Update app.py

app.py

@@ -20,84 +20,66 @@ import huggingface_hub
 from huggingface_hub import Repository, hf_hub_download, upload_file
 from datetime import datetime
 
+# ---------------------------------------------
 # Dataset and Token links - change awacke1 to your own HF id, and add a HF_TOKEN copy to your repo for write permissions
 # This should allow you to save your results to your own Dataset hosted on HF. ---
-DATASET_REPO_URL = "https://huggingface.co/datasets/awacke1/Carddata.csv"
-DATASET_REPO_ID = "awacke1/Carddata.csv"
-DATA_FILENAME = "Carddata.csv"
-DATA_FILE = os.path.join("data", DATA_FILENAME)
-HF_TOKEN = os.environ.get("HF_TOKEN")
-# ---------------------------------------------
-
-SCRIPT = """
-<script>
-if (!window.hasBeenRun) {
-    window.hasBeenRun = true;
-    console.log("should only happen once");
-    document.querySelector("button.submit").click();
-}
-</script>
-"""
-try:
-    hf_hub_download(
-        repo_id=DATASET_REPO_ID,
-        filename=DATA_FILENAME,
-        cache_dir=DATA_DIRNAME,
-        force_filename=DATA_FILENAME
-    )
-except:
-    print("file not found")
-repo = Repository(
-    local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
-)
-def generate_html() -> str:
-    with open(DATA_FILE) as csvfile:
-        reader = csv.DictReader(csvfile)
-        rows = []
-        for row in reader:
-            rows.append(row)
-        rows.reverse()
-        if len(rows) == 0:
-            return "no messages yet"
-        else:
-            html = "<div class='chatbot'>"
-            for row in rows:
-                html += "<div>"
-                html += f"<span>{row['inputs']}</span>"
-                html += f"<span class='outputs'>{row['outputs']}</span>"
-                html += "</div>"
-            html += "</div>"
-            return html
-            
-            
-def store_message(name: str, message: str):
-    if name and message:
-        with open(DATA_FILE, "a") as csvfile:
-            writer = csv.DictWriter(csvfile, fieldnames=["name", "message", "time"])
-            writer.writerow(
-                {"name": name.strip(), "message": message.strip(), "time": str(datetime.now())}
-            )
-        # uncomment line below to begin saving - 
-        commit_url = repo.push_to_hub()
-    return ""            
-
-
-iface = gr.Interface(
-    store_message,
-    [
-        inputs.Textbox(placeholder="Your name"),
-        inputs.Textbox(placeholder="Your message", lines=2),
-    ],
-    "html",
-    css="""
-    .message {background-color:cornflowerblue;color:white; padding:4px;margin:4px;border-radius:4px; }
-    """,
-    title="Reading/writing to a HuggingFace dataset repo from Spaces",
-    description=f"This is a demo of how to do simple *shared data persistence* in a Gradio Space, backed by a dataset repo.",
-    article=f"The dataset repo is [{DATASET_REPO_URL}]({DATASET_REPO_URL})",
-)
-
-
+#DATASET_REPO_URL = "https://huggingface.co/datasets/awacke1/Carddata.csv"
+#DATASET_REPO_ID = "awacke1/Carddata.csv"
+#DATA_FILENAME = "Carddata.csv"
+#DATA_FILE = os.path.join("data", DATA_FILENAME)
+#HF_TOKEN = os.environ.get("HF_TOKEN")
+#SCRIPT = """
+
+#<script>
+#if (!window.hasBeenRun) {
+#    window.hasBeenRun = true;
+#    console.log("should only happen once");
+#    document.querySelector("button.submit").click();
+#}
+#</script>
+#"""
+
+#try:
+#    hf_hub_download(
+#        repo_id=DATASET_REPO_ID,
+#        filename=DATA_FILENAME,
+#        cache_dir=DATA_DIRNAME,
+#        force_filename=DATA_FILENAME
+#    )
+#except:
+#    print("file not found")
+#repo = Repository(
+#    local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
+#)
+           
+#def store_message(name: str, message: str):
+#    if name and message:
+#        with open(DATA_FILE, "a") as csvfile:
+#            writer = csv.DictWriter(csvfile, fieldnames=["name", "message", "time"])
+#            writer.writerow(
+#                {"name": name.strip(), "message": message.strip(), "time": str(datetime.now())}
+#            )
+#        # uncomment line below to begin saving - 
+#        commit_url = repo.push_to_hub()
+#    return ""            
+
+#iface = gr.Interface(
+#    store_message,
+#    [
+#        inputs.Textbox(placeholder="Your name"),
+#        inputs.Textbox(placeholder="Your message", lines=2),
+#    ],
+#    "html",
+#    css="""
+#    .message {background-color:cornflowerblue;color:white; padding:4px;margin:4px;border-radius:4px; }
+#    """,
+#    title="Reading/writing to a HuggingFace dataset repo from Spaces",
+#    description=f"This is a demo of how to do simple *shared data persistence* in a Gradio Space, backed by a dataset repo.",
+#    article=f"The dataset repo is [{DATASET_REPO_URL}]({DATASET_REPO_URL})",
+#)
+
+
+# main -------------------------
 mname = "facebook/blenderbot-400M-distill"
 model = BlenderbotForConditionalGeneration.from_pretrained(mname)
 tokenizer = BlenderbotTokenizer.from_pretrained(mname)
@@ -132,8 +114,9 @@ def chat(message, history):
     history_useful = add_note_to_history(response, history_useful)
     list_history = history_useful[0].split('</s> <s>')
     history.append((list_history[-2], list_history[-1]))
-    store_message(message, response) # Save to dataset
+#    store_message(message, response) # Save to dataset - uncomment if you uncomment above to save inputs and outputs to your dataset
     return history, history
+    
 
