app.py

import streamlit as st
from openai import OpenAI
import os
from wordcloud import WordCloud
import matplotlib.pyplot as plt
from fpdf import FPDF
import tempfile

# Load environment variables
MODEL = 'gpt-4'
client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))

st.title('Speech Analyzer')

# Upload audio file
audio_file = st.file_uploader("Upload an audio file", type=["mp3", "wav", "m4a"])

if audio_file:
    # Display audio player
    st.audio(audio_file)

    # Transcribe audio using Whisper API
    with st.spinner("Transcribing audio..."):
        transcription = client.audio.transcriptions.create(
            model="whisper-1",
            file=audio_file,
        )
    st.subheader("Transcription")
    st.markdown(transcription.text)

    # Summarize in multiple formats
    with st.spinner("Generating summary..."):
        summary_response = client.chat.completions.create(
            model=MODEL,
            messages=[
                {"role": "system", "content": "Summarize the transcription in two formats:\n1. A concise paragraph\n2. Key points in bullet form."},
                {"role": "user", "content": f"Here is the audio transcription: {transcription.text}"}
            ],
            temperature=0,
        )
    st.subheader("Summary and Key Points")
    summary_text = summary_response.choices[0].message.content
    st.markdown(summary_text)

    # Sentiment analysis
    with st.spinner("Analyzing sentiment..."):
        sentiment_response = client.chat.completions.create(
            model=MODEL,
            messages=[
                {"role": "system", "content": "You are an AI sentiment analyzer. Analyze the sentiment of the transcription as positive, negative, or neutral and explain your reasoning."},
                {"role": "user", "content": f"Here is the audio transcription: {transcription.text}"}
            ],
            temperature=0,
        )
    st.subheader("Sentiment Analysis")
    sentiment_text = sentiment_response.choices[0].message.content
    st.markdown(sentiment_text)

    # Generate Sentiment Word Cloud
    with st.spinner("Generating sentiment word cloud..."):
        wordcloud = WordCloud(width=800, height=400, background_color='white').generate(transcription.text)
    st.subheader("Word Cloud of the Transcription")
    fig, ax = plt.subplots()
    ax.imshow(wordcloud, interpolation='bilinear')
    ax.axis('off')
    st.pyplot(fig)

    # Extract keywords and entities
    with st.spinner("Extracting keywords and entities..."):
        keywords_response = client.chat.completions.create(
            model=MODEL,
            messages=[
                {"role": "system", "content": "Extract the key topics, keywords, and named entities (like people, places, or organizations) from the transcription."},
                {"role": "user", "content": f"Here is the audio transcription: {transcription.text}"}
            ],
            temperature=0,
        )
    st.subheader("Key Topics and Keywords")
    keywords_text = keywords_response.choices[0].message.content
    st.markdown(keywords_text)

    # Prepare analysis results
    analysis_results = f"""
### Transcription:
{transcription.text}

### Summary:
{summary_text}

### Sentiment Analysis:
{sentiment_text}

### Key Topics and Keywords:
{keywords_text}
"""

    # Export as TXT
    st.subheader("Export Analysis Results")
    st.download_button("Download as TXT", analysis_results, file_name="audio_analysis.txt")

    # Export as PDF
    def create_pdf(content):
        pdf = FPDF()
        pdf.add_page()
        pdf.set_font("Arial", size=12)
        
        # Add each line of content to the PDF
        for line in content.split("\n"):
            pdf.multi_cell(0, 10, line)
        
        # Save to a temporary file
        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmpfile:
            pdf.output(tmpfile.name)
            return tmpfile.name

    pdf_file = create_pdf(analysis_results)
    with open(pdf_file, "rb") as file:
        st.download_button("Download as PDF", file, file_name="audio_analysis.pdf", mime="application/pdf")