Update app.py

app.py

@@ -1,29 +1,69 @@
 import streamlit as st
 from transformers import pipeline
+import logging
+
+# Setup logging
+def setup_logging():
+    logging.basicConfig(
+        level=logging.INFO,
+        format='%(asctime)s - %(levelname)s - %(message)s',
+        handlers=[
+            logging.StreamHandler()
+        ]
+    )
 
 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'.")
+    setup_logging()
+    logging.info("Starting the Streamlit app.")
 
-    # Initialize the summarization pipeline from Hugging Face
+    # Initialize the summarization pipeline
     summarizer = pipeline("summarization")
 
-    # Create a text area for user input
-    text = st.text_area("Enter text here:", placeholder="Type your text here...")
+    # 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.")
+
+    input_type = st.radio(
+        "Select Input Type:",
+        options=["Text", "Text File", "PDF", "DOCX", "Audio"],
+        index=0
+    )
+
+    file = None
+    text = None
+    audio = None
 
-    # Button to trigger summarization
-    if st.button("Summarize"):
-        if text:
-            try:
-                # Generate the summary using the Hugging Face model
+    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"])
+
+    if st.button("Report Result"):
+        try:
+            if input_type == "Text" and text:
+                logging.info("Processing text input.")
                 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("Text input processed successfully.")
+            elif input_type in ["Text File", "PDF", "DOCX"] and file:
+                logging.info(f"Processing {input_type} file: {file.name}")
+                # Add file reading logic here
+                summary = "File processing not implemented yet."
+            elif input_type == "Audio" and audio:
+                logging.info("Processing audio input.")
+                # Add audio processing logic here
+                summary = "Audio processing not implemented yet."
+            else:
+                summary = "Invalid input. Please provide a valid file or text."
+                logging.warning("Invalid input type provided.")
+
+            st.text_area("Report Result:", summary[0]['summary_text'] if isinstance(summary, list) else 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.")
+
+    logging.info("Closing the Streamlit app.")
 
 if __name__ == "__main__":
     main()