Update app.py

app.py

@@ -1,64 +1,211 @@
 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)",
-        ),
-    ],
-)
+import torch
+from transformers import AutoTokenizer, T5ForConditionalGeneration, pipeline
+from sentence_transformers import SentenceTransformer, util
+import random
+import re
+import nltk
+from nltk.tokenize import sent_tokenize
+import warnings
+from transformers import logging
+import os
+import tensorflow as tf 
+import requests
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  
+os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
+warnings.filterwarnings("ignore", category=FutureWarning)  
+warnings.filterwarnings("ignore", category=UserWarning)    
+warnings.filterwarnings("ignore")   
+logging.set_verbosity_error()
+tf.get_logger().setLevel('ERROR')
+
+nltk.download('punkt')
+GROQ_API_KEY="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"
+
+
+class TextEnhancer:
+    def __init__(self):
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        print(self.device)
+        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") 
+        self.grammar_pipeline = pipeline(
+            "text2text-generation",
+            model="Grammarly/coedit-large",
+            device=0 if self.device == "cuda" else -1
+        )
+        print("grammar check loaded") 
+        self.similarity_model = SentenceTransformer('paraphrase-MiniLM-L6-v2').to(self.device)
+        print("sementics model loaded")
+    def _evaluate_with_groq(self, passage=""):
+        if not passage:
+            raise ValueError("Input passage cannot be empty.")
+
+        # Groq API setup
+        headers = {
+            "Authorization": f"Bearer {GROQ_API_KEY}",  # Replace GROQ_API_KEY with your actual API key.
+            "Content-Type": "application/json"
+        }
+        payload = {
+            "model": "llama3-70b-8192",
+            "messages": [
+                {
+                    "role": "system",
+                    "content": "Paraphrase this sentence to better suit it as an introductory sentence to a student's Statement of purpose. Ensure that the vocabulary and grammar is upto par. ONLY return the raw paraphrased sentence and nothing else.IF IT IS a empty string, return empty string "
+                },
+                {
+                    "role": "user",
+                    "content": f"Here is the passage: {passage}"
+                }
+            ],
+            "temperature": 1.0,
+            "max_tokens": 8192
+        }
+
+        # Sending request to Groq API
+        print("Sending request to Groq API...")
+        response = requests.post("https://api.groq.com/openai/v1/chat/completions", json=payload, headers=headers)
+        print("Response received.")
+
+        # Handling the response
+        if response.status_code == 200:
+            data = response.json()
+            try:
+                segmented_text = data.get("choices", [{}])[0].get("message", {}).get("content", "")
+                print("sentence paraphrase processed successfully.")
+                print(segmented_text)
+                return segmented_text
+            except (IndexError, KeyError) as e:
+                raise ValueError(f"Unexpected response structure from Groq API. Error: {str(e)}")
+        else:
+            raise ValueError(f"Groq API error: {response.status_code}, {response.text}")
+    
+    def _correct_formatting(self, sentence):
+        cleaned_sentence = re.sub(r'([.,!?])\1+', r'\1', sentence)
+        cleaned_sentence = cleaned_sentence.strip()
+        return cleaned_sentence
+    def enhance_text(self, text, min_similarity=0.8, max_variations=3):
+        sent=0
+        enhanced_sentences = []
+        sentences = sent_tokenize(text)
+        total_words = sum(len(sentence.split()) for sentence in sentences)
+        print(f"generated: {total_words}") 
+        for sentence in sentences:
+            if not sentence.strip():
+                continue
+            sent+=1
+                
+            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 sent in {1, len(sentences)} and valid_paraphrases:  
+                gemini_feedback = self._evaluate_with_groq(valid_paraphrases[0])  
+                if gemini_feedback.strip():  
+                    valid_paraphrases[0] = gemini_feedback.strip()  
+                                
+            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)
+                corrected=self._correct_formatting(corrected)
+                enhanced_sentences.append(corrected)
+            else:
+                sentence=self._correct_formatting(sentence)
+                enhanced_sentences.append(sentence)
+                 
+        enhanced_text = ". ".join(sentence.rstrip(".") for sentence in enhanced_sentences) + "."
+        return enhanced_text
+    def _humanize_text(self, text):
+         
+        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])
+
+         
+        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=0.75):
+        try:
+            enhanced = enhancer.enhance_text(
+                text,
+                min_similarity=similarity_threshold / 100,
+                max_variations=10
+            )
+            print("grammar enhanced")
+            return enhanced
+        except Exception as e:
+            return f"Error: {str(e)}"
+    
+    interface = gr.Blocks()
+    with interface:
+        with gr.Row(elem_id="header", variant="panel"):
+            gr.HTML("""
+                <div style="display: flex; align-items: center; justify-content: center; gap: 10px; margin-bottom: 20px;">
+                    <img src="https://raw.githubusercontent.com/juicjaane/blueai/main/logo_2.jpg" style="width: 50px; height: 50px;">
+                    <h1 style="color: gold; font-size: 2em; margin: 0;">Konect U</h1>
+                </div>
+            """)
+
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown("### Your SoP")
+                input_text = gr.Textbox(label="Input", placeholder="Enter SoP to Paraphrase...", lines=10)
+                
+                submit_button = gr.Button("Paraphrase")
+
+            with gr.Column(scale=1):
+                gr.Markdown("### Paraphrased SoP")
+                enhanced_text = gr.Textbox(label="SoP", lines=10)
+
+        submit_button.click(process_text, inputs=[input_text], outputs=enhanced_text)
+
+    return interface
 
 
 if __name__ == "__main__":
-    demo.launch()
+    interface = create_interface()
+    interface.launch(share=True)