Update app.py

app.py

@@ -8,28 +8,65 @@
 # sentiment = classifier("I've been waiting for HuggingFace course my whole life.")
 
 
+# import streamlit as st
+# from transformers import pipeline
+
+# # Initialize the sentiment-analysis pipeline
+# classifier = pipeline("sentiment-analysis")
+
+# # Streamlit app layout
+# st.title("Sentiment Analysis with Hugging Face")
+# st.write("Enter a sentence to analyze its sentiment:")
+
+# # Text input for the user
+# user_input = st.text_input("Sentence", "")
+
+# # Perform sentiment analysis when the user provides input
+# if user_input:
+#     sentiment = classifier(user_input)
+#     label = sentiment[0]['label']
+#     score = sentiment[0]['score']
+
+#     # Display the result
+#     st.write(f"**Sentiment:** {label}")
+#     st.write(f"**Confidence Score:** {score:.4f}")
+
+
+
+
 import streamlit as st
-from transformers import pipeline
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+import torch
+
+# Load the tokenizer and model
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2-large")
+model = GPT2LMHeadModel.from_pretrained("gpt2-large")
+
+def generate_blog(title):
+    # Encode the input text
+    inputs = tokenizer.encode(title, return_tensors='pt')
+    
+    # Generate the output
+    outputs = model.generate(inputs, max_length=500, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
+
+    # Decode the output text
+    blog_post = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return blog_post
 
-# Initialize the sentiment-analysis pipeline
-classifier = pipeline("sentiment-analysis")
+# Streamlit app
+st.title("Blog Post Generator")
+title = st.text_input("Enter the blog title")
 
-# Streamlit app layout
-st.title("Sentiment Analysis with Hugging Face")
-st.write("Enter a sentence to analyze its sentiment:")
+if st.button("Generate Blog"):
+    if title:
+        blog_post = generate_blog(title)
+        st.subheader("Generated Blog Post")
+        st.write(blog_post)
+    else:
+        st.warning("Please enter a blog title.")
 
-# Text input for the user
-user_input = st.text_input("Sentence", "")
 
-# Perform sentiment analysis when the user provides input
-if user_input:
-    sentiment = classifier(user_input)
-    label = sentiment[0]['label']
-    score = sentiment[0]['score']
 
-    # Display the result
-    st.write(f"**Sentiment:** {label}")
-    st.write(f"**Confidence Score:** {score:.4f}")
 
 # Optional: Add a slider example (unrelated to sentiment analysis)
 # x = st.slider('Select a value')