asd

app.py

@@ -1,9 +1,192 @@
+# # import os
+# # import json
+# # import gradio as gr
+# # import spaces
+# # import torch
+# # from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification
+# # from sentence_splitter import SentenceSplitter
+# # from itertools import product
+
+# # # Get the Hugging Face token from environment variable
+# # hf_token = os.getenv('HF_TOKEN')
+
+# # cuda_available = torch.cuda.is_available()
+# # device = torch.device("cpu" if cuda_available else "cpu")
+# # print(f"Using device: {device}")
+
+# # # Initialize paraphraser model and tokenizer
+# # paraphraser_model_name = "NoaiGPT/777"
+# # paraphraser_tokenizer = AutoTokenizer.from_pretrained(paraphraser_model_name, use_auth_token=hf_token)
+# # paraphraser_model = AutoModelForSeq2SeqLM.from_pretrained(paraphraser_model_name, use_auth_token=hf_token).to(device)
+
+# # # Initialize classifier model and tokenizer
+# # classifier_model_name = "andreas122001/roberta-mixed-detector"
+# # classifier_tokenizer = AutoTokenizer.from_pretrained(classifier_model_name)
+# # classifier_model = AutoModelForSequenceClassification.from_pretrained(classifier_model_name).to(device)
+
+# # # Initialize sentence splitter
+# # splitter = SentenceSplitter(language='en')
+
+# # def classify_text(text):
+# #     inputs = classifier_tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(device)
+# #     with torch.no_grad():
+# #         outputs = classifier_model(**inputs)
+# #     probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+# #     predicted_class = torch.argmax(probabilities, dim=-1).item()
+# #     main_label = classifier_model.config.id2label[predicted_class]
+# #     main_score = probabilities[0][predicted_class].item()
+# #     return main_label, main_score
+
+# # # @spaces.GPU
+# # def generate_paraphrases(text, setting, output_format):
+# #     sentences = splitter.split(text)
+# #     all_sentence_paraphrases = []
+    
+# #     if setting == 1:
+# #         num_return_sequences = 5
+# #         repetition_penalty = 1.1
+# #         no_repeat_ngram_size = 2
+# #         temperature = 1.0
+# #         max_length = 128
+# #     elif setting == 2:
+# #         num_return_sequences = 10
+# #         repetition_penalty = 1.2
+# #         no_repeat_ngram_size = 3
+# #         temperature = 1.2
+# #         max_length = 192
+# #     elif setting == 3:
+# #         num_return_sequences = 15
+# #         repetition_penalty = 1.3
+# #         no_repeat_ngram_size = 4
+# #         temperature = 1.4
+# #         max_length = 256
+# #     elif setting == 4:
+# #         num_return_sequences = 20
+# #         repetition_penalty = 1.4
+# #         no_repeat_ngram_size = 5
+# #         temperature = 1.6
+# #         max_length = 320
+# #     else:
+# #         num_return_sequences = 25
+# #         repetition_penalty = 1.5
+# #         no_repeat_ngram_size = 6
+# #         temperature = 1.8
+# #         max_length = 384
+    
+# #     top_k = 50
+# #     top_p = 0.95
+# #     length_penalty = 1.0
+    
+# #     formatted_output = "Original text:\n" + text + "\n\n"
+# #     formatted_output += "Paraphrased versions:\n"
+    
+# #     json_output = {
+# #         "original_text": text,
+# #         "paraphrased_versions": [],
+# #         "combined_versions": [],
+# #         "human_like_versions": []
+# #     }
+    
+# #     for i, sentence in enumerate(sentences):
+# #         inputs = paraphraser_tokenizer(f'paraphraser: {sentence}', return_tensors="pt", padding="longest", truncation=True, max_length=max_length).to(device)
+        
+# #         # Generate paraphrases using the specified parameters
+# #         outputs = paraphraser_model.generate(
+# #             inputs.input_ids,
+# #             attention_mask=inputs.attention_mask,
+# #             num_return_sequences=num_return_sequences,
+# #             repetition_penalty=repetition_penalty,
+# #             no_repeat_ngram_size=no_repeat_ngram_size,
+# #             temperature=temperature,
+# #             max_length=max_length,
+# #             top_k=top_k,
+# #             top_p=top_p,
+# #             do_sample=True,
+# #             early_stopping=False,
+# #             length_penalty=length_penalty
+# #         )
+        
