Update app.py

app.py

@@ -1,31 +1,57 @@
-import pandas as pd
+import gradio as gr
+import openai
+import random
+import time
+import os
 
-# Define the criteria, alternatives, and weights
-criteria = ['Cost', 'Quality', 'Delivery Time']
-alternatives = ['Option A', 'Option B', 'Option C']
-weights = [0.5, 0.3, 0.2]
+# Set up OpenAI API key
+openai.api_key = os.getenv('APICode2')
+system_message = {"role": "system", "content": "You are a helpful assistant."}
 
-# Create a DataFrame to hold the evaluation scores
-evaluation_scores = [
-    [7, 9, 5],
-    [8, 7, 6],
-    [6, 8, 7]
-]
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            msg = gr.Textbox()
+            btn = gr.Button(value="send")
+    with gr.Row():
+        chatbot = gr.Chatbot()
+    with gr.Row():
+        prt = gr.Button("Save in memory")
+        clear = gr.Button("Clear")
+        state = gr.State([])
 
-decision_matrix = pd.DataFrame(evaluation_scores, columns=criteria, index=alternatives)
+    def user(user_message, history):
+        return "", history + [[user_message, None]]
 
-# Normalize and weight the scores
-normalized_scores = decision_matrix / decision_matrix.max()
-weighted_scores = normalized_scores * weights
+    def bot(history, messages_history):
+        user_message = history[-1][0]
+        bot_message, messages_history = ask_gpt(user_message, messages_history)
+        messages_history += [{"role": "assistant", "content": bot_message}]
+        history[-1][1] = bot_message
+        time.sleep(1)
+        return history, messages_history
 
-# Calculate the total score for each alternative
-total_scores = weighted_scores.sum(axis=1)
+    def ask_gpt(message, messages_history):
+        messages_history += [{"role": "user", "content": message}]
+        response = openai.ChatCompletion.create(
+            model="gpt-4",
+            messages=messages_history
+        )
+        return response['choices'][0]['message']['content'], messages_history
 
-# Determine the best alternative
-best_alternative = total_scores.idxmax()
+    def init_history(messages_history):
+        messages_history = []
+        messages_history += [system_message]
+        return messages_history
 
-print("Decision Matrix:\n", decision_matrix)
-print("\nNormalized Scores:\n", normalized_scores)
-print("\nWeighted Scores:\n", weighted_scores)
-print("\nTotal Scores:\n", total_scores)
-print("\nBest Alternative:", best_alternative)
+    def printscr(chatbot):
+        print("\n\nYour chat hiatory:\n")
+        for i in range(len(chatbot)):
+            print(f"User input:{chatbot[i][0]}")
+            print(f"Bot output:{chatbot[i][1]}")
+    
+    btn.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(bot, [chatbot, state], [chatbot, state])
+    prt.click(printscr, chatbot, None)
+    clear.click(lambda: None, None, chatbot, queue=False).success(init_history, [state], [state])
+
+demo.launch()