Create app4.py

app4.py

@@ -0,0 +1,173 @@
+### اول كود للابيلنق اشتغل بس مافرق بين ريكوند و نت ريكومند
+import numpy as np
+import streamlit as st
+from openai import OpenAI
+import os
+from dotenv import load_dotenv
+import random
+
+# Load environment variables
+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('GP2')  # Replace with your Huggingface token
+)
+
+# Initialize session state variables if they are not already defined
+if "labels" not in st.session_state:
+    st.session_state.labels = []
+if "few_shot_examples" not in st.session_state:
+    st.session_state.few_shot_examples = []
+if "examples_to_classify" not in st.session_state:
+    st.session_state.examples_to_classify = []
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Sidebar for model selection and temperature setting
+selected_model = st.sidebar.selectbox("Select Model", ["Meta-Llama-3-8B"], key="model_select")
+temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, 0.5, key="temp_slider")
+
+# Reset conversation button
+st.sidebar.button('Reset Chat', on_click=lambda: (st.session_state.update(conversation=[], messages=[])), key="reset_button")
+
+# Main task selection: Data Generation or Data Labeling
+task_choice = st.selectbox("Choose Task", ["Data Generation", "Data Labeling"], key="task_choice_select")
+
+# Data Generation Section
+if task_choice == "Data Generation":
+    classification_type = st.selectbox(
+        "Choose Classification Type",
+        ["Sentiment Analysis", "Binary Classification", "Multi-Class Classification"],
+        key="classification_type_select"
+    )
+
+    # Define labels based on classification type
+    if classification_type == "Sentiment Analysis":
+        st.session_state.labels = ["Positive", "Negative", "Neutral"]
+        st.write("Sentiment Analysis: Positive, Negative, Neutral")
+    elif classification_type == "Binary Classification":
+        label_1 = st.text_input("Enter first class", key="binary_class_1")
+        label_2 = st.text_input("Enter second class", key="binary_class_2")
+        st.session_state.labels = [label_1, label_2]
+    elif classification_type == "Multi-Class Classification":
+        num_classes = st.slider("How many classes?", 3, 10, 3, key="num_classes_slider")
+        st.session_state.labels = [st.text_input(f"Class {i+1}", key=f"class_input_{i+1}") for i in range(num_classes)]
+
+    # Domain selection
+    domain = st.selectbox("Choose Domain", ["Restaurant reviews", "E-commerce reviews", "Custom"], key="domain_select")
+    if domain == "Custom":
+        domain = st.text_input("Specify custom domain", key="custom_domain_input")
+
+    # Word count selection
+    min_words = st.number_input("Minimum words per example", min_value=10, max_value=90, value=10, key="min_words_input")
+    max_words = st.number_input("Maximum words per example", min_value=10, max_value=90, value=90, key="max_words_input")
+
+    # Few-shot examples option
+    few_shot = st.radio("Do you want to use few-shot examples?", ["Yes", "No"], key="few_shot_radio")
+    if few_shot == "Yes":
+        num_examples = st.slider("How many few-shot examples?", 1, 5, 1, key="num_examples_slider")
+        st.session_state.few_shot_examples = [
+            {
+                "content": st.text_area(f"Example {i+1} Text", key=f"example_text_{i+1}"),
+                "label": st.selectbox(f"Label for Example {i+1}", st.session_state.labels, key=f"label_select_{i+1}")
+            }
+            for i in range(num_examples)
+        ]
+    else:
+        st.session_state.few_shot_examples = []
+
+    # Number of examples to generate
+    num_to_generate = st.number_input("How many examples to generate?", min_value=1, max_value=100, value=10, key="num_to_generate_input")
+
+    # User prompt text field
+    user_prompt = st.text_area("Enter your prompt to guide example generation", "", key="user_prompt_text_area")
+
+    # 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 st.session_state.few_shot_examples:
+        system_prompt += "Use the following few-shot examples as a reference:\n"
+        for example in st.session_state.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(st.session_state.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", key="generate_examples_button"):
+        # Generate examples by concatenating all inputs and sending it to the model
+        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": m["role"], "content": m["content"]}
+                        for m in st.session_state.messages
+                    ],
+                    temperature=temp_values,
+                    stream=True,
+                    max_tokens=3000,
+                )
+                response = ""
+                for chunk in stream:
+                    response += chunk['choices'][0]['delta'].get('content', '')
+                st.write(response)
+            except Exception as e:
+                st.error(f"Error during generation: {e}")
+
+            st.session_state.messages.append({"role": "assistant", "content": response})
+
+# Data Labeling Section
+else:
+    # Classification Type and Labels Setup
+    classification_type = st.selectbox("Choose Classification Type", ["Sentiment Analysis", "Binary Classification", "Multi-Class Classification"], key="classification_type_labeling")
+    
+    if classification_type == "Sentiment Analysis":
+        st.session_state.labels = ["Positive", "Negative", "Neutral"]
+        st.write("Sentiment Analysis labels: Positive, Negative, Neutral")
+    elif classification_type == "Binary Classification":
+        label_1 = st.text_input("Enter first class", key="binary_class_1_labeling")
+        label_2 = st.text_input("Enter second class", key="binary_class_2_labeling")
+        st.session_state.labels = [label_1, label_2]
+    elif classification_type == "Multi-Class Classification":
+        num_classes = st.slider("How many classes?", 3, 10, 3, key="num_classes_labeling")
+        st.session_state.labels = [st.text_input(f"Class {i+1}", key=f"class_input_labeling_{i+1}") for i in range(num_classes)]
+
+    # Few-shot examples for labeling
+    use_few_shot = st.radio("Do you want to use few-shot examples?", ["Yes", "No"], key="use_few_shot_labeling")
+    if use_few_shot == "Yes":
+        num_examples = st.slider("How many few-shot examples?", 1, 5, 1, key="few_shot_num_labeling")
+        st.session_state.few_shot_examples = [
+            {
+                "content": st.text_area(f"Example {i+1} Text", key=f"example_text_labeling_{i+1}"),
+                "label": st.selectbox(f"Label for Example {i+1}", st.session_state.labels, key=f"label_select_labeling_{i+1}")
+            }
+            for i in range(num_examples)
+        ]
+    else:
+        st.session_state.few_shot_examples = []
+
+    # Input Examples for Classification
+    num_to_classify = st.number_input("How many examples do you want to classify?", min_value=1, max_value=100, value=5, key="num_to_classify_input")
+    st.session_state.examples_to_classify = [st.text_area(f"Example {i+1} Text", key=f"example_classify_text_{i+1}") for i in range(num_to_classify)]
+
+    # Placeholder for classification function (can be replaced with actual API call)
+    def classify_examples(examples, labels):
+        classified_results = [{"example": ex, "label": random.choice(labels)} for ex in examples]
+        return classified_results
+
+    # Classification results display
+    if st.button("Classify Examples", key="classify_button"):
+        results = classify_examples(st.session_state.examples_to_classify, st.session_state.labels)
+        st.write("Classification Results:")
+        for result in results:
+            st.write(f"Example: {result['example']}\nLabel: {result['label']}\n")
+شحح