Update app.py

app.py

@@ -13,55 +13,52 @@ openai.api_key = os.environ.get('OPENAI_API_KEY')
 airtable_api_key = os.environ.get('AIRTABLE_API_KEY')
 
 # Airtable table names
-policies_table_name = 'tbla6PC65qZfqdJhE'
+schools_table_name = 'tblFh7tguTjbjWms7'
 prompts_table_name = 'tblYIZEB8m6JkGDEP'
 qalog_table_name = 'tbl4oNgFPWM5xH1XO'
 examples_table_name = 'tblu7sraOEmRgEGkp'
 users_table_name = 'tblLNe5ZL47SvrAEk'
 user_log_table_name = 'tblrlTsRrkl6BqMAJ'
+students_table_name = 'tbl9h106z0Jg34DQX'
+scoutlog_table_name = 'tblF30p0krA1FwOiD'
+sports_table_name = 'tbler8HTzD0Jm8Woi'
 
 # Define the style and content for the response field
-label_text = "NILI Response"
+label_text = "Scout Response"
 color = "#6562F4"
 background_color = "white"
 border_radius = "10px"
 response_label = f'<h3 style="color: {color}; background-color: {background_color}; border-radius: {border_radius}; padding: 10px;display: inline-block;">{label_text}</h3>'
 
-#Airtable Base ID
 base_id = 'appcUK3hUWC7GM2Kb'
 
-#Name of the prompt temlate record
-prompt_name = "NILI_v1"
-
 #App name for user login logging
-app="NILI"
+app="Scout"
 
-#Header for the Airtable requests
 headers = {
     "Authorization": f"Bearer {airtable_api_key}",
     "Content-Type": "application/json",
     "Accept": "application/json",
 }
 
-#Function to trim prompts....not used
+
 def prompt_trim(prompt: str) -> str:
     lines = prompt.split('\n')
     trimmed = '\n'.join([l.strip() for l in lines])
     return trimmed
 
-#Get the policies for the selected schools and concatenate them.
-def get_policies(school_selection):
 
-    airtable_endpoint = f'https://api.airtable.com/v0/{base_id}/{policies_table_name}'    
+def get_students(school_selection):
+    airtable_endpoint = f'https://api.airtable.com/v0/{base_id}/{students_table_name}'
 
     # Parameters for the API request to filter by 'school' field and retrieve 'policy_text'
     params = {
-        'filterByFormula': "OR({})".format(','.join(["school='{}'".format(school) for school in school_selection])),
-        'fields[]': 'policy_text',  # Replace with the name of your field
+        'filterByFormula': "OR({})".format(','.join(["School='{}'".format(school) for school in school_selection])),
+        'fields[]': ['Athlete_Name','Primary_Sport','Position','Facebook_Fans','Twitter_Fans','Instagram_Fans','Interests','Ethnicity','Gender'],
     }
 
     # Initialize an empty string to store concatenated policies
-    concatenated_policies = ''
+    students = ''
 
     #print(params)
 
@@ -76,29 +73,24 @@ def get_policies(school_selection):
 
             # Check if there are records in the response
             if data.get('records'):
-                
-                # Extract the 'policy_text' values from each record and concatenate them
-                for record in data['records']:
-                    policy_text = record['fields']['policy_text']
-                    if concatenated_policies:
-                        concatenated_policies += "\n----------\n"
-                    concatenated_policies += policy_text
+
+                cleaned_data = [{'fields': entry['fields']} for entry in data.get('records')]
+
+                students = json.dumps(cleaned_data, indent=4)
 
             else:
-                print("No records found in the 'policies' table for the selected schools.")
+                print("No records found in the 'students' table for the selected schools.")
         else:
-            print(f"Failed to retrieve data. Status code: {response.status_code}")
+            print(f"Failed to retrieve students data. Status code: {response.status_code}")
     except Exception as e:
         print(f"An error occurred: {str(e)}")
 