+# #         paraphrases = paraphraser_tokenizer.batch_decode(outputs, skip_special_tokens=True)
+        
+# #         formatted_output += f"Original sentence {i+1}: {sentence}\n"
+# #         for j, paraphrase in enumerate(paraphrases, 1):
+# #             formatted_output += f"  Paraphrase {j}: {paraphrase}\n"
+        
+# #         json_output["paraphrased_versions"].append({
+# #             f"original_sentence_{i+1}": sentence,
+# #             "paraphrases": paraphrases
+# #         })
+        
+# #         all_sentence_paraphrases.append(paraphrases)
+# #         formatted_output += "\n"
+    
+# #     all_combinations = list(product(*all_sentence_paraphrases))
+    
+# #     formatted_output += "\nCombined paraphrased versions:\n"
+# #     combined_versions = []
+# #     for i, combination in enumerate(all_combinations[:50], 1):  # Limit to 50 combinations
+# #         combined_paraphrase = " ".join(combination)
+# #         combined_versions.append(combined_paraphrase)
+    
+# #     json_output["combined_versions"] = combined_versions
+    
+# #     # Classify combined versions
+# #     human_versions = []
+# #     for i, version in enumerate(combined_versions, 1):
+# #         label, score = classify_text(version)
+# #         formatted_output += f"Version {i}:\n{version}\n"
+# #         formatted_output += f"Classification: {label} (confidence: {score:.2%})\n\n"
+# #         if label == "human-produced" or (label == "machine-generated" and score < 0.98):
+# #             human_versions.append((version, label, score))
+    
+# #     formatted_output += "\nHuman-like or Less Confident Machine-generated versions:\n"
+# #     for i, (version, label, score) in enumerate(human_versions, 1):
+# #         formatted_output += f"Version {i}:\n{version}\n"
+# #         formatted_output += f"Classification: {label} (confidence: {score:.2%})\n\n"
+    
+# #     json_output["human_like_versions"] = [
+# #         {"version": version, "label": label, "confidence_score": score}
+# #         for version, label, score in human_versions
+# #     ]
+    
+# #     # If no human-like versions, include the top 5 least confident machine-generated versions
+# #     if not human_versions:
+# #         human_versions = sorted([(v, l, s) for v, l, s in zip(combined_versions, [classify_text(v)[0] for v in combined_versions], [classify_text(v)[1] for v in combined_versions])], key=lambda x: x[2])[:5]
+# #         formatted_output += "\nNo human-like versions found. Showing top 5 least confident machine-generated versions:\n"
+# #         for i, (version, label, score) in enumerate(human_versions, 1):
+# #             formatted_output += f"Version {i}:\n{version}\n"
+# #             formatted_output += f"Classification: {label} (confidence: {score:.2%})\n\n"
+    
+# #     if output_format == "text":
+# #         return formatted_output, "\n\n".join([v[0] for v in human_versions])
+# #     else:
+# #         return json.dumps(json_output, indent=2), "\n\n".join([v[0] for v in human_versions])
+
+# # # Define the Gradio interface
+# # iface = gr.Interface(
+# #     fn=generate_paraphrases,
+# #     inputs=[
+# #         gr.Textbox(lines=5, label="Input Text"),
+# #         gr.Slider(minimum=1, maximum=5, step=1, label="Readability to Human-like Setting"),
+# #         gr.Radio(["text", "json"], label="Output Format")
+# #     ],
+# #     outputs=[
+# #         gr.Textbox(lines=20, label="Detailed Paraphrases and Classifications"),
+# #         gr.Textbox(lines=10, label="Human-like or Less Confident Machine-generated Paraphrases")
+# #     ],
+# #     title="Advanced Diverse Paraphraser with Human-like Filter",
+# #     description="Enter a text, select a setting from readable to human-like, and choose the output format to generate diverse paraphrased versions. Combined versions are classified, and those detected as human-produced or less confidently machine-generated are presented in the final output."
+# # )
+
+# # # Launch the interface
+# # iface.launch()
+
 # import os
 # import json
 # import gradio as gr
 # import spaces
 # import torch
-# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification
+# from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification
 # from sentence_splitter import SentenceSplitter
 # from itertools import product
 
