1st commit!

app.py

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+import numpy as np
+import pandas as pd
+import requests
+import os
+import gradio as gr
+import json
+from dotenv import load_dotenv, find_dotenv
+_ = load_dotenv(find_dotenv())
+
+from predibase import Predibase, FinetuningConfig, DeploymentConfig
+
+# Get a KEY from https://app.predibase.com/
+api_token  = os.getenv('PREDIBASE_API_KEY')
+pb = Predibase(api_token=api_token)
+
+adapter_id = 'tour-assistant-model/15'
+lorax_client = pb.deployments.client("solar-1-mini-chat-240612")
+
+
+def extract_json(gen_text, n_shot_learning=0):
+    if(n_shot_learning == -1) :
+        start_index = 0
+    else :
+        start_index = gen_text.index("### Response:\n{") + 14
+    if(n_shot_learning > 0) :
+        for i in range(0, n_shot_learning):
+            gen_text = gen_text[start_index:]
+            start_index = gen_text.index("### Response:\n{") + 14
+    end_index = gen_text.find("}\n\n### ") + 1
+    return gen_text[start_index:end_index]
+
+def get_completion(prompt):
+    return lorax_client.generate(prompt, adapter_id=adapter_id, max_new_tokens=1000).generated_text
+
+def greet(input):
+    sys_str = "You are a helpful support assistant. Answer the following question."
+    qa_list = []
+    n_prompt_list = []
+    # qa_list.append({
+    #     "question": "What are the benefits of joining a union?",
+    #     "answer": "Collective bargaining of salary."
+    # })
+	#
+    # qa_list.append({
+    #     "question": "How much are union dues, and what do they cover?",
+    #     "answer": "The union dues for our union is 3%."
+    # })
+	#
+    # qa_list.append({
+    #     "question": "How does the union handle grievances and disputes?",
+    #     "answer": "There will be a panel to oversee disputes"
+    # })
+	#
+    # qa_list.append({
+    #     "question": "Will joining a union affect my job security?",
+    #     "answer": "No."
+    # })
+	#
+    # qa_list.append({
+    #     "question": "What is the process for joining a union?",
+    #     "answer": "Please use the contact form."
+    # })
+	#
+    # qa_list.append({
+    #     "question": "How do unions negotiate contracts with employers?",
+    #     "answer": "Our dear leader will handle the negotiations."
+    # })
+	#
+    # qa_list.append({
+    #     "question": "What role do I play as a union member?",
+    #     "answer": "You will be invited to our monthly picnics"
+    # })
+	#
+    # qa_list.append({
+    #     "question": "How do unions ensure that employers comply with agreements?",
+    #     "answer": "We will have a monthly meeting for members"
+    # })
+	#
+    # qa_list.append({
+    #     "question": "Can I be forced to join a union?",
+    #     "answer": "What kind of questions is that! Of course no!"
+    # })
+	#
+    # qa_list.append({
+    #     "question": "What happens if I disagree with the union’s decisions?",
+    #     "answer": "We will agree to disagree"
+    # })
+
+    for qna in qa_list:
+      ques_str = qna["question"]
+      ans_str = qna["answer"]
+      n_prompt_list.append(f"""
+<|im_start|>system\n{sys_str}<|im_end|>
+<|im_start|>question\n{ques_str}<|im_end|>
+<|im_start|>answer\n{ans_str}<|im_end|>
+"""
+      )
+
+    n_prompt_str = "\n"
+
+    for prompt in n_prompt_list:
+      n_prompt_str = n_prompt_str + prompt + "\n"
+
+    total_prompt=f"""
+{n_prompt_str}
+
+<|im_start|>system\n{sys_str}<|im_end|>
+<|im_start|>question
+{input}\n<|im_end|>
+<|im_start|>answer
+"""
+
+
+    print("***total_prompt:")
+    print(total_prompt)
+    response = get_completion(total_prompt)
+    #gen_text = response["predictions"][0]["generated_text"]
+    #return json.dumps(extract_json(gen_text, 3))
+
+    ###gen_text = response["choices"][0]["text"]
+
+    #return gen_text
+
+    ###return json.dumps(extract_json(gen_text, -1))
+    return response
+
+    #return json.dumps(response)
+
+#iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+#iface.launch()
+
+#iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Text to find entities", lines=2)], outputs=[gr.HighlightedText(label="Text with entities")], title="NER with dslim/bert-base-NER", description="Find entities using the `dslim/bert-base-NER` model under the hood!", allow_flagging="never", examples=["My name is Andrew and I live in California", "My name is Poli and work at HuggingFace"])
+#iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Question", lines=3)], outputs="json")
+iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Question", lines=3)], outputs="text")
+iface.queue(api_open=True);
+iface.launch()

requirements.txt

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+openai
+python-dotenv
+predibase