Update app.py

app.py

@@ -1,83 +1,29 @@
 import streamlit as st
-import logging
-from models.summarizer import TextSummarizer
-from services.text_input_handler import handle_text_input
-from services.file_input_handler import read_text_file, read_pdf_file, read_docx_file
-from services.audio_input_handler import audio_to_text
-from utils.logging_utils import setup_logging
-...
-def main():
-    # Setup logging
-    setup_logging()
-    logging.info("Starting GenAI Lab Report Analyzer with Streamlit.")
-    
-    # Initialize summarizer
-    summarizer = TextSummarizer()
-
-    # Streamlit UI
-    st.title("GenAI Lab Report Analyzer")
-    st.write("Upload a file, record audio, or type text to generate a summary. Select the appropriate input type and provide the input.")
+from transformers import pipeline
 
-    input_type = st.radio(
-        "Select Input Type:",
-        options=["Text", "Text File", "PDF", "DOCX", "Audio"],
-        index=0
-    )
-
-    file = None
-    text = None
-    audio = None
+def main():
+    # Set up the Streamlit app title and description
+    st.title("Hugging Face Model Summarization")
+    st.write("This app uses a Hugging Face model to summarize text. Enter your text below and click 'Summarize'.")
 
-    if input_type == "Text":
-        text = st.text_area("Enter your text here:", placeholder="Type your text here...")
-    elif input_type in ["Text File", "PDF", "DOCX"]:
-        file = st.file_uploader(f"Upload your {input_type}:", type=["txt", "pdf", "docx"])
-    elif input_type == "Audio":
-        audio = st.file_uploader("Upload your audio file:", type=["wav", "mp3", "m4a"])
+    # Initialize the summarization pipeline from Hugging Face
+    summarizer = pipeline("summarization")
 
-    if st.button("Report Result"):
-        try:
-            if input_type == "Text" and text:
-                logging.info("Processing text input.")
-                processed_text = handle_text_input(text)
-                summary = summarizer.summarize(processed_text)
-                logging.info("Text input processed successfully.")
-            elif input_type in ["Text File", "PDF", "DOCX"] and file:
-                if input_type == "Text File":
-                    logging.info(f"Processing text file: {file.name}")
-                    processed_text = read_text_file(file)
-                elif input_type == "PDF":
-                    logging.info(f"Processing PDF file: {file.name}")
-                    processed_text = read_pdf_file(file)
-                elif input_type == "DOCX":
-                    logging.info(f"Processing DOCX file: {file.name}")
-                    processed_text = read_docx_file(file)
-                
-                if processed_text:
-                    summary = summarizer.summarize(processed_text)
-                    logging.info(f"{input_type} processed successfully.")
-                else:
-                    summary = "Failed to process the file. Check logs for more details."
-                    logging.error(f"Failed to process {input_type}: {file.name}")
-            elif input_type == "Audio" and audio:
-                logging.info("Processing audio input.")
-                processed_text = audio_to_text(audio)
-                if processed_text:
-                    summary = summarizer.summarize(processed_text)
-                    logging.info("Audio input processed successfully.")
-                else:
-                    summary = "Failed to convert audio to text. Check logs for more details."
-                    logging.error("Failed to convert audio to text.")
-            else:
-                summary = "Invalid input. Please provide a valid file or text."
-                logging.warning("Invalid input type provided.")
+    # Create a text area for user input
+    text = st.text_area("Enter text here:", placeholder="Type your text here...")
 
-            st.text_area("Report Result:", summary, height=200)
-        except Exception as e:
-            logging.error(f"Error during summarization: {e}")
-            st.error("An error occurred during summarization. Please check the logs for more details.")
+    # Button to trigger summarization
+    if st.button("Summarize"):
+        if text:
+            try:
+                # Generate the summary using the Hugging Face model
+                summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
+                st.write("Summary:")
+                st.write(summary[0]['summary_text'])
+            except Exception as e:
+                st.error(f"An error occurred during summarization: {e}")
+        else:
+            st.error("Please enter some text to summarize.")
 
-    logging.info("Closing GenAI Lab Report Analyzer with Streamlit.")
-...
 if __name__ == "__main__":
     main()