Update app.py

app.py

@@ -1,104 +1,101 @@
-# Install necessary libraries
-!pip install transformers gradio librosa
-
-import gradio as gr
-from transformers import pipeline
-
-# Load models
-# Sentiment Analysis
-classifier_sentiment = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
-
-def analyze_sentiment(text):
-    result = classifier_sentiment(text)[0]
-    label = result['label']
-    score = result['score']
-    return f"Label: {label}, Score: {score:.2f}"
-
-# Translation
-translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr")
-
-def translate_text(text):
-    result = translator(text)[0]
-    translated_text = result["translation_text"]
-    return translated_text
-
-# Image Classification
-classifier_image = pipeline("image-classification", model="google/mobilenet_v2_1.0_224")
-
-def classify_image(image):
-    results = classifier_image(image)
-    output = ""
-    for result in results:
-        output += f"{result['label']}: {result['score']:.2f}\n"
-    return output
-
-# Speech to Text
-speech_to_text = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")
-
-def transcribe_audio(audio):
-    text = speech_to_text(audio)["text"]
-    return text
-
-# Text Summarization
-summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
-
-def summarize_text(text):
-    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)[0]["summary_text"]
-    return summary
-
-# Define custom CSS styles
-css = """
-<style>
-    body {
-        background-color: #e9ecef;
-        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-    }
-    .gradio-container {
-        border-radius: 15px;
-        box-shadow: 0 4px 20px rgba(0, 0, 0, 0.15);
-        padding: 20px;
-        background-color: #74748a;
-        max-width: 800px;
-        margin: auto;
-    }
-    h1 {
-        color: black;
-    }
-    
-</style>
-"""
-
-with gr.Blocks(css=css) as demo:
-    gr.Markdown("<h1 style='text-align: center;'>Multi-functional AI Demo</h1>")
-
-    with gr.Tab("Sentiment Analysis😣"):
-        text_input = gr.Textbox(placeholder="Enter text here...")
-        text_output = gr.Textbox()
-        sentiment_button = gr.Button("Analyze")
-        sentiment_button.click(analyze_sentiment, inputs=text_input, outputs=text_output)
-
-    with gr.Tab("Translation📚"):
-        text_input_trans = gr.Textbox(placeholder="Enter English text here...")
-        text_output_trans = gr.Textbox()
-        trans_button = gr.Button("Translate")
-        trans_button.click(translate_text, inputs=text_input_trans, outputs=text_output_trans)
-
-    with gr.Tab("Image Classification🔮"):
-        image_input = gr.Image(type="pil")
-        image_output = gr.Textbox()
-        image_button = gr.Button("Classify")
-        image_button.click(classify_image, inputs=image_input, outputs=image_output)
-
-    with gr.Tab("Speech to Text🔊"):
-        audio_input = gr.Audio(sources=["microphone"], type="filepath")
-        audio_output = gr.Textbox()
-        audio_button = gr.Button("Transcribe")
-        audio_button.click(transcribe_audio, inputs=audio_input, outputs=audio_output)
-    
-    with gr.Tab("Text Summarization📑"):
-        text_input_summ = gr.Textbox(placeholder="Enter text here...")
-        text_output_summ = gr.Textbox()
-        summ_button = gr.Button("Summarize")
-        summ_button.click(summarize_text, inputs=text_input_summ, outputs=text_output_summ)
-
+import gradio as gr
+from transformers import pipeline
+
+# Load models
+# Sentiment Analysis
+classifier_sentiment = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+
+def analyze_sentiment(text):
+    result = classifier_sentiment(text)[0]
+    label = result['label']
+    score = result['score']
+    return f"Label: {label}, Score: {score:.2f}"
+
+# Translation
+translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr")
+
+def translate_text(text):
+    result = translator(text)[0]
+    translated_text = result["translation_text"]
+    return translated_text
+
+# Image Classification
+classifier_image = pipeline("image-classification", model="google/mobilenet_v2_1.0_224")
+
+def classify_image(image):
+    results = classifier_image(image)
+    output = ""
+    for result in results:
+        output += f"{result['label']}: {result['score']:.2f}\n"
+    return output
+
+# Speech to Text
+speech_to_text = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")
+
+def transcribe_audio(audio):
+    text = speech_to_text(audio)["text"]
+    return text
+
+# Text Summarization
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+def summarize_text(text):
+    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)[0]["summary_text"]
+    return summary
+
+# Define custom CSS styles
+css = """
+<style>
+    body {
+        background-color: #e9ecef;
+        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+    }
+    .gradio-container {
+        border-radius: 15px;
+        box-shadow: 0 4px 20px rgba(0, 0, 0, 0.15);
+        padding: 20px;
+        background-color: #74748a;
+        max-width: 800px;
+        margin: auto;
+    }
+    h1 {
+        color: black;
+    }
+    
+</style>
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown("<h1 style='text-align: center;'>Multi-functional AI Demo</h1>")
+
+    with gr.Tab("Sentiment Analysis😣"):
+        text_input = gr.Textbox(placeholder="Enter text here...")
+        text_output = gr.Textbox()
+        sentiment_button = gr.Button("Analyze")
+        sentiment_button.click(analyze_sentiment, inputs=text_input, outputs=text_output)
+
+    with gr.Tab("Translation📚"):
+        text_input_trans = gr.Textbox(placeholder="Enter English text here...")
+        text_output_trans = gr.Textbox()
+        trans_button = gr.Button("Translate")
+        trans_button.click(translate_text, inputs=text_input_trans, outputs=text_output_trans)
+
+    with gr.Tab("Image Classification🔮"):
+        image_input = gr.Image(type="pil")
+        image_output = gr.Textbox()
+        image_button = gr.Button("Classify")
+        image_button.click(classify_image, inputs=image_input, outputs=image_output)
+
+    with gr.Tab("Speech to Text🔊"):
+        audio_input = gr.Audio(sources=["microphone"], type="filepath")
+        audio_output = gr.Textbox()
+        audio_button = gr.Button("Transcribe")
+        audio_button.click(transcribe_audio, inputs=audio_input, outputs=audio_output)
+    
+    with gr.Tab("Text Summarization📑"):
+        text_input_summ = gr.Textbox(placeholder="Enter text here...")
+        text_output_summ = gr.Textbox()
+        summ_button = gr.Button("Summarize")
+        summ_button.click(summarize_text, inputs=text_input_summ, outputs=text_output_summ)
+
 demo.launch()