Update app.py

app.py

@@ -2,14 +2,32 @@ import gradio as gr
 import openai
 from langdetect import detect
 from transformers import pipeline
+from keybert import KeyBERT
+from fpdf import FPDF
 import os
 
 openai.api_key = os.getenv("OPENAI_API_KEY")  # Set this in HF Space secrets
 
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+kw_model = KeyBERT()
+# Sample brand list for detection (customize as needed)
+BRANDS = ["Zerodha", "Motilal", "ICICI", "HDFC", "ShareKhan", "IND Money", "Samsung", "Nike", "Adidas"]
+
+def extract_brands(text):
+    found = [brand for brand in BRANDS if brand.lower() in text.lower()]
+    return found if found else ["None detected"]
+
+def extract_topics(text, top_n=5):
+    keywords = kw_model.extract_keywords(text, top_n=top_n, stop_words='english')
+    topics = [kw for kw, score in keywords]
+    return topics if topics else ["None extracted"]
+
+def make_bullets(summary):
+    sentences = summary.replace("\n", " ").split('. ')
+    bullets = [f"- {s.strip()}" for s in sentences if s.strip()]
+    return "\n".join(bullets)
 
 def make_str(val):
-    # Always return string, even if val is None, bool, list, dict, etc.
     try:
         if val is None:
             return ""
@@ -23,10 +41,40 @@ def make_str(val):
     except Exception:
         return ""
 
+def create_pdf_report(language, transcript, transcript_en, summary, brands, topics, key_takeaways):
+    pdf = FPDF()
+    pdf.add_page()
+    pdf.set_font("Arial", "B", 16)
+    pdf.cell(0, 10, "Audio Transcript & Analysis Report", ln=True, align="C")
+    pdf.set_font("Arial", size=12)
+    pdf.ln(5)
+    pdf.cell(0, 10, f"Detected Language: {language}", ln=True)
+    pdf.ln(5)
+    pdf.multi_cell(0, 8, "Original Transcript:\n" + transcript)
+    pdf.ln(3)
+    pdf.multi_cell(0, 8, "English Transcript:\n" + transcript_en)
+    pdf.ln(3)
+    pdf.set_font("Arial", "B", 12)
+    pdf.cell(0, 10, "Brands Detected:", ln=True)
+    pdf.set_font("Arial", size=12)
+    pdf.multi_cell(0, 8, ", ".join(brands))
+    pdf.set_font("Arial", "B", 12)
+    pdf.cell(0, 10, "Key Topics:", ln=True)
+    pdf.set_font("Arial", size=12)
+    pdf.multi_cell(0, 8, ", ".join(topics))
+    pdf.set_font("Arial", "B", 12)
+    pdf.cell(0, 10, "Summary (Bulleted):", ln=True)
+    pdf.set_font("Arial", size=12)
+    for takeaway in key_takeaways.split('\n'):
+        pdf.multi_cell(0, 8, takeaway)
+    # Save to temporary file
+    pdf_file = "/tmp/analysis_report.pdf"
+    pdf.output(pdf_file)
+    return pdf_file
+
 def process_audio(audio_path):
-    # Accept only valid, non-empty file path (string)
     if not audio_path or not isinstance(audio_path, str):
-        return ("No audio file provided.", "", "", "")
+        return ("No audio file provided.", "", "", "", "", "", "", None)
     try:
         with open(audio_path, "rb") as audio_file:
             transcript = openai.audio.transcriptions.create(
@@ -36,7 +84,7 @@ def process_audio(audio_path):
             )
         transcript = make_str(transcript).strip()
     except Exception as e:
-        return (make_str(f"Error in transcription: {e}"), "", "", "")
+        return (make_str(f"Error in transcription: {e}"), "", "", "", "", "", "", None)
     try:
         detected_lang = detect(transcript)
         lang_text = {'en': 'English', 'hi': 'Hindi', 'ta': 'Tamil'}.get(detected_lang, detected_lang)
@@ -55,16 +103,25 @@ def process_audio(audio_path):
         except Exception as e:
             transcript_en = make_str(f"Error translating: {e}")
     try:
-        summary = summarizer(transcript_en, max_length=100, min_length=30, do_sample=False)
-        # Make sure we always extract a string summary, never bool/None/etc.
-        if isinstance(summary, list) and len(summary) > 0 and "summary_text" in summary[0]:
-            summary_text = make_str(summary[0]["summary_text"])
-        else:
-            summary_text = make_str(summary)
+        summary_obj = summarizer(transcript_en, max_length=100, min_length=30, do_sample=False)
+        summary = summary_obj[0]["summary_text"] if isinstance(summary_obj, list) and "summary_text" in summary_obj[0] else make_str(summary_obj)
     except Exception as e:
-        summary_text = make_str(f"Error summarizing: {e}")
-    # Return only strings, never bool/None/dict/list
-    return (make_str(lang_text), make_str(transcript), make_str(transcript_en), make_str(summary_text))
+        summary = make_str(f"Error summarizing: {e}")
+    # New: Brands, topics, bullets
+    brands = extract_brands(transcript_en)
+    topics = extract_topics(transcript_en)
+    key_takeaways = make_bullets(summary)
+    # New: PDF file generation
+    pdf_file = create_pdf_report(lang_text, transcript, transcript_en, summary, brands, topics, key_takeaways)
+    return (
+        lang_text,
+        transcript,
+        transcript_en,
+        ", ".join(brands),
+        ", ".join(topics),
+        key_takeaways,
+        pdf_file
+    )
 
 iface = gr.Interface(
     fn=process_audio,
@@ -73,10 +130,13 @@ iface = gr.Interface(
         gr.Textbox(label="Detected Language"),
         gr.Textbox(label="Original Transcript"),
         gr.Textbox(label="English Transcript (if translated)"),
-        gr.Textbox(label="Summary"),
+        gr.Textbox(label="Brands Detected"),
+        gr.Textbox(label="Key Topics"),
+        gr.Textbox(label="Bulleted Key Takeaways"),
+        gr.File(label="Download PDF Report")
     ],
-    title="Audio Transcript, Translation & Summary (via OpenAI Whisper API)",
-    description="Upload your audio file (MP3/WAV). This app transcribes via OpenAI Whisper API, detects language, translates to English if needed, and summarizes."
+    title="Audio Transcript, Brand & Topic Analysis (OpenAI Whisper + PDF Download)",
+    description="Upload your audio file (MP3/WAV). Get full transcript, summary, brand and topic detection, and download results as PDF."
 )
 
 iface.launch()