-    #print(concatenated_policies)   
+    return students
 
-    return concatenated_policies
 
-#Get a list of School Name from the policies for the UI dropdown
 def get_schools():
 
-    airtable_endpoint = f'https://api.airtable.com/v0/{base_id}/{policies_table_name}'    
+    airtable_endpoint = f'https://api.airtable.com/v0/{base_id}/{schools_table_name}'
 
     # Parameters for the API request to select only the 'school' field
     params = {
@@ -123,22 +115,61 @@ def get_schools():
                 # Extract the 'school' values from each record
                 schools = [record['fields']['school'] for record in data['records']]
 
+            # Print the list of 'school' values
+            # print(schools)
             else:
                 print("No records found in the 'policies' table.")
         else:
-            print(f"Failed to retrieve data. Status code: {response.status_code}")
+            print(f"Failed to retrieve school data. Status code: {response.status_code}")
     except Exception as e:
         print(f"An error occurred: {str(e)}")
 
     return schools
 
-#Get the designated prompt template record
+def get_sports  ():
+
+    airtable_endpoint = f'https://api.airtable.com/v0/{base_id}/{sports_table_name}'
+
+    # Parameters for the API request to select only the 'school' field
+    params = {
+        'fields[]': 'sport',  # Replace with the name of your field
+        'sort[0][field]': 'sport',  # Sort by the 'school' field
+        'sort[0][direction]': 'asc',  # Sort in ascending order
+    }
+
+    schools = ''
+
+    try:
+        # Send a GET request to the Airtable API
+        response = requests.get(airtable_endpoint, headers=headers, params=params)
+
+        # Check if the request was successful (status code 200)
+        if response.status_code == 200:
+            # Parse the JSON response
+            data = response.json()
+
+            # Check if there are records in the response
+            if data.get('records'):
+                # Extract the 'school' values from each record
+                sports = [record['fields']['sport'] for record in data['records']]
+
+            # Print the list of 'school' values
+            # print(sports)
+            else:
+                print("No records found in the 'policies' table.")
+        else:
+            print(f"Failed to retrieve school data. Status code: {response.status_code}")
+    except Exception as e:
+        print(f"An error occurred: {str(e)}")
+
+    return sports
+
 def get_prompt(header, template_content):
 
     airtable_endpoint = f'https://api.airtable.com/v0/{base_id}/{prompts_table_name}'    
 
     params = {
-        'filterByFormula': "prompt_name='NILI_v1'"
+        'filterByFormula': "prompt_name='Scout_v1'",
     }
 
     response = requests.get(airtable_endpoint, headers=headers, params=params)
@@ -178,16 +209,19 @@ def get_examples():
     #print(ui_examples)
            
 
-def append_to_at_qalog(your_role, school_selection, output_format, input_text, gpt_response,response_time,question_cost,prompt_tokens,completion_tokens):
+def append_to_at_scountlog(school_selection, output_format, gender,interests,sports,gpt_response,number_of_athletes,social_media_channel,response_time,question_cost,prompt_tokens,completion_tokens):
 
-    airtable_endpoint = f'https://api.airtable.com/v0/{base_id}/{qalog_table_name}'
+    airtable_endpoint = f'https://api.airtable.com/v0/{base_id}/{scoutlog_table_name}'
     
     # Organize data for Airtable
     new_fields = {
-            'your_role': str(your_role),
             'school_selection': str(school_selection),
+            'gender': str(gender),
+            'social_media_channel': str(social_media_channel),
+            'sports': str(sports),
+            'interests': str(interests),
+            'number_of_athletes': int(number_of_athletes),
             'output_format': str(output_format),
-            'input_text': str(input_text),
             'gpt_response': str(gpt_response),
             'response_time': str(response_time),
             'question_cost': question_cost,
@@ -200,9 +234,12 @@ def append_to_at_qalog(your_role, school_selection, output_format, input_text, g
         'fields': new_fields
         }
     try:
+
         # Post data to Airtable
         response = requests.post(airtable_endpoint, headers=headers, json=data)
 
