Update app.py

app.py

@@ -1,42 +1,30 @@
 import streamlit as st
-import transformers
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 
-# Load the Phi 2 model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained(
-    "microsoft/phi-2",
-    trust_remote_code=True
-)
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype="auto", device_map="auto", trust_remote_code=True)
 
-model = AutoModelForCausalLM.from_pretrained(
-    # "kroonen/phi-2-GGUF",
-    "microsoft/phi-2",
-    device_map="auto",
-    trust_remote_code=True,
-    torch_dtype=torch.float32
-)
+# Streamlit app
+st.title("Text Generation with Transformers")
 
-# Streamlit UI
-st.title("Microsoft Phi 2 Streamlit App")
+# Input prompt
+prompt = st.text_input("Enter your prompt:")
 
-# User input prompt
-prompt = st.text_area("Enter your prompt:", """Write a short summary about how to create a healthy lifestyle.""")
-
-# Generate output based on user input
-if st.button("Generate Output"):
+# Generate button
+if st.button("Generate"):
     with torch.no_grad():
-        token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt",  
-                                     return_attention_mask=False
-                                    )
+        # Tokenize and generate output
+        token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
         output_ids = model.generate(
             token_ids.to(model.device),
-            # max_new_tokens=512,
+            max_new_tokens=512,
             do_sample=True,
-            temperature=0.3,
-            max_length=200
+            temperature=0.1
         )
-
-    output = tokenizer.decode(output_ids[0][token_ids.size(1):])
-    st.text("Generated Output:")
-    st.write(output)
+    
+    # Decode and display the generated text
+    generated_text = tokenizer.decode(output_ids[0][token_ids.size(1):])
+    st.text("Generated Text:")
+    st.text(generated_text)