Create NILI.py

NILI.py

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+import os
+import openai
+import datetime
+import utilities.parsed_policies as policies
+import utilities.engagements as engagements
+import utilities.pickfromlist as pick
+import gradio as gr
+import json
+from jinja2 import Template
+import csv
+
+# openai.api_key = os.getenv("OPENAI_API_KEY")
+openai.api_key = "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"
+
+#Write log file function
+def append_to_qalog(your_role, school_selection, output_format, input_text, gpt_response,response_time,question_cost):
+    # Define the filename
+    filename = "qalog.csv"
+    
+    # Check if file exists to decide if headers are needed
+    file_exists = os.path.isfile(filename)
+    
+    # Open the file in append mode
+    with open(filename, 'a', newline='') as csvfile:
+        writer = csv.DictWriter(csvfile, fieldnames=["your_role", "school_selection", "output_format", "input_text", "gpt_response","response_time","question_cost"])
+        
+        # If the file didn't exist, write the headers first
+        if not file_exists:
+            writer.writeheader()
+        
+        # Append the data
+        writer.writerow({
+            "your_role": your_role,
+            "school_selection": school_selection,
+            "output_format": output_format,
+            "input_text": input_text,
+            "gpt_response": gpt_response,
+            "response_time": response_time,
+            "question_cost": question_cost
+        })
+
+#Chatbot Function
+def chatbot(your_role,school_selection,output_format,input_text):
+
+    start_time = datetime.datetime.now()
+
+    # Read in the prompt header for engagement analysis
+    with open('prompts/nili_analysis_header.txt', 'r') as file:
+        header = file.read()
+
+    # school_selection holds an array of one or more schools, so create a comma delimited str to process.
+    #print(school_selection)
+    str=",".join(school_selection)
+
+    # Read the Hydrated policies
+
+    # Split the input string by commas to get the list of university names
+    university_names = str.split(',')
+
+    policies = ''
+
+    for university in university_names:
+        # Construct the file path
+        file_path = f"nili_hydrated_policies/{university}.txt"
+
+        try:
+            # Open the file and read its contents
+            with open(file_path, 'r', encoding='utf-8') as file:
+                content = file.read()
+
+                # If 'policies' already has content, add separator before appending
+                if policies:
+                    policies += "\n----------\n"
+                
+                policies += content
+        
+        except FileNotFoundError:
+
+            print(f"File for {university} not found!")
+
+    with open('combined_policies.txt', 'w', encoding='utf-8') as out_file:
+        out_file.write(policies)
+
+    #print(policies)
+    
+    # Insert into prompt
+    with open("prompts/nili_engagement_user_v2.j2", "r") as file:
+    #with open("prompts/nili_engagement_user_v3.j2", "r") as file:    
+        template_content = file.read()
+
+    # 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)
+
+    with open('analysis_input.txt', 'w', encoding='utf-8') as out_file:
+        out_file.write(analysis_input)
+
+    response = openai.ChatCompletion.create(
+        model="gpt-4",
+      # model="gpt-3.5-turbo",
+        temperature=0,
+        messages=[
+            {
+                "role": "system",
+                "content": header
+            },
+            {
+                "role": "user",
+                "content": analysis_input
+            }
+          ]
+        )
+
+    gpt_response = response.choices[0].message["content"]
+
+    tokens_used = response.usage
+
+    question_cost = (tokens_used.get('total_tokens', 0) / 1000) * .03
+
+    #print(question_cost)
+
+    with open('response.txt', 'w', encoding='utf-8') as out_file:
+        out_file.write(gpt_response)
+
+    """print(ui_examples)
+
+    # Read the csv file
+    with open('examples/examples_v3.csv', 'r') as csvfile:
+        reader = csv.reader(csvfile)
+        next(reader)  # Skip the header row
+        for row in reader:
+            ui_examples.append([convert_to_none(item) for item in row])
+
+    print(ui_examples)"""
+
+    # Define the style and content
+    label_text = "NILI Response"
+    color = "#6562F4"
+    background_color = "white"
+    border_radius = "10px"  # You can adjust this value as needed
+
+    response_label = f'<h3 style="color: {color}; background-color: {background_color}; border-radius: {border_radius}; padding: 10px;display: inline-block;">{label_text}</h3>'
+
+    end_time = datetime.datetime.now()
+
+    response_time = end_time - start_time
+
+    append_to_qalog(your_role, school_selection, output_format, input_text, gpt_response,response_time,question_cost)
+
+    return response_label,gpt_response
+
+def load_example(example_id):
+    global ui_examples
+    return ui_examples[example_id][0]
+
+
+
+#Gradio UI
+CIMStheme = gr.themes.Soft().set(button_primary_background_fill='#6562F4')
+
+# Initialize an empty list to store the examples
+ui_examples = []
+
+nili_response = "Test"
+
+# Function to convert 'None' string to None object
+def convert_to_none(value):
+    return None if value == 'None' else value
+
+# Read the csv file
+with open('examples/examples_v2.csv', 'r') as csvfile:
+    reader = csv.reader(csvfile)
+    next(reader)  # Skip the header row
+    for row in reader:
+        ui_examples.append([convert_to_none(item) for item in row])
+
+#response_label='<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Formatted Text</title><style>.formatted-text {color: black;font-weight: bold;text-align: left;}</style></head><body><div class="formatted-text">NILI Response</div></body></html>'
+# Define the style and content
+label_text = "NILI Response"
+color = "#6562F4"
+background_color = "white"
+border_radius = "10px"  # You can adjust this value as needed
+
+response_label = f'<h3 style="color: {color}; background-color: {background_color}; border-radius: {border_radius}; padding: 10px;display: inline-block;">{label_text}</h3>'
+
+with gr.Blocks(CIMStheme) as iface:
+    with gr.Row():
+        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")
+        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(["Stanford","CU","LSU","CMU","Tulane","Cal Poly","Long Beach State University","Auburn"],multiselect=True,info="Select one or more schools. This will help set the context of your question.",label="School Context"),
+                         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")],
+                     outputs=[
+                         gr.components.Markdown(response_label),
+                         gr.components.HTML(label="NILI Response")
+                         ],
+                     description="Ask any question about Name, Image, Likeness (NIL)",
+                     allow_flagging="manual",
+                     examples=ui_examples,
+                     flagging_options=["The response is incorrect","The response is inappropriate","The response doesn't make sense"]
+                      )
+    with gr.Row():
+        with gr.Column():
+            gr.HTML('<center><i>CIMS.AI Confidential 2023</i></center>')
+
+iface.launch(share=True)