+        #print(response.json())
+
         # Check for errors
         response.raise_for_status()
 
@@ -216,16 +253,11 @@ def append_to_at_qalog(your_role, school_selection, output_format, input_text, g
 
 
 #Chatbot Function
-def chatbot(your_role,school_selection,output_format,input_text):
+def chatbot(school_selection,gender,social_media_channel,sports,interests,output_format,number_of_athletes):
 
     start_time = datetime.datetime.now()
 
-    # school_selection holds an array of one or more schools
-    #print(school_selection)
-
-    # Read the Hydrated policies
-  
-    policies = get_policies(school_selection)
+    students = get_students(school_selection)
 
     template_content = ''
     header = ''
@@ -235,20 +267,14 @@ def chatbot(your_role,school_selection,output_format,input_text):
     #print(header)
     #print(template_content)
 
-    header_template = Template(header)
-    merged_header = header_template.render(your_role=your_role)
-
     # Create a Jinja2 template from the content
     template = Template(template_content)
 
-    # Render the template with the policy JSON
-    analysis_input = template.render(policies=policies, question=input_text,format=output_format,your_role=your_role)
+    # Render the template with the inputs
+    analysis_input = template.render(students=students, gender=gender,social_media_channel=social_media_channel,sports=sports,interests=interests,format=output_format,number_of_athletes=number_of_athletes)
 
     trimmed_input = prompt_trim(analysis_input)
 
-    with open('analysis_input.txt', 'w', encoding='utf-8') as out_file:
-        out_file.write(trimmed_input)
-  
     response = openai.ChatCompletion.create(
         model="gpt-4",
         #model="gpt-3.5-turbo",
@@ -256,7 +282,7 @@ def chatbot(your_role,school_selection,output_format,input_text):
         messages=[
             {
                 "role": "system",
-                "content": merged_header
+                "content": header
             },
             {
                 "role": "user",
@@ -273,14 +299,15 @@ def chatbot(your_role,school_selection,output_format,input_text):
     prompt_tokens = tokens_used.get('prompt_tokens',)
     completion_tokens = tokens_used.get('completion_tokens', 0)
 
-    """
-    with open('response.txt', 'w', encoding='utf-8') as out_file:
-        out_file.write(gpt_response)
-    """
     end_time = datetime.datetime.now()
+
     response_time = end_time - start_time
 
-    append_to_at_qalog(your_role, school_selection, output_format, input_text, gpt_response,response_time,question_cost,prompt_tokens,completion_tokens)
+    global scout_response
+
+    scout_response = gpt_response
+
+    append_to_at_scountlog(school_selection, output_format, gender,interests,sports,gpt_response,number_of_athletes,social_media_channel,response_time,question_cost,prompt_tokens,completion_tokens)
 
     return response_label,gpt_response
 
@@ -297,14 +324,14 @@ def log_login(username):
     data = {
         'fields': new_fields
         }
-
     try:
-    # Post data to Airtable
-        response = requests.post(airtable_endpoint, headers=headers, json=data)
 
-        # Check for errors
-        response.raise_for_status()
+    # Post data to Airtable
+    response = requests.post(airtable_endpoint, headers=headers, json=data)
 
+    # Check for errors
+    response.raise_for_status()
+    
     except requests.exceptions.HTTPError as http_error:
         # Handle the HTTP error (e.g., log it or display an error message)
         print(f"HTTP error occurred: {http_error}")
@@ -313,7 +340,6 @@ def log_login(username):
         # Handle exceptions, log errors, or raise them as needed
         print(f"An error occurred: {str(e)}")
 
-
 def login_auth(username, password):
 
     airtable_endpoint = f'https://api.airtable.com/v0/{base_id}/{users_table_name}'
@@ -327,13 +353,9 @@ def login_auth(username, password):
 
     if response.status_code == 200:
         data = response.json()
-        #If the matching user/password record is found:
         if data.get('records'):
 
