Create app.py

app.py

@@ -0,0 +1,93 @@
+import gradio as gr
+import logging
+from transformers import pipeline
+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
+
+class TextSummarizer:
+    def __init__(self, model_name="t5-small"):
+        self.summarizer = pipeline("summarization", model=model_name)
+
+    def summarize(self, text):
+        if not text:
+            return "No text to summarize."
+        summary = self.summarizer(text, max_length=150, min_length=30, do_sample=False)
+        return summary[0]['summary_text']
+
+def process_input(input_type, input_data):
+    try:
+        logging.info(f"Processing input type: {input_type}")
+        
+        if input_type == "Text":
+            processed_text = handle_text_input(input_data)
+        elif input_type == "Text File":
+            processed_text = read_text_file(input_data)
+        elif input_type == "PDF":
+            processed_text = read_pdf_file(input_data)
+        elif input_type == "DOCX":
+            processed_text = read_docx_file(input_data)
+        elif input_type == "Audio":
+            processed_text = audio_to_text(input_data)
+        else:
+            return "Invalid input type."
+
+        if processed_text:
+            summary = summarizer.summarize(processed_text)
+            logging.info(f"{input_type} processed successfully.")
+            return summary
+        else:
+            logging.error(f"Failed to process {input_type}.")
+            return "Failed to process the input. Check logs for more details."
+    except Exception as e:
+        logging.error(f"Error during summarization: {e}")
+        return "An error occurred during summarization. Please check the logs for more details."
+
+def update_input_type(input_type):
+    if input_type == "Text":
+        return gr.update(value="", placeholder="Type your text here...")
+    elif input_type == "Text File":
+        return gr.update(value=None, type="file", label="Upload your text file")
+    elif input_type == "PDF":
+        return gr.update(value=None, type="file", label="Upload your PDF file")
+    elif input_type == "DOCX":
+        return gr.update(value=None, type="file", label="Upload your DOCX file")
+    elif input_type == "Audio":
+        return gr.update(value=None, type="file", label="Upload your audio file")
+    else:
+        return gr.update(value="", placeholder="Invalid input type")
+
+def main():
+    # Setup logging
+    setup_logging()
+    logging.info("Starting GenAI Lab Report Analyzer with Gradio.")
+    
+    # Initialize summarizer
+    global summarizer
+    summarizer = TextSummarizer()
+
+    # Gradio interface
+    input_type = gr.inputs.Radio(choices=["Text", "Text File", "PDF", "DOCX", "Audio"], label="Select Input Type")
+    input_data = gr.inputs.Textbox(lines=5, label="Enter your text here")  # Default for text input
+
+    def interface_fn(input_type, input_data):
+        updated_input = update_input_type(input_type)
+        return process_input(input_type, input_data), updated_input
+
+    interface = gr.Interface(
+        fn=interface_fn,
+        inputs=[input_type, input_data],
+        outputs=[gr.outputs.Textbox(label="Report Result"), input_data],
+        title="GenAI Lab Report Analyzer",
+        description="Upload a file, record audio, or type text to generate a summary. Select the appropriate input type and provide the input.",
+        live=True,
+        theme="default",
+        layout="vertical",
+        allow_flagging="never"
+    )
+
+    interface.launch()
+
+if __name__ == "__main__":
+    main()