Update app.py

app.py

@@ -1,25 +1,38 @@
 import gradio as gr
 from transformers import T5ForConditionalGeneration, T5Tokenizer
+from fastapi import FastAPI
+import gradio as gr
 
 # Load the model
 model_name = "ramsrigouthamg/t5_paraphraser"
 tokenizer = T5Tokenizer.from_pretrained(model_name)
 model = T5ForConditionalGeneration.from_pretrained(model_name)
 
-def paraphrase_text(text):
+def paraphrase_text(text, creativity_level=3, tone="neutral"):
     if not text.strip():
         return "Please enter some text to paraphrase."
     
+    # Adjust generation parameters based on creativity level
+    num_beams = 3 + creativity_level  # Scale beams with creativity (3-8)
+    temperature = 0.7 + (creativity_level * 0.15)  # Scale temperature (0.7-1.45)
+    
     # Preprocess input
     input_text = "paraphrase: " + text + " </s>"
-    encoding = tokenizer.encode_plus(input_text, max_length=256, padding="max_length", return_tensors="pt", truncation=True)
+    encoding = tokenizer.encode_plus(
+        input_text, 
+        max_length=256, 
+        padding="max_length", 
+        return_tensors="pt", 
+        truncation=True
+    )
     
     # Generate paraphrased output
     generated_ids = model.generate(
         input_ids=encoding["input_ids"],
         attention_mask=encoding["attention_mask"],
         max_length=256,
-        num_beams=5,
+        num_beams=num_beams,
+        temperature=temperature,
         num_return_sequences=1,
         no_repeat_ngram_size=2,
         early_stopping=True
@@ -28,13 +41,29 @@ def paraphrase_text(text):
     paraphrased_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
     return paraphrased_text
 
-# Gradio Interface
-iface = gr.Interface(
+# Create Gradio interface
+demo = gr.Interface(
     fn=paraphrase_text,
-    inputs=gr.Textbox(lines=6, placeholder="Enter your text here...", label="Original Text"),
+    inputs=[
+        gr.Textbox(lines=6, placeholder="Enter your text here...", label="Original Text"),
+        gr.Slider(1, 5, value=3, label="Creativity Level"),
+        gr.Dropdown(["neutral", "formal", "casual", "academic"], value="neutral", label="Output Tone")
+    ],
     outputs=gr.Textbox(label="Paraphrased Text"),
     title="AI Paraphraser",
     description="An advanced paraphrasing tool using the T5 transformer model."
 )
 
-iface.launch()
+# Create FastAPI app and mount Gradio app
+app = FastAPI()
+
+@app.get("/")
+async def root():
+    return {"message": "Paraphraser API is running"}
+
+# Mount Gradio app
+app = gr.mount_gradio_app(app, demo, path="/gradio")
+
+# For direct python execution
+if __name__ == "__main__":
+    demo.launch()