Delete app6.py

app6.py

@@ -1,190 +0,0 @@
-import numpy as np
-import streamlit as st
-from openai import OpenAI
-import os
-from dotenv import load_dotenv
-import random
-
-os.environ["BROWSER_GATHERUSAGESTATS"] = "false"
-load_dotenv()
-
-# Initialize the client
-client = OpenAI(
-    base_url="https://api-inference.huggingface.co/v1",
-    api_key=os.environ.get('TOKEN2')  # Add your Huggingface token here  
-)
-
-# Supported models
-model_links = {
-    "Meta-Llama-3-8B": "meta-llama/Meta-Llama-3-8B-Instruct"
-}
-
-# Reset conversation
-def reset_conversation():
-    st.session_state.conversation = []
-    st.session_state.messages = []
-
-# Define the available models
-models = [key for key in model_links.keys()]
-
-# Sidebar for model selection
-selected_model = st.sidebar.selectbox("Select Model", models)
-temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, 0.5)
-st.sidebar.button('Reset Chat', on_click=reset_conversation)
-st.sidebar.write(f"You're now chatting with **{selected_model}**")
-st.sidebar.markdown("*Generated content may be inaccurate or false.*")
-
-# Chat initialization
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
-
-# Main logic to choose between data generation and data labeling
-task_choice = st.selectbox("Choose Task", ["Data Generation", "Data Labeling"])
-
-if task_choice == "Data Generation":
-    classification_type = st.selectbox(
-        "Choose Classification Type",
-        ["Sentiment Analysis", "Binary Classification", "Multi-Class Classification"]
-    )
-
-    if classification_type == "Sentiment Analysis":
-        st.write("Sentiment Analysis: Positive, Negative, Neutral")
-        labels = ["Positive", "Negative", "Neutral"]
-    elif classification_type == "Binary Classification":
-        label_1 = st.text_input("Enter first class")
-        label_2 = st.text_input("Enter second class")
-        labels = [label_1, label_2]
-    elif classification_type == "Multi-Class Classification":
-        num_classes = st.slider("How many classes?", 3, 10, 3)
-        labels = [st.text_input(f"Class {i+1}") for i in range(num_classes)]
-
-    domain = st.selectbox("Choose Domain", ["Restaurant reviews", "E-commerce reviews", "Custom"])
-    if domain == "Custom":
-        domain = st.text_input("Specify custom domain")
-
-    min_words = st.number_input("Minimum words per example", min_value=10, max_value=90, value=10)
-    max_words = st.number_input("Maximum words per example", min_value=10, max_value=90, value=90)
-
-    few_shot = st.radio("Do you want to use few-shot examples?", ["Yes", "No"])
-    if few_shot == "Yes":
-        num_examples = st.slider("How many few-shot examples?", 1, 5, 1)
-        few_shot_examples = [
-            {"content": st.text_area(f"Example {i+1}"), "label": st.selectbox(f"Label for example {i+1}", labels)}
-            for i in range(num_examples)
-        ]
-    else:
-        few_shot_examples = []
-
-    num_to_generate = st.number_input("How many examples to generate?", min_value=1, max_value=100, value=10)
-    user_prompt = st.text_area("Enter your prompt to guide example generation", "")
-
-    # System prompt generation
-    system_prompt = f"You are a professional {classification_type.lower()} expert. Your role is to generate data for {domain}.\n\n"
-    if few_shot_examples:
-        system_prompt += "Use the following few-shot examples as a reference:\n"
-        for example in few_shot_examples:
-            system_prompt += f"Example: {example['content']} \n Label: {example['label']}\n"
-    system_prompt += f"Generate {num_to_generate} unique examples with diverse phrasing.\n"
-    system_prompt += f"Each example should have between {min_words} and {max_words} words.\n"
-    system_prompt += f"Use the labels specified: {', '.join(labels)}.\n"
-    if user_prompt:
-        system_prompt += f"Additional instructions: {user_prompt}\n"
-
-    st.write("System Prompt:")
-    st.code(system_prompt)
-
-    if st.button("Generate Examples"):
-        with st.spinner("Generating..."):
-            st.session_state.messages.append({"role": "system", "content": system_prompt})
-
-            try:
-                stream = client.chat.completions.create(
-                    model=model_links[selected_model],
-                    messages=[
-                        {"role": m["role"], "content": m["content"]}
-                        for m in st.session_state.messages
-                    ],
-                    temperature=temp_values,
-                    stream=True,
-                    max_tokens=3000,
-                )
-                response = st.write_stream(stream)
-            except Exception as e:
-                st.error("An error occurred during generation.")
