Update app.py
Browse files
app.py
CHANGED
@@ -23,7 +23,9 @@ def query(image):
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binary_data = buffer.tobytes()
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response = requests.post(API_URL, headers=headers, data=binary_data)
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-
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def text_extraction(image):
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global text_content
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@@ -110,7 +112,7 @@ def inference(image, text, audio, sentiment_option):
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image_sentiment_output = display_sentiment_results(image_sentiment_results, sentiment_option)
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text_sentiment_output = display_sentiment_results(text_sentiment_results, sentiment_option)
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return extracted_image, extracted_facial_data, extracted_text, image_sentiment_output, text_sentiment_output, lang.upper(), result.text,
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title = """<h1 align="center">Cross Model Machine Learning (Sentiment Analysis)</h1>"""
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image_path = "thmbnail.png"
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@@ -166,19 +168,19 @@ with block:
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gr.HTML(description)
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with gr.Blocks():
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with gr.
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with gr.
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image = gr.Image()
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image_output = gr.Image()
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text_output = gr.Textbox(label="Text Content")
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text_sentiment = gr.Textbox(label="Text Sentiment")
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facial_output = gr.
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with gr.Column():
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gr.Textbox(label="Text Content")
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output_text_sentiment = gr.Textbox("Text Sentiment")
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with gr.Column():
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audio = gr.Audio(label="Input Audio", show_label=False, type="filepath")
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binary_data = buffer.tobytes()
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response = requests.post(API_URL, headers=headers, data=binary_data)
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result = {item['label']: item['score'] for item in response.json()}
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return result
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def text_extraction(image):
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global text_content
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image_sentiment_output = display_sentiment_results(image_sentiment_results, sentiment_option)
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text_sentiment_output = display_sentiment_results(text_sentiment_results, sentiment_option)
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return extracted_image, extracted_facial_data, extracted_text, image_sentiment_output, text_sentiment_output, lang.upper(), result.text, audio_sentiment_output
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title = """<h1 align="center">Cross Model Machine Learning (Sentiment Analysis)</h1>"""
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image_path = "thmbnail.png"
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gr.HTML(description)
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with gr.Blocks():
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with gr.Column():
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with gr.Row():
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image = gr.Image()
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image_output = gr.Image()
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text_output = gr.Textbox(label="Text Content")
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text_sentiment = gr.Textbox(label="Text Sentiment")
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facial_output = gr.Label(label='Facial Data', container=True, scale=2)
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with gr.Column():
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gr.Textbox(label="Text Content")
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output_text_sentiment = gr.Textbox(label="Text Sentiment")
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with gr.Column():
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audio = gr.Audio(label="Input Audio", show_label=False, type="filepath")
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