-            #Log that the user logged in
             log_login(username)
-
-            #Set the global logged_in_user variable. This used in the append_to_at_qalog function to track what user asked the question
             global logged_in_user
             logged_in_user = username
 
@@ -351,6 +373,7 @@ ui_examples = []
 school_selection = []
 
 schools = get_schools()
+sports = get_sports()
 
 get_examples()
 
@@ -361,31 +384,31 @@ with gr.Blocks(CIMStheme) as iface:
         with gr.Column(scale=2):
             gr.Image(label="Logo",value="CIMS Logo Purple.png",width=10,show_download_button=False,interactive=False,show_label=False,elem_id="logo",container=False)
         with gr.Column(scale=2):
-            #gr.Textbox(value="# NILI - Powered by CIMS.AI",show_label=False,interactive=False,text_align="center",elem_id="CIMSTitle")
-            gr.Markdown(value="# NILI - Powered by CIMS.AI")
+            gr.Markdown(value="# Scout - Powered by CIMS.AI")
         with gr.Column(scale=2):
             gr.Markdown("")
     with gr.Row():
         with gr.Column():
             gr.Interface(fn=chatbot,
                      inputs=[
-                         gr.components.Dropdown(["Student Athlete","Parent","Athletic Director"],multiselect=False,info="Select a role.",label="User Role", ),
-                         gr.components.Dropdown(schools,multiselect=True,info="Select one or more schools. This will help set the context of your question.",label="School Context"),
+                         gr.components.Dropdown(schools,multiselect=True,info="Select one or more schools. This will select avaliable Student Athletes from the school(s)",label="School Selection"),
+                         gr.components.Dropdown(["Male","Female"],multiselect=True,info="Select one or more.",label="Gender"),
+                         gr.components.Dropdown(["Facebook","Instagram","TikTok","X (Twitter)","YouTube","SnapChat","Twitch"],multiselect=True,info="Select one or more.",label="Social Media Channel"),
+                         gr.components.Dropdown(sports,multiselect=True,info="Select one or more.",label="Sports"),
+                         gr.components.Dropdown(["Fashion","Sports","Food","Cooking","Travel","Arts","Music"],multiselect=True,info="Select one or more.",label="Interests"),
                          gr.components.Dropdown(["Summary","Detailed Analysis","Table"],multiselect=False,info="Select the desired output format.",label="Output Format"),
-                         gr.components.Textbox(lines=5, placeholder="Enter your question here", label="NIL Question")],
+                        gr.components.Dropdown(["1","2","3","4","5","6","7","8","9","10"],multiselect=False,info="How many athletes are you looking for?",label="Number of Athletes")],
                      outputs=[
                          gr.components.Markdown(response_label),
-                         gr.components.HTML(label="NILI Response")
+                         gr.components.HTML(label="Scout Response")
                          ],
-                     description="Ask any question about Name, Image, Likeness (NIL)",
-                     allow_flagging="manual",
-                     examples=ui_examples,
-                     cache_examples=False,
-                     flagging_options=["The response is incorrect","The response is inappropriate","The response doesn't make sense"]
-                      )
+                     description="Use Scout to find and rank Athletes that meet your criteria",
+                         allow_flagging="never",
+                     cache_examples=False
+                         )
     with gr.Row():
         with gr.Column():
             gr.HTML('<center><i>CIMS.AI Confidential 2023</i></center>')
 
-iface.launch(auth=login_auth, auth_message= "Enter your username and password that you received from CIMS.AI. To request a login, please email 'info@cims.ai'")
-#iface.launch(share=True)
+iface.launch(auth=login_auth, auth_message= "Enter your username and password that you received from CIMS.AI. To request a login, please email 'info@cims.ai'",share=True)
+#iface.launch()