Update app.py

app.py

@@ -1,16 +1,42 @@
 import streamlit as st
-from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-sentiment_pipeline = pipeline("sentiment-analysis")
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("namannn/llama2-13b-hyperbolic-cluster-pruned")
+model = AutoModelForCausalLM.from_pretrained("namannn/llama2-13b-hyperbolic-cluster-pruned")
 
-st.title("Sentiment Analysis with HuggingFace Spaces")
-st.write("Enter a sentence to analyze its sentiment:")
+# Streamlit UI components
+st.title("Text Generation with LLaMa2-13b Hyperbolic Model")
+st.write("Enter a prompt below and the model will generate text.")
 
-user_input = st.text_input("")
-if user_input:
-    result = sentiment_pipeline(user_input)
-    sentiment = result[0]["label"]
-    confidence = result[0]["score"]
+# User input for prompt
+prompt = st.text_area("Input Prompt", "Once upon a time, in a land far away")
+
+# Slider for controlling the length of the output
+max_length = st.slider("Max Length of Generated Text", min_value=50, max_value=200, value=100)
+
+# Button to trigger text generation
+if st.button("Generate Text"):
+    if prompt:
+        # Encode the prompt text
+        inputs = tokenizer(prompt, return_tensors="pt")
+
+        # Generate text with the model
+        outputs = model.generate(
+            inputs["input_ids"],
+            max_length=max_length,
+            num_return_sequences=1,
+            no_repeat_ngram_size=2,  # You can tune this for diversity
+            do_sample=True,  # Use sampling for diverse generation
+            top_k=50,  # Top-k sampling for diversity
+            top_p=0.95,  # Top-p (nucleus) sampling
+            temperature=0.7  # Control randomness (lower = more deterministic)
+        )
+
+        # Decode and display generated text
+        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        st.subheader("Generated Text:")
+        st.write(generated_text)
+    else:
+        st.warning("Please enter a prompt to generate text.")
 
-    st.write(f"Sentiment: {sentiment}")
-    st.write(f"Confidence: {confidence:.2f}")