Update app.py

app.py

@@ -4,10 +4,10 @@ import pandas as pd
 from transformers import pipeline
 import random
 
-# Load the Hugging Face model for text generation (Bloom or any other open-source model)
+# Load the Hugging Face model for text generation and summarization (FLAN-T5 or T5-Small)
 @st.cache_resource
 def load_text_generator():
-    return pipeline("text-generation", model="bigscience/bloom-560m")  # Smaller version of Bloom for free use
+    return pipeline("text2text-generation", model="google/flan-t5-base")  # Efficient and professional model
 
 text_generator = load_text_generator()
 
@@ -34,9 +34,9 @@ def search_topic_in_content(content, topic):
     topic_sentences = [s for s in sentences if topic.lower() in s.lower()]  # Filter sentences containing the topic
     return ". ".join(topic_sentences) if topic_sentences else None
 
-# Function to generate content using Hugging Face model
-def generate_ai_content(topic):
-    prompt = f"Explain '{topic}' in simple terms for engineering students. Provide examples and applications."
+# Function to generate structured content using Hugging Face model
+def generate_professional_content(topic):
+    prompt = f"Explain '{topic}' in bullet points, highlighting the key concepts, examples, and applications in a professional manner for electrical engineering students."
     response = text_generator(prompt, max_length=300, num_return_sequences=1)
     return response[0]['generated_text']
 
@@ -86,7 +86,7 @@ if st.button("Generate Study Material"):
             st.write(filtered_content)
         else:
             st.warning("No relevant content found in the uploaded material. Generating AI-based content instead.")
-            ai_content = generate_ai_content(topic)
+            ai_content = generate_professional_content(topic)
             st.write("**AI-Generated Content:**")
             st.write(ai_content)
     else: