Update app.py

app.py

@@ -1,3 +1,62 @@
+# 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
@@ -11,9 +70,6 @@ MODEL_NAME = "llama3-8b-8192"
 # 🎙 Load Whisper
 transcriber = whisper.load_model("base")
 
-inputs=gr.Audio(source="microphone", type="filepath", label="🎙️ Speak Now or Upload Audio"),
-
-
 def transcribe_and_summarize(audio):
     # Step 1: Transcribe + Detect Language
     result = transcriber.transcribe(audio)
@@ -44,17 +100,17 @@ 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,
-    inputs=gr.Audio(type="filepath", label="🎧 Upload Audio (English or Urdu)"),
+    inputs=gr.Audio(source="microphone", type="filepath", label="🎙️ Speak or Upload Audio (English/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."
+    description="🎙️ Speak directly or upload an English/Urdu audio file. This app transcribes and summarizes using Whisper + Groq (LLaMA3)."
 )
 
 demo.launch()
 
-