Create app.py

app.py

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+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load your text generation model and tokenizer
+pipe = pipeline("text-generation", model="mostafaHaydar/model_test")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Set up the Streamlit app
+st.title("Text Generation with LLaMA 3")
+
+# Text input from the user
+prompt = st.text_area("Enter your prompt:")
+
+# Generate text when the user clicks the button
+if st.button("Generate"):
+    if prompt:
+        # Tokenize and generate text
+        inputs = tokenizer(prompt, return_tensors="pt")
+        output = model.generate(**inputs, max_length=150)  # Adjust max_length as needed
+        generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+
+        # Display the generated text
+        st.subheader("Generated Text:")
+        st.write(generated_text)
+    else:
+        st.warning("Please enter a prompt to generate text.")