Tweaks

.gitignore

@@ -157,4 +157,7 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+#.idea/
+
+# Local development
+data/

README.md

@@ -14,6 +14,7 @@ A basic example of an RLHF interface with a Gradio app.
 **Instructions for someone to use for their own project:**
 
 *Setting up the Space*
+
 1. Clone this repo and deploy it on your own Hugging Face space.
 2. Add the following secrets to your space:
    - `HF_TOKEN`: One of your Hugging Face tokens.
@@ -24,11 +25,21 @@ A basic example of an RLHF interface with a Gradio app.
    huggingface.co, the app will use your token to automatically store new HITs
    in your dataset. Setting `FORCE_PUSH` to "yes" ensures that your repo will
    force push changes to the dataset during data collection. Otherwise,
-   accidental manual changes to your dataset could result in your space gettin
+   accidental manual changes to your dataset could result in your space getting
    merge conflicts as it automatically tries to push the dataset to the hub. For
    local development, add these three keys to a `.env` file, and consider setting
    `FORCE_PUSH` to "no".
+
+To launch the Space locally, run:
+
+```bash
+python app.py
+```
+
+The app will then be available at http://127.0.0.1:7860
+
 *Running Data Collection*
+
 1. On your local repo that you pulled, create a copy of `config.py.example`,
    just called `config.py`. Now, put keys from your AWS account in `config.py`.
    These keys should be for an AWS account that has the

app.py

@@ -1,20 +1,21 @@
 # Basic example for doing model-in-the-loop dynamic adversarial data collection
 # using Gradio Blocks.
+import json
 import os
+import threading
 import uuid
+from pathlib import Path
 from urllib.parse import parse_qs
+
 import gradio as gr
-from huggingface_hub import Repository
 from dotenv import load_dotenv
-from pathlib import Path
-import json
-from utils import force_git_push
-import threading
-
-from langchain.prompts import load_prompt
-from langchain import LLMChain, PromptTemplate
-from langchain.llms import HuggingFaceHub
+from huggingface_hub import Repository
+from langchain import ConversationChain
 from langchain.chains.conversation.memory import ConversationBufferMemory
+from langchain.llms import HuggingFaceHub
+from langchain.prompts import load_prompt
+
+from utils import force_git_push
 
 # These variables are for storing the mturk HITs in a Hugging Face dataset.
 if Path(".env").is_file():
@@ -22,10 +23,8 @@ if Path(".env").is_file():
 DATASET_REPO_URL = os.getenv("DATASET_REPO_URL")
 FORCE_PUSH = os.getenv("FORCE_PUSH")
 HF_TOKEN = os.getenv("HF_TOKEN")
-
 PROMPT_TEMPLATES = Path("prompt_templates")
-
-# Set env variable for langchain 
+# Set env variable for langchain to communicate with Hugging Face Hub
 os.environ["HUGGINGFACEHUB_API_TOKEN"] = HF_TOKEN
 
 DATA_FILENAME = "data.jsonl"
@@ -59,26 +58,45 @@ asynchronous_push(f_stop)
 # Now let's run the app!
 prompt = load_prompt(PROMPT_TEMPLATES / "openai_chatgpt.json")
 
-chatbot_1 = LLMChain(
+chatbot_1 = ConversationChain(
     llm=HuggingFaceHub(
         repo_id="google/flan-t5-xl",
-        model_kwargs={"temperature": 1, "do_sample":True, "top_p":"0.8"}
+        model_kwargs={"temperature": 1}
     ),
     prompt=prompt,
     verbose=False,
     memory=ConversationBufferMemory(ai_prefix="Assistant"),
 )
 
-chatbot_2 = LLMChain(
+chatbot_2 = ConversationChain(
     llm=HuggingFaceHub(
         repo_id="bigscience/bloom",
-        model_kwargs={"temperature": 1, "do_sample":True, "top_p":"0.8"}
+        model_kwargs={"temperature": 0.7}
     ),
     prompt=prompt,
     verbose=False,
     memory=ConversationBufferMemory(ai_prefix="Assistant"),
 )
 
+chatbot_3 = ConversationChain(
+    llm=HuggingFaceHub(
+        repo_id="bigscience/T0_3B",
+        model_kwargs={"temperature": 1}
+    ),
+    prompt=prompt,
+    verbose=False,
+    memory=ConversationBufferMemory(ai_prefix="Assistant"),
+)
+
+chatbot_4 = ConversationChain(
+    llm=HuggingFaceHub(
+        repo_id="EleutherAI/gpt-j-6B",
+        model_kwargs={"temperature": 1}
+    ),
+    prompt=prompt,
+    verbose=False,
+    memory=ConversationBufferMemory(ai_prefix="Assistant"),
+)
 
 demo = gr.Blocks()
 
@@ -94,6 +112,8 @@ with demo:
         "generated_responses": [],
         "response_1": "",
         "response_2": "",
+        "response_3": "",
+        "response_4": "",
         }
     state = gr.JSON(state_dict, visible=False)
 
@@ -104,21 +124,27 @@ with demo:
 
     # Generate model prediction
     def _predict(txt, state):
-        response2model = {}
+        # TODO: parallelize this!
         response_1 = chatbot_1.predict(input=txt)
         response_2 = chatbot_2.predict(input=txt)
+        response_3 = chatbot_3.predict(input=txt)
+        response_4 = chatbot_4.predict(input=txt)
+
+        response2model = {}
         response2model[response_1] = chatbot_1.llm.repo_id
         response2model[response_2] = chatbot_2.llm.repo_id
+        response2model[response_3] = chatbot_3.llm.repo_id
+        response2model[response_4] = chatbot_4.llm.repo_id
 
         state["cnt"] += 1
 
         new_state_md = f"Inputs remaining in HIT: {state['cnt']}/{TOTAL_CNT}"
 
-        state["data"].append({"cnt": state["cnt"], "text": txt, "response_1": response_1,  "response_2": response_2, "response2model": response2model})
+        state["data"].append({"cnt": state["cnt"], "text": txt, "response_1": response_1,  "response_2": response_2, "response_3": response_3, "response_4": response_4,"response2model": response2model})
         state["past_user_inputs"].append(txt)
 
         past_conversation_string = "<br />".join(["<br />".join(["😃: " + user_input, "🤖: " + model_response]) for user_input, model_response in zip(state["past_user_inputs"], state["generated_responses"] + [""])])
-        return gr.update(visible=False), gr.update(visible=True), gr.update(visible=True, choices=[response_1, response_2], interactive=True, value=response_1), gr.update(value=past_conversation_string), state, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), new_state_md, dummy
+        return gr.update(visible=False), gr.update(visible=True), gr.update(visible=True, choices=[response_1, response_2, response_3, response_4], interactive=True, value=response_1), gr.update(value=past_conversation_string), state, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), new_state_md, dummy
 
     def _select_response(selected_response, state, dummy):
         done = state["cnt"] == TOTAL_CNT