fix gradio client for huggingface Space

app.py

@@ -2,24 +2,29 @@
 
 import os
 import gradio as gr
+from gradio_client import Client
+import requests
 
 from dotenv import load_dotenv
 from pydub import AudioSegment
 from tqdm.auto import tqdm
-print('starting')
 
-load_dotenv()
+print("starting")
 
-from gradio_client import Client
+load_dotenv()
 
 HF_API = os.getenv("HF_API")
 SEAMLESS_API_URL = os.getenv("SEAMLESS_API_URL")  # path to Seamlessm4t API endpoint
 GPU_AVAILABLE = os.getenv("GPU_AVAILABLE")
 DEFAULT_TARGET_LANGUAGE = "French"
-MISTRAL_SUMMARY_URL= "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"
-LLAMA_SUMMARY_URL="https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
+MISTRAL_SUMMARY_URL = (
+    "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"
+)
+LLAMA_SUMMARY_URL = (
+    "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
+)
 
-print('env setup ok')
+print("env setup ok")
 
 
 DESCRIPTION = """
@@ -34,29 +39,31 @@ To duplicate this repo, you have to give permission from three reopsitories and
 
 """
 from pyannote.audio import Pipeline
-#initialize diarization pipeline
+
+# initialize diarization pipeline
 diarizer = Pipeline.from_pretrained(
-    "pyannote/speaker-diarization-3.1",
-    use_auth_token=HF_API)
+    "pyannote/speaker-diarization-3.1", use_auth_token=HF_API
+)
 # send pipeline to GPU (when available)
 import torch
+
 diarizer.to(torch.device(GPU_AVAILABLE))
 
-print('diarizer setup ok')
+print("diarizer setup ok")
 
 
 # predict is a generator that incrementally yields recognized text with speaker label
 def predict(target_language, input_audio):
-    print('->predict started')
+    print("->predict started")
     print(target_language, type(input_audio), input_audio)
 
-    print('-->diarization')
+    print("-->diarization")
     diarized = diarizer(input_audio, min_speakers=2, max_speakers=5)
-    
-    print('-->automatic speech recognition')
+
+    print("-->automatic speech recognition")
     # split audio according to diarization
     song = AudioSegment.from_wav(input_audio)
-    client = Client(SEAMLESS_API_URL, hf_token=HF_API)
+    client = Client(SEAMLESS_API_URL, hf_token=HF_API, serialize=False)
     output_text = ""
     for turn, _, speaker in diarized.itertracks(yield_label=True):
         print(speaker, turn)
@@ -64,11 +71,7 @@ def predict(target_language, input_audio):
             clipped = song[turn.start * 1000 : turn.end * 1000]
             clipped.export(f"my.wav", format="wav", bitrate=16000)
 
-            result = client.predict(
-		        f"my.wav",
-		        target_language,
-                api_name="/asr"
-            )
+            result = client.predict(f"my.wav", target_language, api_name="/asr")
 
             current_text = f"speaker: {speaker} text: {result} "
             print(current_text)
@@ -81,11 +84,25 @@ def predict(target_language, input_audio):
             print(e)
 
 
-import requests
+def automatic_speech_recognition(language, filename):
+    match language:
+        case "French":
+            api_url = "https://api-inference.huggingface.co/models/bofenghuang/whisper-large-v3-french"
+        case "English":
+            api_url = "https://api-inference.huggingface.co/models/facebook/wav2vec2-base-960h"
+        case _:
+            return f"Unknown language {language}"
+    print(f"-> automatic_speech_recognition with {api_url}")
+    with open(filename, "rb") as f:
+        data = f.read()
+    response = requests.post(
+        api_url, headers={"Authorization": f"Bearer {HF_API}"}, data=data
+    )
+    return response.json()["text"]
 
 
 def generate_summary_llama3(language, transcript):
-    queryTxt = f'''
+    queryTxt = f"""
 <|begin_of_text|><|start_header_id|>system<|end_header_id|>
 
 You are a helpful and truthful patient-doctor encounter summary writer.
@@ -108,26 +125,27 @@ The summary only includes relevant sections.
 {transcript}
 </transcript><|eot_id|>
 <|start_header_id|>assistant<|end_header_id|>
-'''
-    
+"""
+
     payload = {
-	"inputs": queryTxt,
-	"parameters": {
-		"return_full_text": False,
-		"wait_for_model": True,
-		"min_length": 1000
-	},
-	"options": {
-		"use_cache": False
-	}
+        "inputs": queryTxt,
+        "parameters": {
+            "return_full_text": False,
+            "wait_for_model": True,
+            "min_length": 1000,
+        },
+        "options": {"use_cache": False},
     }
 
