Upload 2 files

app.py

@@ -0,0 +1,94 @@
+import streamlit as st
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import os
+from huggingface_hub import login
+
+# Hugging Face Authentication
+hf_token = os.getenv("HUGGINGFACE_TOKEN", "").strip()
+
+if not hf_token:
+    st.error("HUGGINGFACE_TOKEN not found. Please set your Hugging Face token.")
+    st.stop()
+
+login(token=hf_token)
+
+# Load Model & Tokenizer
+model_name = "meta-llama/Llama-2-7b-chat-hf"  # Use the chat model
+
+@st.cache_resource
+def load_model():
+    # Load tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token)
+
+    # Load model with FP16 (half-precision) on CPU
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        device_map="cpu",  # Force CPU usage
+        torch_dtype=torch.float16,  # Use FP16 to reduce memory usage
+        token=hf_token
+    )
+
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+# Function to classify text using a prompt-based approach
+def classify_text(text, classes):
+    # Create a prompt for classification
+    prompt = f"""
+    Classify the following text into one of these categories: {", ".join(classes)}.
+    Text: {text}
+    Category:
+    """
+    
+    # Tokenize the prompt
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    
+    # Generate the output
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_length=100, num_return_sequences=1)
+    
+    # Decode the output
+    decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Extract the predicted class
+    predicted_class = decoded_output.split("Category:")[-1].strip()
+    return predicted_class
+
+# Custom CSS to make all text red
+st.markdown(
+    """
+    <style>
+    /* Target all text elements */
+    body, h1, h2, h3, h4, h5, h6, p, div, span, input, textarea, button, label {
+        color: #E25822 !important;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Streamlit UI
+st.title("📝 Text Classification with LLaMA 2 Chat (FP16)")
+st.write("Powered by LLaMA 2 Chat & Hugging Face")
+
+# User Input
+user_input = st.text_area("Enter the text to classify:")
+
+# Define classes for classification
+classes = ["Positive", "Negative", "Neutral"]
+
+if st.button("Classify"):
+    if user_input:
+        # Perform classification
+        predicted_class = classify_text(user_input, classes)
+
+        # Display result
+        st.subheader("Predicted Class:")
+        st.write(predicted_class)
+    else:
+        st.warning("Please enter some text to classify.")
+
+st.markdown("---")
+st.write("🔍 This app classifies text using the LLaMA 2 Chat model with FP16.")

requirements.txt

@@ -0,0 +1,7 @@
+streamlit
+torch
+transformers
+huggingface-hub
+sentencepiece
+accelerate>=0.26.0
+bitsandbytes