Create app.py

app.py

@@ -0,0 +1,199 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, T5ForConditionalGeneration, pipeline
+from sentence_transformers import SentenceTransformer, util
+import openai
+import random
+import re
+import requests
+import warnings
+from transformers import logging
+import os
+import tensorflow as tf
+# Set your OpenAI API key
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  # 0 = all messages, 1 = INFO, 2 = WARNING, 3 = ERROR
+tf.get_logger().setLevel('ERROR')
+
+# Suppress Python warnings
+warnings.filterwarnings("ignore", category=FutureWarning)  # Suppress FutureWarnings
+warnings.filterwarnings("ignore", category=UserWarning)    # Suppress UserWarnings
+warnings.filterwarnings("ignore")                          # Suppress all warnings (optional)
+
+# Suppress Hugging Face Transformers warnings
+logging.set_verbosity_error()
+# GPT-powered sentence segmentation function
+def segment_into_sentences_groq(passage):
+    headers = {
+        "Authorization": f"Bearer {GROQ_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    payload = {
+        "model": "llama3-8b-8192",
+        "messages": [
+            {
+            "role": "system",
+            "content": "you are to segment the sentence by adding '1!2@3#' at the end of each sentence. Return only the segmented sentences only return the modified passage and nothing else do not add your responses"
+        },
+            {
+                "role": "user",
+                "content": f"you are to segment the sentence by adding '1!2@3#' at the end of each sentence. Return only the segmented sentences only return the modified passage and nothing else do not add your responses. here is the passage:{passage}"
+            }
+        ],
+        "temperature": 1.0,
+        "max_tokens": 8192
+    }
+    print("response sent")
+    response = requests.post("https://api.groq.com/openai/v1/chat/completions", json=payload, headers=headers)
+    print("response recieved")
+    if response.status_code == 200:
+        data = response.json()
+        try:
+            segmented_text = data.get("choices", [{}])[0].get("message", {}).get("content", "")
+            print("SOP segmented")
+            # Split sentences based on the custom token
+            sentences = segmented_text.split("1!2@3#")
+            return [sentence.strip() for sentence in sentences if sentence.strip()]
+        except (IndexError, KeyError):
+            raise ValueError("Unexpected response structure from Groq API.")
+    else:
+        raise ValueError(f"Groq API error: {response.text}")
+
+
+
+class TextEnhancer:
+    def __init__(self):
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        
+        # Initialize paraphrase model
+        self.paraphrase_tokenizer = AutoTokenizer.from_pretrained("prithivida/parrot_paraphraser_on_T5")
+        self.paraphrase_model = T5ForConditionalGeneration.from_pretrained("prithivida/parrot_paraphraser_on_T5").to(self.device)
+        print("paraphraser loaded")
+        # Initialize grammar correction
+        self.grammar_pipeline = pipeline(
+            "text2text-generation",
+            model="Grammarly/coedit-large",
+            device=0 if self.device == "cuda" else -1
+        )
+        print("grammar check loaded")
+        # Initialize semantic similarity model
+        self.similarity_model = SentenceTransformer('paraphrase-MiniLM-L6-v2').to(self.device)
+        print("sementics model loaded")
+
+    def enhance_text(self, text, min_similarity=0.8, max_variations=3):
+        # Use GPT for sentence segmentation
+        sentences = segment_into_sentences_groq(text)
+
+        enhanced_sentences = []
+        
+        for sentence in sentences:
+            if not sentence.strip():
+                continue
+
+            inputs = self.paraphrase_tokenizer(
+                f"paraphrase: {sentence}",
+                return_tensors="pt",
+                padding=True,
+                max_length=150,
+                truncation=True
+            ).to(self.device)
+        
+            outputs = self.paraphrase_model.generate(
+                **inputs,
+                max_length=len(sentence.split()) + 20,
+                num_return_sequences=max_variations,
+                num_beams=max_variations,
+                temperature=0.7
+            )
+        
+            
+            paraphrases = [
+                self.paraphrase_tokenizer.decode(output, skip_special_tokens=True)
+                for output in outputs
+            ]
+            
+            sentence_embedding = self.similarity_model.encode(sentence)
+            paraphrase_embeddings = self.similarity_model.encode(paraphrases)
+            similarities = util.cos_sim(sentence_embedding, paraphrase_embeddings)
+            
+            valid_paraphrases = [
+                para for para, sim in zip(paraphrases, similarities[0])
+                if sim >= min_similarity
+            ]
+            
+            if valid_paraphrases:
+                corrected = self.grammar_pipeline(
+                    valid_paraphrases[0],
+                    max_length=150,
+                    num_return_sequences=1
+                )[0]["generated_text"]
+
+                corrected = self._humanize_text(corrected)
+                enhanced_sentences.append(corrected)
+            else:
+                enhanced_sentences.append(sentence)
+                print(sentence)
+        
+        enhanced_text = ". ".join(sentence.rstrip(".") for sentence in enhanced_sentences) + "."
+        return enhanced_text
+
+    def _humanize_text(self, text):
+        """
+        Introduce small variations to make text appear more 'human-like'
+        """
+        # Randomly replace contractions in some sentences
+        contractions = {"can't": "cannot", "won't": "will not", "I'm": "I am", "it's": "it is"}
+        words = text.split()
+        text = " ".join([contractions.get(word, word) if random.random() > 0.9 else word for word in words])
+
+        # Add optional comma variations for natural breaks
+        if random.random() > 0.7:
+            text = text.replace(" and ", ", and ")
+        
+        # Minor variations in sentence structure
+        if random.random() > 0.5:
+            text = text.replace(" is ", " happens to be ")
+        
+        return text
+
+
+def create_interface():
+    enhancer = TextEnhancer()
+    
+    def process_text(text, similarity_threshold):
+        try:
+            enhanced = enhancer.enhance_text(
+                text,
+                min_similarity=similarity_threshold / 100
+            )
+            print("grammar enhanced")
+            return enhanced
+        except Exception as e:
+            return f"Error: {str(e)}"
+    
+    interface = gr.Interface(
+        fn=process_text,
+        inputs=[
+            gr.Textbox(
+                label="Input Text",
+                placeholder="Enter text to enhance...",
+                lines=10
+            ),
+            gr.Slider(
+                minimum=50,
+                maximum=100,
+                value=80,
+                label="Minimum Semantic Similarity (%)"
+            )
+        ],
+        outputs=gr.Textbox(label="Enhanced Text", lines=10),
+        title="Text Enhancement System",
+        description="Improve text quality while preserving original meaning"
+    )
+    
+    return interface
+
+
+if __name__ == "__main__":
+    interface = create_interface()
+    interface.launch()