Update app.py

app.py

@@ -1,27 +1,45 @@
 import streamlit as st
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 # Load model and tokenizer
-model_name = "gpt2"  # You can also try other models like "gpt2-medium", "gpt2-large", etc.
+model_name = "google/flan-t5-large"  # You can use "google/flan-t5-xl" for better results if you have more computational resources
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 
-def generate_blog_post(topic):
-    prompt = f"Write a detailed blog post about {topic}."
-    inputs = tokenizer.encode(prompt, return_tensors="pt")
-    outputs = model.generate(inputs, max_length=512, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
-    blog_post = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return blog_post
+def generate_blog_post(topic, max_length=1000):
+    prompt = f"Write a detailed blog post about {topic}. The blog post should be informative, engaging, and well-structured."
+    inputs = tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True)
+    
+    outputs = model.generate(
+        inputs.input_ids,
+        max_length=max_length,
+        num_return_sequences=1,
+        do_sample=True,
+        top_k=50,
+        top_p=0.95,
+        temperature=0.7,
+    )
+    
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_text
 
 # Streamlit interface
 st.title("Blog Post Generator")
-st.write("Enter a topic to generate a detailed blog post.")
 
-topic = st.text_input("Topic", "")
-if st.button("Generate Blog Post"):
-    if topic:
-        with st.spinner('Generating blog post...'):
-            blog_post = generate_blog_post(topic)
+topic = st.text_input("Enter a topic for your blog post:")
+max_length = st.slider("Maximum length of the blog post", min_value=100, max_value=1000, value=500, step=50)
+generate_button = st.button("Generate Blog Post")
+
+if generate_button and topic:
+    with st.spinner("Generating blog post... This may take a moment."):
+        blog_post = generate_blog_post(topic, max_length)
+        
+        # Display the generated blog post
+        st.subheader("Generated Blog Post")
         st.write(blog_post)
-    else:
-        st.write("Please enter a topic to generate a blog post.")
+
+st.sidebar.title("About")
+st.sidebar.info(
+    "This app generates a blog post on a given topic using a large language model. "
+    "Enter a topic and click 'Generate Blog Post' to create your content."
+)