adding changes

app.py

@@ -1,63 +1,133 @@
+import torch
+from transformers import PegasusForConditionalGeneration, PegasusTokenizer
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    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
-
-        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)",
+
+# Load the tokenizer and model once when the app starts
+model_name = 'tuner007/pegasus_paraphrase'
+torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+# Initialize tokenizer and model
+tokenizer = PegasusTokenizer.from_pretrained(model_name)
+model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
+
+def get_response(input_text, num_return_sequences=1, num_beams=3):
+    """
+    Generate paraphrased text for a given input_text using the Pegasus model.
+    
+    Args:
+        input_text (str): The text to paraphrase.
+        num_return_sequences (int): Number of paraphrased sequences to return.
+        num_beams (int): Number of beams for beam search.
+        
+    Returns:
+        list: A list containing paraphrased text strings.
+    """
+    # Tokenize the input text
+    batch = tokenizer(
+        [input_text],
+        truncation=True,
+        padding='longest',
+        max_length=60,
+        return_tensors="pt"
+    ).to(torch_device)
+    
+    # Generate paraphrased outputs
+    translated = model.generate(
+        **batch,
+        max_length=60,
+        num_beams=num_beams,
+        num_return_sequences=num_return_sequences,
+        temperature=0.7
+    )
+    
+    # Decode the generated tokens
+    tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
+    return tgt_text
+
+def split_text_by_fullstop(text):
+    """
+    Split the input text into sentences based on full stops.
+    
+    Args:
+        text (str): The text to split.
+        
+    Returns:
+        list: A list of sentences.
+    """
+    sentences = [sentence.strip() for sentence in text.split('.') if sentence]
+    return sentences
+
+def process_text_by_fullstop(text, num_return_sequences=1, num_beams=3):
+    """
+    Process the input text by splitting it into sentences and paraphrasing each sentence.
+    
+    Args:
+        text (str): The text to paraphrase.
+        num_return_sequences (int): Number of paraphrased sequences per sentence.
+        num_beams (int): Number of beams for beam search.
+        
+    Returns:
+        str: The paraphrased text.
+    """
+    sentences = split_text_by_fullstop(text)
+    paraphrased_sentences = []
+    
+    for sentence in sentences:
+        # Ensure each sentence ends with a period
+        sentence = sentence + '.' if not sentence.endswith('.') else sentence
+        paraphrases = get_response(sentence, num_return_sequences, num_beams)
+        paraphrased_sentences.extend(paraphrases)
+    
+    # Join all paraphrased sentences into a single string
+    return ' '.join(paraphrased_sentences)
+
+def paraphrase(text, num_beams, num_return_sequences):
+    """
+    Interface function to paraphrase input text based on user parameters.
+    
+    Args:
+        text (str): The input text to paraphrase.
+        num_beams (int): Number of beams for beam search.
+        num_return_sequences (int): Number of paraphrased sequences to return.
+        
+    Returns:
+        str: The paraphrased text.
+    """
+    return process_text_by_fullstop(text, num_return_sequences, num_beams)
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=paraphrase,
+    inputs=[
+        gr.components.Textbox(
+            lines=10, 
+            placeholder="Enter text here...", 
+            label="Input Text"
         ),
+        gr.components.Slider(
+            minimum=1, 
+            maximum=10, 
+            step=1, 
+            value=3, 
+            label="Number of Beams"
+        ),
+        gr.components.Slider(
+            minimum=1, 
+            maximum=5, 
+            step=1, 
+            value=1, 
+            label="Number of Return Sequences"
+        )
     ],
+    outputs=gr.components.Textbox(
+        lines=10, 
+        label="Paraphrased Text"
+    ),
+    title="Text Paraphrasing App",
+    description="Enter your text and adjust the parameters to receive paraphrased versions using the Pegasus model.",
+    allow_flagging="never"
 )
 
-
+# Launch the app
 if __name__ == "__main__":
-    demo.launch()
+    iface.launch()