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| import google.generativeai as palm | |
| import gradio as gr | |
| import os | |
| import json | |
| from transformers import pipeline | |
| # Set your API key | |
| palm.configure(api_key=os.environ['PALM_KEY']) | |
| # Select the PaLM 2 model | |
| # model = 'models/text-bison-001' | |
| # candlab= ["recharge coins or get subscription", "show list of my friends"] | |
| def responsenew(data): | |
| print(data) | |
| response = palm.chat(messages=f"""From the text given as data below by the user, find out what intention or category does the data fall under out of given 5 intents i.e:\n | |
| 1. purchasing coins\n | |
| 2. viewing friends list\n | |
| 3. viewing groups been joined by the user\n | |
| 4. viewing pages been joined by the user\n | |
| 5. user is saying to view the reminders been shared to the user or by the user\n | |
| data = {data}\n\n | |
| After you are done find out the intent, tell me in one word the intent. Use the following word for your answer, as given below in sequence to the intent:\n | |
| 1. recoin\n | |
| 2. view_friends\n | |
| 3. view_groups\n | |
| 4. view_pages\n | |
| 5. sharedrem\n""") | |
| respo = { | |
| "message": response.last, | |
| "action": "nothing", | |
| "function": "nothing" | |
| } | |
| # user_intent = pipeline("zero-shot-classification", model="facebook/bart-large-mnli") | |
| # if "payment" in response.last: | |
| # respo = { | |
| # "message": "Click the button below to view Premium Services and Coin Recharge options: ", | |
| # "action": "payment", | |
| # "function": "nothing" | |
| # } | |
| # elif "friends" in response.last: | |
| # respo = { | |
| # "message": "Slide left or right profiles or tap on 'My Friends' to view list of your friends: ", | |
| # "action": "show_friends", | |
| # "function": "nothing" | |
| # } | |
| # else: | |
| # respo = { | |
| # "message": response.last, | |
| # "action": "nothing", | |
| # "function": "nothing" | |
| # } | |
| return json.dumps(respo) | |
| gradio_interface = gr.Interface( | |
| fn = responsenew, | |
| inputs = "text", | |
| outputs = "text" | |
| ) | |
| gradio_interface.launch() | |