Create app.py

main.py

@@ -0,0 +1,79 @@
+import gradio as gr
+from transformers import MarianMTModel, MarianTokenizer, GPT2LMHeadModel, GPT2Tokenizer, AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Translation
+def translate(text, target_language):
+    language_codes = {
+        "Spanish": "es",
+        "French (European)": "fr",
+        "French (Canadian)": "fr",
+        "Italian": "it",
+        "Ukrainian": "uk",
+        "Portuguese (Brazilian)": "pt_BR",
+        "Portuguese (European)": "pt",
+        "Russian": "ru",
+        "Chinese": "zh",
+        "Dutch": "nl",
+        "German": "de",
+        "Arabic": "ar",
+        "Hebrew": "he",
+        "Greek": "el"
+    }
+    
+
+# Text Generation
+def generate_text(prompt):
+    text_gen = pipeline("text-generation", model="gpt2")
+    generated_text = text_gen(prompt, max_length=max_length, do_sample=True)[0]["generated_text"]
+    return generated_text
+
+
+    
+
+# Text Classification
+def classify_text(text):
+    classifier = pipeline("zero-shot-classification")
+    result = classifier(text, labels.split(','))
+    scores = result["scores"]
+    predictions = result["labels"]
+    sorted_predictions = [pred for _, pred in sorted(zip(scores, predictions), reverse=True)]
+    return sorted_predictions
+
+    
+
+# Sentiment Analysis
+def sentiment_analysis(text):
+    model_name = "distilbert-base-uncased-finetuned-sst-2-english"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    inputs = tokenizer(text, return_tensors="pt")
+    outputs = model(**inputs)
+    sentiment_scores = torch.softmax(outputs.logits, dim=1)
+    sentiment = "positive" if sentiment_scores[0, 1] > sentiment_scores[0, 0] else "negative"
+    return sentiment
+
+language_options = [
+    "Spanish", "French (European)", "French (Canadian)", "Italian", "Ukrainian",
+    "Portuguese (Brazilian)", "Portuguese (European)", "Russian", "Chinese",
+    "Dutch", "German", "Arabic", "Hebrew", "Greek"
+]
+
+iface = gr.Interface(
+    [translate, generate_text, classify_text, sentiment_analysis],
+    inputs=[
+        gr.inputs.Textbox(lines=5, label="Enter text to translate:"),
+        gr.inputs.Dropdown(choices=language_options, label="Target Language"),
+        gr.inputs.Textbox(lines=5, label="Enter text for text generation:"),
+        gr.inputs.Textbox(lines=5, label="Enter text for text classification:"),
+        gr.inputs.Textbox(lines=5, label="Enter text for sentiment analysis:"),
+    ],
+    outputs=[
+        gr.outputs.Textbox(label="Translated Text"),
+        gr.outputs.Textbox(label="Generated Text"),
+        gr.outputs.Textbox(label="Classification Result"),
+        gr.outputs.Textbox(label="Sentiment Result"),
+    ],
+)
+
+iface.launch()