 SAMPLE_RATE = 16000
 model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("nvidia/stt_en_conformer_transducer_xlarge")
@@ -148,26 +131,19 @@ def process_audio_file(file):
     data = librosa.to_mono(data)
     return data
 
-#def transcribe(audio, state = "", im4 = "", file = ""):
-#def transcribe(audio, state = "", im4 = None, file = None):
-def transcribe(audio, state = ""):    # two parms - had been testing video and file inputs at same time.
-    # Grant additional context
-    # time.sleep(1)
+
+def transcribe(audio, state = ""):   
     if state is None:
         state = ""
     audio_data = process_audio_file(audio)
     with tempfile.TemporaryDirectory() as tmpdir:
-        # Filepath transcribe
         audio_path = os.path.join(tmpdir, f'audio_{uuid.uuid4()}.wav')
         soundfile.write(audio_path, audio_data, SAMPLE_RATE)
         transcriptions = model.transcribe([audio_path])
-       # Direct transcribe
-        # transcriptions = model.transcribe([audio])
-        # if transcriptions form a tuple (from RNNT), extract just "best" hypothesis
         if type(transcriptions) == tuple and len(transcriptions) == 2:
             transcriptions = transcriptions[0]
         transcriptions = transcriptions[0]
-    store_message(transcriptions, state) # Save to dataset
+#    store_message(transcriptions, state) # Save to dataset - uncomment to store into a dataset - hint you will need your HF_TOKEN
     state = state + transcriptions + " "
     return state, state
 
@@ -176,16 +152,10 @@ iface = gr.Interface(
     inputs=[
         gr.Audio(source="microphone", type='filepath', streaming=True),
         "state",
-        #gr.Image(label="Webcam", source="webcam"),
-        #gr.File(label="File"),
     ],
     outputs=[
         "textbox",
         "state",
-        #gr.HighlightedText(label="HighlightedText", color_map={"punc": "pink", "test 0": "blue"}),
-        #gr.HighlightedText(label="HighlightedText", show_legend=True),
-        #gr.JSON(label="JSON"),
-        #gr.HTML(label="HTML"),
     ],
     layout="horizontal",
     theme="huggingface",
@@ -193,6 +163,6 @@ iface = gr.Interface(
     description=f"Live Automatic Speech Recognition (ASR) with Memory💾 Dataset.",
     allow_flagging='never',
     live=True,    
-    article=f"Result Output Saved to Memory💾 Dataset: [{DATASET_REPO_URL}]({DATASET_REPO_URL})"
+#    article=f"Result Output Saved to Memory💾 Dataset: [{DATASET_REPO_URL}]({DATASET_REPO_URL})"
 )
 iface.launch()