Create app.py

app.py

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+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()