Delete app10.py

app10.py

@@ -1,208 +0,0 @@
-## update of aap7.py
-
-import os
-import streamlit as st
-from openai import OpenAI
-from dotenv import load_dotenv
-from langchain_core.prompts import PromptTemplate
-
-# Load environment variables
-load_dotenv()
-##openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Initialize the client
-client = OpenAI(
-    base_url="https://api-inference.huggingface.co/v1",
-    api_key=os.environ.get('TOKEN2')  # Add your Huggingface token here
-)
-
-
-# Initialize the OpenAI client
-##client = OpenAI(
-    ##base_url="https://api-inference.huggingface.co/v1",
-    ##api_key=openai_api_key
-##)
-
-# Define reset function for the conversation
-def reset_conversation():
-    st.session_state.conversation = []
-    st.session_state.messages = []
-
-# Streamlit interface setup
-st.title("🤖 Text Data Generation & Labeling App")
-st.sidebar.title("Settings")
-
-# Sidebar settings
-selected_model = st.sidebar.selectbox("Select Model", ["meta-llama/Meta-Llama-3-8B-Instruct"])
-temperature = st.sidebar.slider("Temperature", 0.0, 1.0, 0.5)
-st.sidebar.button("Reset Conversation", on_click=reset_conversation)
-st.sidebar.write(f"You're now chatting with **{selected_model}**")
-st.sidebar.markdown("*Note: Generated content may be inaccurate or false.*")
-
-# Initialize conversation state
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-# Display conversation
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
-
-# Main logic: 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":
-        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]
-    else:  # 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)
-
-    use_few_shot = st.radio("Use few-shot examples?", ["Yes", "No"])
-    few_shot_examples = []
-    if use_few_shot == "Yes":
-        num_examples = st.slider("Number of few-shot examples", 1, 5, 1)
-        for i in range(num_examples):
-            content = st.text_area(f"Example {i+1} Content")
-            label = st.selectbox(f"Example {i+1} Label", labels)
-            few_shot_examples.append({"content": content, "label": label})
-
-    num_to_generate = st.number_input("Number of examples to generate", 1, 100, 10)
-    user_prompt = st.text_area("Enter additional instructions", "")
-
-    # Construct the LangChain prompt
-    prompt_template = PromptTemplate(
-        input_variables=["classification_type", "domain", "num_examples", "min_words", "max_words", "labels", "user_prompt"],
-        template=(
-            "You are a professional {classification_type} expert tasked with generating examples for {domain}.\n"
-            "Use the following parameters:\n"
-            "- Number of examples: {num_examples}\n"
-            "- Word range: {min_words}-{max_words}\n"
-            "- Labels: {labels}\n"
-            "{user_prompt}"
-        )
-    )
-    system_prompt = prompt_template.format(
-        classification_type=classification_type,
-        domain=domain,
-        num_examples=num_to_generate,
-        min_words=min_words,
-        max_words=max_words,
-        labels=", ".join(labels),
-        user_prompt=user_prompt
-    )
-
-    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=selected_model,
-                    messages=[{"role": "system", "content": system_prompt}],
-                    temperature=temperature,
-                    stream=True,
-                    max_tokens=3000,
-                )
-                response = st.write_stream(stream)
-                st.session_state.messages.append({"role": "assistant", "content": response})
-            except Exception as e:
-                st.error("An error occurred during generation.")
-                st.error(f"Details: {e}")
-
-    
-elif task_choice == "Data Labeling":
-    # Labeling logic
-    labeling_type = st.selectbox(
-        "Classification Type for Labeling",
-        ["Sentiment Analysis", "Binary Classification", "Multi-Class Classification"]
-    )
-
-    if labeling_type == "Sentiment Analysis":
-        labels = ["Positive", "Negative", "Neutral"]
-    elif labeling_type == "Binary Classification":
-        label_1 = st.text_input("First label for classification")
-        label_2 = st.text_input("Second label for classification")
-        labels = [label_1, label_2]
-    else:  # Multi-Class Classification
-        num_classes = st.slider("Number of labels", 3, 10, 3)
-        labels = [st.text_input(f"Label {i+1}") for i in range(num_classes)]
-
-    use_few_shot_labeling = st.radio("Add few-shot examples for labeling?", ["Yes", "No"])
-    few_shot_labeling_examples = []
-    if use_few_shot_labeling == "Yes":
-        num_labeling_examples = st.slider("Number of few-shot labeling examples", 1, 5, 1)
-        for i in range(num_labeling_examples):
-            content = st.text_area(f"Labeling Example {i+1} Content")
-            label = st.selectbox(f"Label for Example {i+1}", labels)
-            few_shot_labeling_examples.append({"content": content, "label": label})
-
-    text_to_classify = st.text_area("Enter text to classify")
-
-    if st.button("Classify Text"):
-        if text_to_classify:
-            # Construct the labeling prompt
-            labeling_prompt_template = PromptTemplate(
-                input_variables=["labeling_type", "labels", "few_shot_examples", "text_to_classify"],
-                template=(
-                    "You are an expert in {labeling_type} classification. "
-                    "Classify the following text using: {labels}.\n\n"
-                    "DO NO write additional information or commentary"
-                    "use user {few_shot_examples} as guidance in labeling process\n"
-                    "Write calassifaication as {text_to_classify}. Label: [Label] \n"
-                    "Classify this: {text_to_classify}"
-                )
-            )
-
-            # Prepare few-shot examples for the prompt
-            few_shot_examples_text = ""
-            if few_shot_labeling_examples:
-                few_shot_examples_text += "Example classifications:\n"
-                for ex in few_shot_labeling_examples:
-                    few_shot_examples_text += f"Text: {ex['content']} - Label: {ex['label']}\n"
-
-            # Format the prompt with the user's input
-            labeling_prompt = labeling_prompt_template.format(
-                labeling_type=labeling_type.lower(),
-                labels=", ".join(labels),
-                few_shot_examples=few_shot_examples_text.strip(),
-                text_to_classify=text_to_classify
-            )
-
-            with st.spinner("Classifying..."):
-                st.session_state.messages.append({"role": "system", "content": labeling_prompt})
-                try:
-                    stream = client.chat.completions.create(
-                        model=selected_model,
-                        messages=[{"role": "system", "content": labeling_prompt}],
-                        temperature=temperature,
-                        stream=True,
-                        max_tokens=3000,
-                    )
-                    labeling_response = st.write_stream(stream)
-                    # Format response to match desired output
-                    formatted_response = f"Label: {labeling_response}"
-                    st.write(formatted_response)
-                except Exception as e:
-                    st.error("An error occurred during classification.")
-                    st.error(f"Details: {e}")
-        else:
-            st.warning("Please enter text to classify.")