-                st.error(f"Error details: {str(e)}")
-
-            st.session_state.messages.append({"role": "assistant", "content": response})
-
-else:  # Data Labeling Process
-    labeling_classification_type = st.selectbox(
-        "Choose Classification Type for Labeling",
-        ["Sentiment Analysis", "Binary Classification", "Multi-Class Classification"]
-    )
-
-    if labeling_classification_type == "Sentiment Analysis":
-        st.write("Sentiment Analysis: Positive, Negative, Neutral")
-        labeling_labels = ["Positive", "Negative", "Neutral"]
-    elif labeling_classification_type == "Binary Classification":
-        labeling_label_1 = st.text_input("Enter first class for labeling")
-        labeling_label_2 = st.text_input("Enter second class for labeling")
-        labeling_labels = [labeling_label_1, labeling_label_2]
-    elif labeling_classification_type == "Multi-Class Classification":
-        labeling_num_classes = st.slider("How many classes for labeling?", 3, 10, 3)
-        labeling_labels = [st.text_input(f"Labeling Class {i+1}") for i in range(labeling_num_classes)]
-
-    labeling_few_shot = st.radio("Do you want to add few-shot examples for labeling?", ["Yes", "No"])
-    if labeling_few_shot == "Yes":
-        labeling_num_examples = st.slider("How many few-shot examples for labeling?", 1, 5, 1)
-        labeling_few_shot_examples = [
-            {"content": st.text_area(f"Labeling Example {i+1}"), 
-             "label": st.selectbox(f"Label for labeling example {i+1}", labeling_labels)}
-            for i in range(labeling_num_examples)
-        ]
-    else:
-        labeling_few_shot_examples = []
-
-    text_to_classify = st.text_area("Enter text to classify")
-
-    if st.button("Classify Text"):
-        if text_to_classify:
-            labeling_system_prompt = (
-                f"You are a professional {labeling_classification_type.lower()} expert. "
-                f"Classify the following text using these labels: {', '.join(labeling_labels)}.\n\n"
-            )
-            if labeling_few_shot_examples:
-                labeling_system_prompt += "Here are some example classifications:\n"
-                for example in labeling_few_shot_examples:
-                    labeling_system_prompt += f"Review: {example['content']}\nLabel: {example['label']}\n\n"
-
-            labeling_system_prompt += (
-                f"Text to classify: {text_to_classify}\n"
-                f"Provide only the classification result in this format:\n"
-                f"'Review: [text provided] Label: [appropriate label]'\n"
-            )
-
-            with st.spinner("Classifying..."):
-                st.session_state.messages.append({"role": "system", "content": labeling_system_prompt})
-
-                try:
-                    stream = client.chat.completions.create(
-                        model=model_links[selected_model],
-                        messages=[
-                            {"role": m["role"], "content": m["content"]}
-                            for m in st.session_state.messages
-                        ],
-                        temperature=temp_values,
-                        stream=True,
-                        max_tokens=1000,
-                    )
-                    response = st.write_stream(stream)
-                except Exception as e:
-                    st.error("An error occurred during classification.")
-                    st.error(f"Error details: {str(e)}")
-
-                st.session_state.messages.append({"role": "assistant", "content": response})
-        else:
-            st.warning("Please enter text to classify.")