Create app10.py

app10.py

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+## 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:
+            labeling_prompt = (
+                f"You are an expert in {labeling_type.lower()} classification. Classify this text using: {', '.join(labels)}.\n\n"
+            )
+            if few_shot_labeling_examples:
+                labeling_prompt += "Example classifications:\n"
+                for ex in few_shot_labeling_examples:
+                    labeling_prompt += f"Text: {ex['content']} - Label: {ex['label']}\n"
+            labeling_prompt += f"\nClassify this: {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)
+                    st.write("Label:", labeling_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.")