Upload app.py

app.py

@@ -0,0 +1,112 @@
+import gradio as gr
+import torch
+import os
+from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
+from pyannote.audio import Pipeline as DiarizationPipeline
+import whisper
+import tempfile
+import shutil
+from pydub import AudioSegment
+
+# Load whisper model
+whisper_model = whisper.load_model("base")  # Use "small" or "medium" if needed
+
+# Load summarization pipeline
+summarizer_tokenizer = AutoTokenizer.from_pretrained("t5-small")
+summarizer_model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
+summarizer = pipeline("summarization", model=summarizer_model, tokenizer=summarizer_tokenizer)
+
+def convert_to_wav(input_path, output_path):
+    audio = AudioSegment.from_file(input_path)
+    audio.export(output_path, format="wav")
+
+def transcribe_audio(audio_path):
+    result = whisper_model.transcribe(audio_path, fp16=torch.cuda.is_available())
+    return result['text']
+
+def diarize_audio(audio_path, hf_token):
+    os.environ["HF_TOKEN"] = hf_token
+    diarization_pipeline = DiarizationPipeline.from_pretrained("pyannote/speaker-diarization", use_auth_token=hf_token)
+    diarization = diarization_pipeline(audio_path)
+    return diarization
+
+def combine_diarized_transcript(diarization, full_text):
+    # Basic speaker labeling using diarization and full text
+    # Note: This is a simplified alignment using time chunks only
+    chunks = []
+    for turn, _, speaker in diarization.itertracks(yield_label=True):
+        start, end = turn.start, turn.end
+        chunks.append(f"{speaker}: [from {start:.1f}s to {end:.1f}s]")
+    # Combine for display/demo
+    return "\n".join(chunks) + "\n" + full_text
+
+def summarize_text(text):
+    prefix = "summarize: " + text.strip()
+    inputs = summarizer_tokenizer.encode(prefix, return_tensors="pt", max_length=512, truncation=True)
+    summary_ids = summarizer_model.generate(inputs, max_length=100, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
+    return summarizer_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+def process_pipeline(audio_file, hf_token):
+    if not hf_token:
+        return "", "", "Error: HuggingFace token is required."
+
+    if not os.path.exists(audio_file) or os.path.getsize(audio_file) == 0:
+        return "", "", "Error: Uploaded file is missing or empty."
+
+    # Step 1: Convert to WAV if needed
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_wav:
+        try:
+            sound = AudioSegment.from_file(audio_file)
+            sound.export(tmp_wav.name, format="wav")
+            tmp_path = tmp_wav.name
+        except Exception as e:
+            return "", "", f"Audio conversion failed: {str(e)}"
+
+    # Step 2: Transcription (Whisper)
+    try:
+        transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base",return_timestamps=True, device=0 if torch.cuda.is_available() else -1)
+        result = transcriber(tmp_path)
+        transcript = result["text"]
+    except Exception as e:
+        return "", "", f"Transcription failed: {str(e)}"
+
+    # Step 3: Summarization
+    try:
+        summarizer = pipeline("summarization", model="facebook/bart-large-cnn", device=0 if torch.cuda.is_available() else -1)
+        summary = summarizer(transcript, max_length=130, min_length=30, do_sample=False)[0]["summary_text"]
+    except Exception as e:
+        return transcript, "", f"Summarization failed: {str(e)}"
+
+    return tmp_path, transcript, summary
+
+description = """
+### 🩺 GP Consultation Summarizer (Demo App)
+
+This app:
+1. Transcribes short consultation audio using Whisper
+2. Identifies who spoke when using PyAnnote speaker diarization
+3. Combines both into a labeled transcript
+4. Generates a short summary using T5-small
+
+⚠️ **Note:** Best for short consultations (under 5–6 minutes).
+⚠️ You must provide your own Hugging Face token (required for diarization).
+"""
+
+app = gr.Interface(
+    fn=process_pipeline,
+    inputs=[
+        gr.Audio(type="filepath", label="Upload Consultation Audio (.wav)"),
+        gr.Textbox(label="Your Hugging Face Token", type="password")
+    ],
+    outputs=[
+        gr.Textbox(label="Raw Transcript"),
+        gr.Textbox(label="Labeled Transcript (with Speaker Info)"),
+        gr.Textbox(label="Summary")
+    ],
+    title="GP Consultation Summarizer",
+    description=description,
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    app.launch(share=True)