created app

app.py

@@ -0,0 +1,136 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, T5ForConditionalGeneration, pipeline
+from sentence_transformers import SentenceTransformer, util
+import requests
+import random
+import warnings
+from transformers import logging
+import os
+import tensorflow as tf
+
+# Set environment configurations
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+tf.get_logger().setLevel('ERROR')
+warnings.filterwarnings("ignore")
+logging.set_verbosity_error()
+
+GROQ_API_KEY = "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"
+
+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": 0.0,
+        "max_tokens": 8192
+    }
+    response = requests.post("https://api.groq.com/openai/v1/chat/completions", json=payload, headers=headers)
+    if response.status_code == 200:
+        data = response.json()
+        try:
+            segmented_text = data.get("choices", [{}])[0].get("message", {}).get("content", "")
+            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"
+        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)
+        self.grammar_pipeline = pipeline(
+            "text2text-generation",
+            model="Grammarly/coedit-large",
+            device=0 if self.device == "cuda" else -1
+        )
+        self.similarity_model = SentenceTransformer('paraphrase-MiniLM-L6-v2').to(self.device)
+
+    def enhance_text(self, text, min_similarity=0.8, max_variations=3):
+        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"]
+                enhanced_sentences.append(corrected)
+            else:
+                enhanced_sentences.append(sentence)
+        
+        enhanced_text = ". ".join(sentence.rstrip(".") for sentence in enhanced_sentences) + "."
+        return enhanced_text
+
+def create_interface():
+    enhancer = TextEnhancer()
+    
+    def process_text(text, similarity_threshold):
+        try:
+            return enhancer.enhance_text(
+                text,
+                min_similarity=similarity_threshold / 100
+            )
+        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()