-    response = requests.post(LLAMA_SUMMARY_URL, headers = {"Authorization": f"Bearer {HF_API}"}, json=payload)
+    response = requests.post(
+        LLAMA_SUMMARY_URL, headers={"Authorization": f"Bearer {HF_API}"}, json=payload
+    )
     print(response.json())
-    return response.json()[0]['generated_text'][len('<summary>'):]
+    return response.json()[0]["generated_text"][len("<summary>") :]
+
 
 def generate_summary_mistral(language, transcript):
-    sysPrompt = f'''<s>[INST]
+    sysPrompt = f"""<s>[INST]
 You are a helpful and truthful patient-doctor encounter summary writer.
 Users sends you transcripts of patient-doctor encounter and you create accurate and concise summaries.
 The summary only contains informations from the transcript.
@@ -143,41 +161,43 @@ The summary only includes relevant sections.
     # Additional Notes
     </template>
 
-'''
-    queryTxt=f'''
+"""
+    queryTxt = f"""
 <transcript>
 {transcript}
 </transcript>
 [/INST]
-'''
-    
+"""
+
     payload = {
-	"inputs": sysPrompt + queryTxt,
-	"parameters": {
-		"return_full_text": False,
-		"wait_for_model": True,
-		"min_length": 1000
-	},
-	"options": {
-		"use_cache": False
-	}
+        "inputs": sysPrompt + queryTxt,
+        "parameters": {
+            "return_full_text": False,
+            "wait_for_model": True,
+            "min_length": 1000,
+        },
+        "options": {"use_cache": False},
     }
 
-    response = requests.post(MISTRAL_SUMMARY_URL, headers = {"Authorization": f"Bearer {HF_API}"}, json=payload)
+    response = requests.post(
+        MISTRAL_SUMMARY_URL, headers={"Authorization": f"Bearer {HF_API}"}, json=payload
+    )
     print(response.json())
-    return response.json()[0]['generated_text'][len('<summary>'):]
+    return response.json()[0]["generated_text"][len("<summary>") :]
+
 
 def generate_summary(model, language, transcript):
     match model:
         case "Mistral-7B":
             print("-> summarize with mistral")
-            return generate_summary_mistral( language, transcript)
+            return generate_summary_mistral(language, transcript)
         case "LLAMA3":
             print("-> summarize with llama3")
             return generate_summary_llama3(language, transcript)
         case _:
             return f"Unknown model {model}"
 
+
 def update_audio_ui(audio_source: str) -> tuple[dict, dict]:
     mic = audio_source == "microphone"
     return (
@@ -191,16 +211,14 @@ with gr.Blocks() as demo:
     with gr.Group():
         with gr.Row():
             target_language = gr.Dropdown(
-                choices= ["French", "English"],
+                choices=["French", "English"],
                 label="Output Language",
                 value="French",
                 interactive=True,
                 info="Select your target language",
             )
         with gr.Row() as audio_box:
-            input_audio = gr.Audio(
-                type="filepath"
-            )
+            input_audio = gr.Audio(type="filepath")
         submit = gr.Button("Transcribe")
         transcribe_output = gr.Textbox(
             label="Transcribed Text",
@@ -212,16 +230,13 @@ with gr.Blocks() as demo:
         )
         submit.click(
             fn=predict,
-            inputs=[
-                target_language,
-                input_audio
-            ],
+            inputs=[target_language, input_audio],
             outputs=[transcribe_output],
             api_name="predict",
         )
         with gr.Row():
             sumary_model = gr.Dropdown(
-                choices= ["Mistral-7B", "LLAMA3"],
+                choices=["Mistral-7B", "LLAMA3"],
                 label="Summary model",
                 value="Mistral-7B",
                 interactive=True,
@@ -238,15 +253,10 @@ with gr.Blocks() as demo:
         )
         summarize.click(
             fn=generate_summary,
-            inputs=[
-                sumary_model,
-                target_language,
-                transcribe_output
-            ],
+            inputs=[sumary_model, target_language, transcribe_output],
             outputs=[summary_output],
             api_name="predict",
         )
     gr.Markdown(DUPLICATE)
 
 demo.queue(max_size=50).launch()
-