Update app.py

app.py

@@ -1,12 +1,16 @@
 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('AUDIO ANALYZER')
+st.title('Advanced Audio Analyzer')
 
 # Upload audio file
 audio_file = st.file_uploader("Upload an audio file", type=["mp3", "wav", "m4a"])
@@ -15,22 +19,100 @@ if audio_file:
     # Display audio player
     st.audio(audio_file)
 
-    # Transcribe audio
-    transcription = client.audio.transcriptions.create(
-        model="whisper-1",
-        file=audio_file,
-    )
-
-    # Analyze transcription
-    response = client.chat.completions.create(
-        model=MODEL,
-        messages=[
-            {"role": "system", "content": """You are an AI audio analyzer. 
-             You are tasked to analyze the audio and create a summary of the provided transcription. Please respond in Markdown."""},
-            {"role": "user", "content": f"The audio transcription is: {transcription.text}"}
-        ],
-        temperature=0,
-    )
-
-    # Display the response
-    st.markdown(response.choices[0].message.content)
+    # 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 in Multiple Formats")
+    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 Sentiment Analysis")
+    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")