Update app.py

app.py

@@ -1,64 +1,73 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import torch
+from transformers import pipeline, T5ForConditionalGeneration, T5Tokenizer
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Device configuration
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
+# Load models
+fill_mask = pipeline("fill-mask", model="bert-base-uncased")
+corrector = pipeline("text2text-generation", model="pszemraj/grammar-synthesis-small")
+tokenizer = T5Tokenizer.from_pretrained("EnglishVoice/t5-base-uk-to-us-english")
+model = T5ForConditionalGeneration.from_pretrained("EnglishVoice/t5-base-uk-to-us-english").to(device)
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Fill Mask Function
+def fill_mask_function(text):
+    if "_" not in text:
+        return "Please add an underscore (_) where you want the mask to be predicted."
+    text_with_mask = text.replace("_", "[MASK]")
+    predictions = fill_mask(text_with_mask)
+    filtered = [p for p in predictions if p['token_str'].isalnum()]
+    if not filtered:
+        return "No valid predictions."
+    return "\n".join([f"{p['sequence']} (Score: {p['score']:.4f})" for p in filtered])
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# Grammar Correction Function
+def grammar_correction_function(text):
+    corrected = corrector(text)
+    return corrected[0]['generated_text']
 
-    messages.append({"role": "user", "content": message})
+# UK to US English Conversion
+def uk_to_us_function(text):
+    try:
+        input_text = f"UK to US: {text}"
+        encoding = tokenizer.encode_plus(input_text, return_tensors="pt", padding=True, truncation=True, max_length=512)
+        input_ids = encoding["input_ids"].to(device)
+        attention_mask = encoding["attention_mask"].to(device)
 
-    response = ""
+        output_ids = model.generate(
+            input_ids=input_ids, 
+            attention_mask=attention_mask, 
+            max_length=150, 
+            num_beams=5, 
+            early_stopping=True
+        )
+        result = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+        return result
+    except Exception as e:
+        return f"Error: {str(e)}"
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+# Interface Function
+def interface_function(choice, text):
+    if choice == "Fill Mask":
+        return fill_mask_function(text)
+    elif choice == "Grammar Correction":
+        return grammar_correction_function(text)
+    elif choice == "UK to US English":
+        return uk_to_us_function(text)
 
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+# Gradio Interface
+iface = gr.Interface(
+    fn=interface_function,
+    inputs=[
+        gr.Radio(["Fill Mask", "Grammar Correction", "UK to US English"], label="Choose Functionality"),
+        gr.Textbox(lines=3, placeholder="Enter your text here...", label="Input Text")
     ],
+    outputs=gr.Textbox(label="Output Result"),
+    title="Language Processing App",
+    description="Choose one of the functionalities and provide input text. Supported tasks:\n- Fill Mask: Predict missing words.\n- Grammar Correction: Correct grammatical errors.\n- UK to US English: Convert British English to American English."
 )
 
-
+# Launch Interface
 if __name__ == "__main__":
-    demo.launch()
+    iface.launch()