@@ -11,13 +194,14 @@
 # hf_token = os.getenv('HF_TOKEN')
 
 # cuda_available = torch.cuda.is_available()
-# device = torch.device("cpu" if cuda_available else "cpu")
+# device = torch.device("cuda" if cuda_available else "cpu")
 # print(f"Using device: {device}")
 
 # # Initialize paraphraser model and tokenizer
-# paraphraser_model_name = "NoaiGPT/777"
-# paraphraser_tokenizer = AutoTokenizer.from_pretrained(paraphraser_model_name, use_auth_token=hf_token)
-# paraphraser_model = AutoModelForSeq2SeqLM.from_pretrained(paraphraser_model_name, use_auth_token=hf_token).to(device)
+# paraphraser_model_name = "sharad/ParaphraseGPT"
+# paraphraser_tokenizer = AutoTokenizer.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base")
+# paraphraser_model = AutoModelForSeq2SeqLM.from_pretrained(paraphraser_model_name).to(device)
+# paraphrase_pipeline = pipeline("text2text-generation", model=paraphraser_model, tokenizer=paraphraser_tokenizer, device=0 if cuda_available else -1)
 
 # # Initialize classifier model and tokenizer
 # classifier_model_name = "andreas122001/roberta-mixed-detector"
@@ -37,7 +221,7 @@
 #     main_score = probabilities[0][predicted_class].item()
 #     return main_label, main_score
 
-# # @spaces.GPU
+# @spaces.GPU
 # def generate_paraphrases(text, setting, output_format):
 #     sentences = splitter.split(text)
 #     all_sentence_paraphrases = []
@@ -46,31 +230,31 @@
 #         num_return_sequences = 5
 #         repetition_penalty = 1.1
 #         no_repeat_ngram_size = 2
-#         temperature = 1.0
+#         temperature = 0.9
 #         max_length = 128
 #     elif setting == 2:
-#         num_return_sequences = 10
+#         num_return_sequences = 5
 #         repetition_penalty = 1.2
 #         no_repeat_ngram_size = 3
-#         temperature = 1.2
+#         temperature = 0.95
 #         max_length = 192
 #     elif setting == 3:
-#         num_return_sequences = 15
+#         num_return_sequences = 5
 #         repetition_penalty = 1.3
 #         no_repeat_ngram_size = 4
-#         temperature = 1.4
+#         temperature = 1.0
 #         max_length = 256
 #     elif setting == 4:
-#         num_return_sequences = 20
+#         num_return_sequences = 5
 #         repetition_penalty = 1.4
 #         no_repeat_ngram_size = 5
-#         temperature = 1.6
+#         temperature = 1.05
 #         max_length = 320
 #     else:
-#         num_return_sequences = 25
+#         num_return_sequences = 5
 #         repetition_penalty = 1.5
 #         no_repeat_ngram_size = 6
-#         temperature = 1.8
+#         temperature = 1.1
 #         max_length = 384
     
 #     top_k = 50
@@ -88,36 +272,30 @@
 #     }
     
 #     for i, sentence in enumerate(sentences):
-#         inputs = paraphraser_tokenizer(f'paraphraser: {sentence}', return_tensors="pt", padding="longest", truncation=True, max_length=max_length).to(device)
-        
-#         # Generate paraphrases using the specified parameters
-#         outputs = paraphraser_model.generate(
-#             inputs.input_ids,
-#             attention_mask=inputs.attention_mask,
+#         paraphrases = paraphrase_pipeline(
+#             sentence,
 #             num_return_sequences=num_return_sequences,
-#             repetition_penalty=repetition_penalty,
-#             no_repeat_ngram_size=no_repeat_ngram_size,
-#             temperature=temperature,
-#             max_length=max_length,
+#             do_sample=True,
 #             top_k=top_k,
 #             top_p=top_p,
-#             do_sample=True,
-#             early_stopping=False,
-#             length_penalty=length_penalty
+#             temperature=temperature,
+#             no_repeat_ngram_size=no_repeat_ngram_size,
+#             repetition_penalty=repetition_penalty,
+#             max_length=max_length
 #         )
         
-#         paraphrases = paraphraser_tokenizer.batch_decode(outputs, skip_special_tokens=True)
+#         paraphrases_texts = [p['generated_text'] for p in paraphrases]
         
 #         formatted_output += f"Original sentence {i+1}: {sentence}\n"
-#         for j, paraphrase in enumerate(paraphrases, 1):
+#         for j, paraphrase in enumerate(paraphrases_texts, 1):
 #             formatted_output += f"  Paraphrase {j}: {paraphrase}\n"
         
 #         json_output["paraphrased_versions"].append({
 #             f"original_sentence_{i+1}": sentence,
-#             "paraphrases": paraphrases
+#             "paraphrases": paraphrases_texts
 #         })
         
-#         all_sentence_paraphrases.append(paraphrases)
+#         all_sentence_paraphrases.append(paraphrases_texts)
 #         formatted_output += "\n"
     
 #     all_combinations = list(product(*all_sentence_paraphrases))
@@ -186,7 +364,7 @@ import json
 import gradio as gr
 import spaces
 import torch
-from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification
 from sentence_splitter import SentenceSplitter
 from itertools import product
 
