=0#gid=0').worksheet('Sheet1')
+        test_file = client.open_by_url('https://docs.google.com/spreadsheets/d/13PUHySUXWtKBusjugoe7Dbsm39PwBUfG4tGLipspIx4/edit?gid=0#gid=0').worksheet('Sheet1')
+    else:
+        raise HTTPException(status_code=404, detail="Invalid coaching code")
+    
+    return journal_file, panic_button_file, test_file
+
+# Main route to get sorted scores
+@app.post("/get_sorted_scores")
+async def get_sorted_scores(data: CoachingCodeRequest):
+    journal_file, panic_button_file, test_file = select_files(data.coachingCode)
+
+    # Load data into 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())
+
+    # Processing logic
+    panic_data = []
+    for index, row in panic_button_df.iterrows():
+        user_id = row[0]
+        row_pairs = row[1:].dropna().to_list()[-5:]
+        for i in range(0, len(row_pairs), 2):
+            panic = row_pairs[i].upper().strip()
+            if pd.notna(panic):
+                panic_data.append({'user_id': user_id, 'panic_button': panic})
+    panic_df_processed = pd.DataFrame(panic_data)
+
+    test_data = []
+    for index, row in test_df.iterrows():
+        user_id = row[0]
+        row_pairs = row[1:].dropna().to_list()
+        chapter_scores = {}
+        for i in range(0, len(row_pairs), 2):
+            chapter = row_pairs[i].lower().strip()
+            score = row_pairs[i + 1]
+            if pd.notna(score):
+                if chapter not in chapter_scores:
+                    chapter_scores[chapter] = []
+                chapter_scores[chapter].append(score)
+        for chapter, scores in chapter_scores.items():
+            last_5_scores = scores[-5:]
+            for score in last_5_scores:
+                test_data.append({'user_id': user_id, 'test_chapter': chapter, 'test_score': score})
+    test_df_processed = pd.DataFrame(test_data)
+
+    journal_data = []
+    for index, row in journal_df.iterrows():
+        user_id = row[0]
+        row_pairs = row[1:].dropna().to_list()[-10:]
+        for i in range(0, len(row_pairs), 2):
+            productivity_yes_no = row_pairs[i].lower().strip()
+            productivity_rate = row_pairs[i + 1]
+            if pd.notna(productivity_rate):
+                journal_data.append({'user_id': user_id, 'productivity_yes_no': productivity_yes_no, 'productivity_rate': productivity_rate})
+    journal_df_processed = pd.DataFrame(journal_data)
+
+    merged_journal_panic = pd.merge(panic_df_processed, journal_df_processed, on='user_id', how='outer')
+    merged_data = pd.merge(merged_journal_panic, test_df_processed, on='user_id', how='outer')
+    merged_data_cleaned = merged_data.dropna(subset=['productivity_yes_no', 'productivity_rate', 'panic_button', 'test_chapter'], how='all')
+
+    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
+        })
+
+    # Define scoring weights
+    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])
+
+    def calculate_potential_score(row):
+        if row['test_scores']:
+            avg_test_score = sum(row['test_scores'].values()) / len(row['test_scores'])
+            test_score_normalized = (avg_test_score / 40) * 70
+        else:
+            test_score_normalized = 0
+        student_panic_score = 0
+        if row['panic_button']:
+            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
+        panic_score = 20 * (1 - (student_panic_score / max_weighted_panic_score) if max_weighted_panic_score != 0 else 1)
+        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
+            else:
+                journal_score = 0
+        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
+            else:
+                journal_score = 0
+        else:
+            journal_score = 0
+        total_potential_score = test_score_normalized + panic_score + journal_score
+        return total_potential_score
+
+    merged_df = merged_data_cleaned.groupby('user_id').apply(process_group).reset_index()
+    merged_df['potential_score'] = merged_df.apply(calculate_potential_score, axis=1)
+    merged_df['potential_score'] = merged_df['potential_score'].round(2)
+    sorted_df = merged_df[['user_id', 'potential_score']].sort_values(by='potential_score', ascending=False)
+    result = sorted_df.to_dict(orient="records")
+    
+    return {"sorted_scores": result}
+

requirements.txt

@@ -0,0 +1,5 @@
+fastapi
+uvicorn
+pandas
+gspread
+google-auth