Update app.py

app.py

@@ -1,62 +1,3 @@
-# import gradio as gr
-# import whisper
-# import os
-# from groq import Groq
-
-# # 🔐 Get Groq API key securely from Hugging Face Secrets
-# GROQ_API_KEY = os.getenv("GROQ_API_KEY")
-# groq_client = Groq(api_key=GROQ_API_KEY)
-# MODEL_NAME = "llama3-8b-8192"
-
-# # 🎙 Load Whisper
-# transcriber = whisper.load_model("base")
-
-
-
-
-# def transcribe_and_summarize(audio):
-#     # Step 1: Transcribe + Detect Language
-#     result = transcriber.transcribe(audio)
-#     transcript = result["text"]
-#     detected_lang = result["language"]
-
-#     # Step 2: Summarize in the same language
-#     if detected_lang == "en":
-#         system_prompt = "You are an expert English summarizer."
-#         user_prompt = f"Please summarize the following English text:\n\n{transcript}"
-#     elif detected_lang == "ur":
-#         system_prompt = "آپ ایک ماہر خلاصہ نگار ہیں جو اردو میں خلاصہ فراہم کرتے ہیں۔"
-#         user_prompt = f"براہ کرم مندرجہ ذیل اردو متن کا خلاصہ فراہم کریں:\n\n{transcript}"
-#     else:
-#         system_prompt = "You are a helpful summarizer."
-#         user_prompt = f"Summarize this text:\n\n{transcript}"
-
-#     response = groq_client.chat.completions.create(
-#         model=MODEL_NAME,
-#         messages=[
-#             {"role": "system", "content": system_prompt},
-#             {"role": "user", "content": user_prompt}
-#         ]
-#     )
-#     summary = response.choices[0].message.content.strip()
-
-#     lang_label = "English" if detected_lang == "en" else "Urdu" if detected_lang == "ur" else detected_lang.upper()
-
-#     return f"[{lang_label}] {transcript}", f"[{lang_label}] {summary}"
-
-# demo = gr.Interface(
-#     fn=transcribe_and_summarize,
-#     inputs=gr.Audio(type="filepath", label="🎧 Upload Audio (English or Urdu)"),
-#     outputs=[
-#         gr.Textbox(label="📝 Transcript"),
-#         gr.Textbox(label="🧠 Summary")
-#     ],
-#     title="🗣️ Multilingual Audio Summarizer",
-#     description="Upload English or Urdu audio. The app transcribes and summarizes in the same language using Whisper + Groq."
-# )
-
-# demo.launch()
-
 import gradio as gr
 import whisper
 import os
@@ -70,6 +11,9 @@ MODEL_NAME = "llama3-8b-8192"
 # 🎙 Load Whisper
 transcriber = whisper.load_model("base")
 
+
+
+
 def transcribe_and_summarize(audio):
     # Step 1: Transcribe + Detect Language
     result = transcriber.transcribe(audio)
@@ -100,17 +44,15 @@ def transcribe_and_summarize(audio):
 
     return f"[{lang_label}] {transcript}", f"[{lang_label}] {summary}"
 
-# ✅ Enable microphone + upload support
 demo = gr.Interface(
     fn=transcribe_and_summarize,
-   gr.Audio(type="filepath", label="🎙️ Speak or Upload Audio (English/Urdu)")
-
+    inputs=gr.Audio(type="filepath", label="🎧 Upload Audio (English or Urdu)"),
     outputs=[
         gr.Textbox(label="📝 Transcript"),
         gr.Textbox(label="🧠 Summary")
     ],
     title="🗣️ Multilingual Audio Summarizer",
-    description="🎙️ Speak directly or upload an English/Urdu audio file. This app transcribes and summarizes using Whisper + Groq (LLaMA3)."
+    description="Upload English or Urdu audio. The app transcribes and summarizes in the same language using Whisper + Groq."
 )
 
 demo.launch()