Update app.py

app.py

@@ -1,54 +1,56 @@
-#from transformers import pipeline
-#import streamlit as st
-
-#pipe = pipeline('sentiment-analysis')
-#text = st.text_area('Enter some text here!')
-
-#if text:
-#    out = pipe(text)
-#    st.json(out)
-
-
 from transformers import pipeline
+import streamlit as st
 import torch
 
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
-classifier = pipeline(
-    "audio-classification", model="MIT/ast-finetuned-speech-commands-v2", device=device
-)
-
-from transformers.pipelines.audio_utils import ffmpeg_microphone_live
-
-
-def launch_fn(
-    wake_word="marvin",
-    prob_threshold=0.5,
-    chunk_length_s=2.0,
-    stream_chunk_s=1,
-    debug=False,
-):
-    if wake_word not in classifier.model.config.label2id.keys():
-        raise ValueError(
-            f"Wake word {wake_word} not in set of valid class labels, pick a wake word in the set {classifier.model.config.label2id.keys()}."
-        )
-
-    sampling_rate = classifier.feature_extractor.sampling_rate
-
-    mic = ffmpeg_microphone_live(
-        sampling_rate=sampling_rate,
-        chunk_length_s=chunk_length_s,
-        stream_chunk_s=stream_chunk_s,
-    )
-
-    print("Listening for wake word...")
-    mic_results = classifier(mic)
-    for prediction in mic_results:
-        prediction = prediction[0]
-        if debug:
-            print(prediction)
-        if prediction["label"] == wake_word:
-            if prediction["score"] > prob_threshold:
-                return True
-
-launch_fn(debug=True)
+pipe = pipeline('chat-response', 'liuhaotian/llava-v1.5-13b', device=device )
+text = st.text_area('Enter some text here!')
+
+if text:
+   out = pipe(text)
+   st.json(out)
+
+
+# from transformers import pipeline
+# import torch
+
+# device = "cuda:0" if torch.cuda.is_available() else "cpu"
+# classifier = pipeline(
+#     "audio-classification", model="MIT/ast-finetuned-speech-commands-v2", device=device
+# )
+
+# from transformers.pipelines.audio_utils import ffmpeg_microphone_live
+
+
+# def launch_fn(
+#     wake_word="marvin",
+#     prob_threshold=0.5,
+#     chunk_length_s=2.0,
+#     stream_chunk_s=1,
+#     debug=False,
+# ):
+#     if wake_word not in classifier.model.config.label2id.keys():
+#         raise ValueError(
+#             f"Wake word {wake_word} not in set of valid class labels, pick a wake word in the set {classifier.model.config.label2id.keys()}."
+#         )
+
+#     sampling_rate = classifier.feature_extractor.sampling_rate
+
+#     mic = ffmpeg_microphone_live(
+#         sampling_rate=sampling_rate,
+#         chunk_length_s=chunk_length_s,
+#         stream_chunk_s=stream_chunk_s,
+#     )
+
+#     print("Listening for wake word...")
+#     mic_results = classifier(mic)
+#     for prediction in mic_results:
+#         prediction = prediction[0]
+#         if debug:
+#             print(prediction)
+#         if prediction["label"] == wake_word:
+#             if prediction["score"] > prob_threshold:
+#                 return True
+
+# launch_fn(debug=True)