app.py

# app.py

import gradio as gr
from backend import fetch_video_metadata, fetch_transcript, split_long_sentences, match_keywords_in_sentences, read_keywords
import pandas as pd

# Gradio interface function to call the backend functions
def analyze_sentiment(video_url, excel_file_path):
    # Fetch video metadata
    metadata, metadata_error = fetch_video_metadata(video_url)
    if metadata_error:
        return f"Error: {metadata_error}"

    # Fetch transcript
    transcript, transcript_error = fetch_transcript(video_url)
    if transcript_error:
        return f"Error: {transcript_error}"

    # Process sentences from transcript
    sentences = split_long_sentences(transcript)

    # Read keywords from uploaded Excel file
    keywords, attributes = read_keywords(excel_file_path)

    # Match keywords in sentences
    matched_keywords = match_keywords_in_sentences(sentences, keywords)

    # Prepare the report for Gradio display
    report = {
        "Metadata": metadata,
        "Matched Keywords": matched_keywords,
        "Sentences": sentences
    }

    return report

# Gradio interface setup
interface = gr.Interface(
    fn=analyze_sentiment,
    inputs=[
        gr.inputs.Textbox(label="YouTube Video URL"),
        gr.inputs.File(label="Upload Keywords Excel File")  # User uploads keywords file
    ],
    outputs=[
        gr.outputs.JSON(label="Metadata and Sentiment Analysis Results"),
        gr.outputs.Textbox(label="Sentences from Transcript")  # Display sentences detected in transcript
    ]
)

# Launch Gradio interface
interface.launch()









import gradio as gr

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]
    )

iface.launch(share=True)