Update app.py

app.py

@@ -1,36 +1,37 @@
+from transformers import AutoTokenizer
+import transformers
 import torch
 import streamlit as st
-from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 
-model_name = "gpt2"
+model_name = "tiiuae/falcon-7b-instruct"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
 
-generator = pipeline(
+generator = transformers.pipeline(
     "text-generation",
-    model=model,
+    model=model_name,
     tokenizer=tokenizer,
-    device=0 if torch.cuda.is_available() else -1
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto"
 )
 
-# Function for text generation with filtering of repeated sequences
-def generate_text(prompt, section, max_length=200, temperature=0.7, top_k=50, repetition_penalty=1.2):
-    return generator(
+def generate_text(prompt, section, max_length=200, top_k=50, temperature=0.7):
+    sequences = generator(
         f"{section} - {prompt}",
         max_length=max_length,
         do_sample=True,
         top_k=top_k,
         temperature=temperature,
-        repetition_penalty=repetition_penalty,
         num_return_sequences=1,
         eos_token_id=tokenizer.eos_token_id,
-    )[0]["generated_text"]
+    )
+    return sequences[0]["generated_text"]
 
 # Streamlit app
 st.title("AI-Generated Blog Post")
 
 # Keyword selection input
-keywords_input = st.text_input("Step 1: Keyword Selection (Separate keywords with commas)","Artificial Intelligence")
+keywords_input = st.text_input("Step 1: Keyword Selection (Separate keywords with commas)", "Artificial Intelligence")
 keywords = [word.strip() for word in keywords_input.split(',')]
 
 # Display generated content on button click
@@ -38,11 +39,11 @@ if st.button('Generate Article'):
     if keywords_input:
 
         generated_text = " ".join(keywords)
-        intro_text = generate_text(generated_text, "Introduction", max_length=200, temperature=0.7, top_k=50)
-        body_text = generate_text(generated_text, "Body", max_length=500, temperature=0.7, top_k=50)
-        conclusion_text = generate_text(generated_text, "Conclusion", max_length=150, temperature=0.7, top_k=50)
+        intro_text = generate_text(generated_text, "Introduction", max_length=200, top_k=50, temperature=0.7)
+        body_text = generate_text(generated_text, "Body", max_length=500, top_k=50, temperature=0.7)
+        conclusion_text = generate_text(generated_text, "Conclusion", max_length=150, top_k=50, temperature=0.7)
 
-        # Displaying the sections with adjusted parameters
+        # Displaying the sections
         st.header("Introduction")
         st.write(intro_text)