testing summary text

app.py

@@ -5,6 +5,8 @@ from pydantic_ai import Agent
 from pydantic_ai.models.groq import GroqModel
 import nest_asyncio
 import pdfplumber
+from transformers import pipeline
+import torch
 import os
 import presentation as customClass
 
@@ -15,9 +17,14 @@ last_message = ''
 
 
 
-
+# to generate ppt
 model = GroqModel('llama-3.1-70b-versatile', api_key = api_key)
 
+
+# to summarize
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+
 def split_long_string(long_string, chunk_size=3500):
     string_data =  "".join(long_string)
     words = string_data.split()
@@ -43,10 +50,7 @@ async def ppt_content(data):
         "1. Title : title of the slide ",
         "2. Text: he detailed description or narrative content of the slide. This should include key information, explanations, or supporting arguments. Keep it concise yet informative to avoid overwhelming the audience.",
         "3. Bullet point: A list of bullet points summarizing key information on the slide. Each bullet point should be detail, long, and highlight a specific aspect of the slide's topic. ideally, limit to 3-5 points.",
-        "4. Image Suggestion: A prompt for generating an image to complement the slide content. Describe the desired visual in detail, including elements, style, and relevance to the topic. Ensure the prompt is actionable for AI tools.",
-        
-        
-        
+        "4. Image Suggestion: A prompt for generating an image to complement the slide content. Describe the desired visual in detail, including elements, style, and relevance to the topic. Ensure the prompt is actionable for AI tools.", 
     ))
     listOfString = split_long_string(data)
     for i, chunk in enumerate(listOfString):
@@ -58,13 +62,16 @@ async def ppt_content(data):
     
     
     
-    result_1 = agent.run_sync(user_prompt = listOfString[0])
-    result_2 = agent.run_sync(user_prompt = listOfString[1],message_history=result_1.new_messages())
+    result_1 = agent.run_sync(user_prompt = data)
+    #result_2 = agent.run_sync(user_prompt = listOfString[1],message_history=result_1.new_messages())
     
-    print(result_2.data)
+    print(result_1.data)
     
 
 def ai_ppt(data):
+    #call summerizer to summerize pdf 
+    summary = summarizer(data, max_length=130, min_length=30, do_sample=False)
+    print(summary)
     asyncio.run(ppt_content(data=data))
 
 
@@ -87,7 +94,7 @@ def main():
     uploaded_file = st.file_uploader('Choose your .pdf file', type="pdf")
     if uploaded_file is not None:
         extract_data(uploaded_file)
-    if st.button("Search"):
+    if st.button("Make PPT"):
         ai_ppt(data)
 
 if __name__ == '__main__':

requirements.txt

@@ -4,4 +4,6 @@ pdfplumber
 streamlit_pdf_viewer
 pydantic_ai
 nest_asyncio
+torch==2.2.2
+transformers==4.39.1
 #pydantic_ai.models.groq