add local model playground

wt2_playground_local.py

@@ -0,0 +1,81 @@
+import openai
+import os
+from dotenv import load_dotenv
+from llama_cpp import Llama, ChatCompletionMessage
+
+load_dotenv()
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+llm = Llama(model_path="models/gpt4all-converted.bin", n_ctx=500)
+def generate_summarizer(
+        max_tokens,
+        temperature,
+        top_p,
+        frequency_penalty,
+        question_type,
+        question,
+        answer
+):
+    messages = [
+        ChatCompletionMessage(role="system", content="You are a IELTS examiner."),
+        ChatCompletionMessage(role="system",
+                              content=f"The question you have to grade is of type {question_type} and is the following: {question}"),
+
+        ChatCompletionMessage(role="system", content="Please provide a JSON object response with the overall grade and breakdown grades, "
+                           "formatted as follows: {'overall': 7.0, 'task_response': {'Task Achievement': 8.0, "
+                           "'Coherence and Cohesion': 6.5, 'Lexical Resource': 7.5, 'Grammatical Range and Accuracy': "
+                           "6.0}}"),
+        ChatCompletionMessage(role="system",
+                              content="Don't give explanations for the grades, just provide the json with the grades."),
+        ChatCompletionMessage(role="user",
+                              content=f"Evaluate this answer according to ielts grading system: {answer}")
+    ]
+    output = llm.create_chat_completion(messages, max_tokens=50)
+    print(output)
+    return output
+
+
+import streamlit as st
+
+# Set the application title
+st.title("GPT-3.5 IELTS Examiner")
+
+# qt_col, q_col = st.columns(2)
+
+# Selection box to select the question type
+# with qt_col:
+question_type = st.selectbox(
+    "What is the question type?",
+    (
+        "Writing Task 2"
+    ),
+)
+
+# Provide the input area for question to be answered
+# with q_col:
+question = st.text_area("Enter the question:", height=100)
+
+# Provide the input area for text to be summarized
+answer = st.text_area("Enter the answer:", height=100)
+
+# Initiate two columns for section to be side-by-side
+# col1, col2 = st.columns(2)
+
+# Slider to control the model hyperparameter
+# with col1:
+token = st.slider("Token", min_value=0.0, max_value=2000.0, value=1000.0, step=1.0)
+temp = st.slider("Temperature", min_value=0.0, max_value=1.0, value=0.7, step=0.01)
+top_p = st.slider("Top_p", min_value=0.0, max_value=1.0, value=0.9, step=0.01)
+f_pen = st.slider("Frequency Penalty", min_value=-1.0, max_value=1.0, value=0.5, step=0.01)
+
+# Showing the current parameter used for the model
+# with col2:
+with st.expander("Current Parameter"):
+    st.write("Current Token :", token)
+    st.write("Current Temperature :", temp)
+    st.write("Current Nucleus Sampling :", top_p)
+    st.write("Current Frequency Penalty :", f_pen)
+
+# Creating button for execute the text summarization
+if st.button("Grade"):
+    st.write(generate_summarizer(token, temp, top_p, f_pen, question_type, question, answer))