Update app.py

app.py

@@ -1,12 +1,11 @@
 import streamlit as st
-from langchain.prompts import PromptTemplate
 from transformers import pipeline
 
 # Initialize the Hugging Face pipeline
 pipe = pipeline("text2text-generation", model="google/flan-t5-small")
 
 # Function to get response from the model
-def get_response(input_text, keywords, blog_style):
+def get_response(input_text, keywords, blog_style, max_new_tokens=50):
     # Prompt Template
     template = """
     Generate technical project ideas for {blog_style} job profile for a topic {input_text} using these keywords: {keywords}.
@@ -15,7 +14,7 @@ def get_response(input_text, keywords, blog_style):
     prompt = template.format(blog_style=blog_style, input_text=input_text, keywords=keywords)
     
     # Generate the response from the model
-    response = pipe(prompt)
+    response = pipe(prompt, max_new_tokens=max_new_tokens)
     return response[0]['generated_text']  # Extract the generated text
 
 # Streamlit configuration
@@ -37,9 +36,12 @@ with col2:
     blog_style = st.selectbox('Generating project idea for',
                               ('Researchers', 'Data Scientist', 'Software Developer', 'Common People'), index=0)
     
+# Adding an additional field for max_new_tokens
+max_new_tokens = st.slider('Max New Tokens', min_value=10, max_value=100, value=50, step=10)
+
 submit = st.button("Generate")
 
 # Final response
 if submit:
-    response = get_response(input_text, keywords, blog_style)
+    response = get_response(input_text, keywords, blog_style, max_new_tokens)
     st.write(response)