Update app.py

Added extra futures

app.py

@@ -1,6 +1,8 @@
 import gradio as gr
-from transformers import MarianMTModel, MarianTokenizer
+from transformers import MarianMTModel, MarianTokenizer, GPT2LMHeadModel, GPT2Tokenizer, AutoTokenizer, AutoModelForSequenceClassification
+import torch
 
+ # Translation
 def translate(text, target_language):
     language_codes = {
         "Spanish": "es",
@@ -18,32 +20,63 @@ def translate(text, target_language):
         "Hebrew": "he",
         "Greek": "el"
     }
-    target_language_code = language_codes[target_language]
-    model_name = f'helsinki-nlp/opus-mt-en-{target_language_code}'
-    tokenizer = MarianTokenizer.from_pretrained(model_name)
-    model = MarianMTModel.from_pretrained(model_name)
+    
+
+ # 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.generate(**inputs)
-    translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return translation
+    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 = [
+ language_options = [
     "Spanish", "French (European)", "French (Canadian)", "Italian", "Ukrainian",
     "Portuguese (Brazilian)", "Portuguese (European)", "Russian", "Chinese",
     "Dutch", "German", "Arabic", "Hebrew", "Greek"
 ]
 
-iface = gr.Interface(
-    fn=translate,
+ 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"),
     ],
-    outputs=gr.outputs.Textbox(label="Translated Text"),
 )
 
-iface.launch()
-
+ iface.launch()