asd

app.py

@@ -1,18 +1,182 @@
-import gradio as gr
 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("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)
+
+# 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."
+)
 
-# Get the Hugging Face token from the environment variable
-hf_token = os.getenv("hf_token")
-
-if not hf_token:
-    raise ValueError("No hf_token found in environment variables")
-
-# Load the model using the token
-try:
-    interface = gr.load("models/NoaiGPT/777", api_key=hf_token)
-    # Launch the Gradio interface
-    interface.launch()
-except gradio.exceptions.ModelNotFoundError as e:
-    print(f"Model not found: {e}")
-except Exception as e:
-    print(f"An error occurred: {e}")
+# Launch the interface
+iface.launch()