@@ -198,10 +376,9 @@ device = torch.device("cuda" if cuda_available else "cpu")
 print(f"Using device: {device}")
 
 # Initialize paraphraser model and tokenizer
-paraphraser_model_name = "sharad/ParaphraseGPT"
-paraphraser_tokenizer = AutoTokenizer.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base")
+paraphraser_model_name = "ramsrigouthamg/t5-large-paraphraser-diverse-high-quality"
+paraphraser_tokenizer = AutoTokenizer.from_pretrained(paraphraser_model_name)
 paraphraser_model = AutoModelForSeq2SeqLM.from_pretrained(paraphraser_model_name).to(device)
-paraphrase_pipeline = pipeline("text2text-generation", model=paraphraser_model, tokenizer=paraphraser_tokenizer, device=0 if cuda_available else -1)
 
 # Initialize classifier model and tokenizer
 classifier_model_name = "andreas122001/roberta-mixed-detector"
@@ -227,40 +404,26 @@ def generate_paraphrases(text, setting, output_format):
     all_sentence_paraphrases = []
     
     if setting == 1:
-        num_return_sequences = 5
-        repetition_penalty = 1.1
-        no_repeat_ngram_size = 2
-        temperature = 0.9
+        num_return_sequences = 3
+        num_beams = 5
         max_length = 128
     elif setting == 2:
-        num_return_sequences = 5
-        repetition_penalty = 1.2
-        no_repeat_ngram_size = 3
-        temperature = 0.95
+        num_return_sequences = 3
+        num_beams = 7
         max_length = 192
     elif setting == 3:
-        num_return_sequences = 5
-        repetition_penalty = 1.3
-        no_repeat_ngram_size = 4
-        temperature = 1.0
+        num_return_sequences = 3
+        num_beams = 9
         max_length = 256
     elif setting == 4:
-        num_return_sequences = 5
-        repetition_penalty = 1.4
-        no_repeat_ngram_size = 5
-        temperature = 1.05
+        num_return_sequences = 3
+        num_beams = 11
         max_length = 320
     else:
-        num_return_sequences = 5
-        repetition_penalty = 1.5
-        no_repeat_ngram_size = 6
-        temperature = 1.1
+        num_return_sequences = 3
+        num_beams = 15
         max_length = 384
     
-    top_k = 50
-    top_p = 0.95
-    length_penalty = 1.0
-    
     formatted_output = "Original text:\n" + text + "\n\n"
     formatted_output += "Paraphrased versions:\n"
     
@@ -272,19 +435,21 @@ def generate_paraphrases(text, setting, output_format):
     }
     
     for i, sentence in enumerate(sentences):
-        paraphrases = paraphrase_pipeline(
-            sentence,
-            num_return_sequences=num_return_sequences,
-            do_sample=True,
-            top_k=top_k,
-            top_p=top_p,
-            temperature=temperature,
-            no_repeat_ngram_size=no_repeat_ngram_size,
-            repetition_penalty=repetition_penalty,
-            max_length=max_length
+        text = "paraphrase: " + sentence + " </s>"
+        encoding = paraphraser_tokenizer.encode_plus(text, max_length=max_length, padding=True, return_tensors="pt")
+        input_ids, attention_mask = encoding["input_ids"].to(device), encoding["attention_mask"].to(device)
+        
+        paraphraser_model.eval()
+        beam_outputs = paraphraser_model.generate(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            max_length=max_length,
+            early_stopping=True,
+            num_beams=num_beams,
+            num_return_sequences=num_return_sequences
         )
         
-        paraphrases_texts = [p['generated_text'] for p in paraphrases]
+        paraphrases_texts = [paraphraser_tokenizer.decode(beam_output, skip_special_tokens=True, clean_up_tokenization_spaces=True) for beam_output in beam_outputs]
         
         formatted_output += f"Original sentence {i+1}: {sentence}\n"
         for j, paraphrase in enumerate(paraphrases_texts, 1):
@@ -314,7 +479,7 @@ def generate_paraphrases(text, setting, output_format):
         label, score = classify_text(version)
         formatted_output += f"Version {i}:\n{version}\n"
         formatted_output += f"Classification: {label} (confidence: {score:.2%})\n\n"
-        if label == "human-produced" or (label == "machine-generated" and score < 0.98):
+        if label == "human-produced" or (label == "machine-generated" and score < 0.90):  # Adjusted threshold
             human_versions.append((version, label, score))
     
     formatted_output += "\nHuman-like or Less Confident Machine-generated versions:\n"