Update app.py

app.py

@@ -1,10 +1,36 @@
-
 import gradio as gr
 
-# Example Gradio function for sentiment analysis (adjust with your model/logic)
-def sentiment_analyze(input_text):
-    return f"Sentiment analyzed for: {input_text}"
+def process_keywords_and_video(url, excel_file):
+    metadata, error = fetch_video_metadata(url)
+    if error:
+        return error, None
+
+    transcript, error = fetch_transcript(url)
+    if error:
+        return error, None
+
+    sentences = split_long_sentences(transcript)
+    keywords, attributes = read_keywords(excel_file)
+    matched_keywords = match_keywords_in_sentences(sentences, keywords)
+    sentiment_results = analyze_sentiment_for_keywords(matched_keywords, sentences)
+    wordclouds = generate_word_clouds(matched_keywords)
+    pdf_file = generate_pdf_with_sections(metadata, sentiment_results, wordclouds)
+
+    return "Processing completed successfully!", pdf_file
+
+# Gradio App
+with gr.Blocks() as iface:
+    gr.Markdown("<h1>Auto-Insight: YouTube Video Analyzer for Automobiles</h1>")
+    video_url = gr.Textbox(label="YouTube Video URL", placeholder="Enter the YouTube video URL")
+    excel_file = gr.File(label="Upload Excel File with Keywords")
+    process_button = gr.Button("Analyze Video")
+    processing_status = gr.Textbox(label="Processing Status", interactive=False)
+    pdf_output = gr.File(label="Download Sentiment Report (PDF)")
+
+    process_button.click(
+        process_keywords_and_video,
+        inputs=[video_url, excel_file],
+        outputs=[processing_status, pdf_output]
+    )
 
-# Create Gradio interface
-app = gr.Interface(fn=sentiment_analyze, inputs="text", outputs="text")
-app.launch()
+iface.launch(share=True)