Create app.py

app.py

@@ -0,0 +1,102 @@
+import gradio as gr
+import os
+import requests
+
+# Configuration for both endpoints
+TRANSCRIPTION_ENDPOINT = "https://your-whisper-endpoint.endpoints.huggingface.cloud/api/v1/audio/transcriptions"
+SUMMARIZATION_ENDPOINT = "https://your-qwen-endpoint.endpoints.huggingface.cloud/v1/chat/completions"
+HF_TOKEN = os.getenv("HF_TOKEN")  # Your Hugging Face Hub token
+
+# Headers for authentication
+headers = {
+    "Authorization": f"Bearer {HF_TOKEN}"
+}
+
+def transcribe_audio(audio_file_path):
+    """Transcribe audio using direct requests to the endpoint"""
+    
+    # Read audio file and prepare for upload
+    with open(audio_file_path, "rb") as audio_file:
+        files = {"file": audio_file.read()}
+    
+    # Make the request to the transcription endpoint
+    response = requests.post(TRANSCRIPTION_ENDPOINT, headers=headers, files=files)
+    
+    if response.status_code == 200:
+        result = response.json()
+        return result.get("text", "No transcription available")
+    else:
+        return f"Error: {response.status_code} - {response.text}"
+
+def generate_summary(transcript):
+    """Generate summary using requests to the chat completions endpoint"""
+    
+    prompt = f"""
+    Analyze this meeting transcript and provide:
+    1. A concise summary of key points
+    2. Action items with responsible parties
+    3. Important decisions made
+    
+    Transcript: {transcript}
+    
+    Format with clear sections:
+    ## Summary
+    ## Action Items  
+    ## Decisions Made
+    """
+    
+    # Prepare the payload using the Messages API format
+    payload = {
+        "model": "your-qwen-endpoint-name",  # Use the name of your endpoint
+        "messages": [{"role": "user", "content": prompt}],
+        "max_tokens": 1000,
+        "temperature": 0.7,
+        "stream": False
+    }
+    
+    # Headers for chat completions
+    chat_headers = {
+        "Accept": "application/json",
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {HF_TOKEN}"
+    }
+    
+    # Make the request
+    response = requests.post(SUMMARIZATION_ENDPOINT, headers=chat_headers, json=payload)
+    response.raise_for_status()
+    
+    # Parse the response
+    result = response.json()
+    return result["choices"][0]["message"]["content"]
+
+def process_meeting_audio(audio_file):
+    """Main processing function that handles the complete workflow"""
+    if audio_file is None:
+        return "Please upload an audio file.", ""
+    
+    try:
+        # Step 1: Transcribe the audio
+        transcript = transcribe_audio(audio_file)
+        
+        # Step 2: Generate summary from transcript
+        summary = generate_summary(transcript)
+        
+        return transcript, summary
+    
+    except Exception as e:
+        return f"Error processing audio: {str(e)}", ""
+
+# Create Gradio interface
+app = gr.Interface(
+    fn=process_meeting_audio,
+    inputs=gr.Audio(label="Upload Meeting Audio", type="filepath"),
+    outputs=[
+        gr.Textbox(label="Full Transcript", lines=10),
+        gr.Textbox(label="Meeting Summary", lines=8),
+    ],
+    title="🎤 AI Meeting Notes",
+    description="Upload audio to get instant transcripts and summaries.",
+)
+
+if __name__ == "__main__":
+    app.launch()