Update app.py

app.py

@@ -1,230 +1,239 @@
-import gradio as gr # type: ignore
-import pandas as pd
-import re
-import spacy # type: ignore
-from sklearn.cluster import KMeans
-from sklearn.metrics.pairwise import cosine_similarity
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sentence_transformers import SentenceTransformer, util # type: ignore
-from transformers import pipeline, AutoTokenizer
-import textstat # type: ignore
-
-sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
-tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
-
-nlp = spacy.load("en_core_web_sm")
-
-model = SentenceTransformer('all-MiniLM-L6-v2')
-
-weights = {
-    "information_density": 0.2,  
-    "unique_key_points": 0.8,   
-    "strength_word_count": 0.002,  
-    "weakness_word_count": 0.004, 
-    "discussion_word_count": 0.01  
-}
-
-THRESHOLDS = {
-    "normalized_length": (0.15, 0.25),
-    "unique_key_points": (3, 10),
-    "information_density": (0.01, 0.02),
-    "unique_insights_per_word": 0.002,
-    "optimization_score": 0.7,
-    "composite_score": 5,
-    "adjusted_argument_strength": 0.75
-}
-
-def chunk_text(text, max_length):
-    tokens = tokenizer(text, return_tensors="pt", truncation=False)["input_ids"].squeeze(0).tolist()
-    return [tokenizer.decode(tokens[i:i+max_length]) for i in range(0, len(tokens), max_length)]
-
-def analyze_text(texts):
-    results = []
-    for text in texts:
-        chunks = chunk_text(text, max_length=200)
-        chunk_results = sentiment_analyzer(chunks)  
-        overall_sentiment = {
-            "label": "POSITIVE" if sum(1 for res in chunk_results if res["label"] == "POSITIVE") >= len(chunk_results) / 2 else "NEGATIVE",
-            "score": sum(res["score"] for res in chunk_results) / len(chunk_results),
-        }
-        results.append(overall_sentiment)
-    return results
-
-def word_count(text):
-    return len(text.split()) if isinstance(text, str) else 0
-
-def count_citations(text):
-    doc = nlp(text)
-    return sum(1 for ent in doc.ents if ent.label_ in ['WORK_OF_ART', 'ORG', 'GPE'])
-
-def calculate_unique_insights_per_word(text):
-    sentences = text.split('.')  
-    tfidf = TfidfVectorizer().fit_transform(sentences)
-    similarities = cosine_similarity(tfidf)
-    avg_similarity = (similarities.sum() - len(sentences)) / (len(sentences)**2 - len(sentences)) 
-    return 1 - avg_similarity 
-
-def calculate_unique_key_points_and_density(texts):
-    unique_key_points = []
-    information_density = []
-
-    for text in texts:
-        if not isinstance(text, str) or text.strip() == "":
-            unique_key_points.append(0)
-            information_density.append(0)
-            continue
-
-        doc = nlp(text)
-        sentences = [sent.text for sent in doc.sents]
-
-        embeddings = model.encode(sentences)
-
-        n_clusters = max(1, len(sentences) // 5)  
-        kmeans = KMeans(n_clusters=n_clusters, random_state=42)
-        kmeans.fit(embeddings)
-
-        cluster_centers = kmeans.cluster_centers_
-        unique_points_count = len(cluster_centers)
-
-        word_count = len(text.split())
-        density = unique_points_count / word_count if word_count > 0 else 0
-
-        unique_key_points.append(unique_points_count)
-        information_density.append(density)
-
-    return unique_key_points, information_density
-
-def segment_comments(comments):
-    if comments == "N/A":
-        return {"strengths": "", "weaknesses": "", "general_discussion": ""}
-    
-    strengths = re.search(r"- Strengths:\n([\s\S]*?)(\n- Weaknesses:|\Z)", comments)
-    weaknesses = re.search(r"- Weaknesses:\n([\s\S]*?)(\n- General Discussion:|\Z)", comments)
-    general_discussion = re.search(r"- General Discussion:\n([\s\S]*?)\Z", comments)
-    
-    return {
-        "strengths": strengths.group(1).strip() if strengths else "",
-        "weaknesses": weaknesses.group(1).strip() if weaknesses else "",
-        "general_discussion": general_discussion.group(1).strip() if general_discussion else ""
-    }
-
-def preprocess(comment, abstract):
-    df = pd.DataFrame({"comments": [comment]})
-    abstracts = pd.DataFrame({"abstract": [abstract]})
-
-    segmented_reviews = df["comments"].apply(segment_comments)
-    df["strengths"] = segmented_reviews.apply(lambda x: x["strengths"])
-    df["weaknesses"] = segmented_reviews.apply(lambda x: x["weaknesses"])
-    df["general_discussion"] = segmented_reviews.apply(lambda x: x["general_discussion"])
-
-    comments_embeddings = model.encode(df['comments'].tolist(), convert_to_tensor=True)
-    abstract_embeddings = model.encode(abstracts["abstract"].tolist(), convert_to_tensor=True) 
-    df['content_relevance'] = util.cos_sim(comments_embeddings, abstract_embeddings).diagonal()
-
-    df['evidence_support'] = df['comments'].apply(count_citations)
-
-    df['strengths'] = df['strengths'].fillna('').astype(str)
-    texts = df['strengths'].tolist()  
-    results = analyze_text(texts)  
-    df['strength_argument_score'] = [result['score'] for result in results]
-
-    df['weaknesses'] = df['weaknesses'].fillna('').astype(str)
-    texts = df['weaknesses'].tolist()  
-    results = analyze_text(texts)  
-    df['weakness_argument_score'] = [result['score'] for result in results]
-
-    df['argument_strength'] = (df['strength_argument_score'] + df['weakness_argument_score']) / 2
-
-    df['readability_index'] = df['comments'].apply(textstat.flesch_reading_ease)
-    df['sentence_complexity'] = df['comments'].apply(textstat.sentence_count)
-    df['technical_depth'] = df['readability_index'] / df['sentence_complexity']
-
-    df['total_word_count'] = df['comments'].apply(word_count)
-    df['strength_word_count'] = df['strengths'].apply(word_count)
-    df['weakness_word_count'] = df['weaknesses'].apply(word_count)
-    df['discussion_word_count'] = df['general_discussion'].apply(word_count)
-
-    average_length = df['total_word_count'].mean()
-    df['normalized_length'] = df['total_word_count'] / average_length
-    df["unique_key_points"], df["information_density"] = calculate_unique_key_points_and_density(df["comments"])
-
-    df['unique_insights_per_word'] = df['comments'].apply(calculate_unique_insights_per_word) / df['total_word_count']
-
-    return df 
-
-def calculate_composite_score(df):
-    df['composite_score'] = (
-        weights['information_density'] * df['information_density'] +
-        weights['unique_key_points'] * df['unique_key_points'] +
-        weights['strength_word_count'] * df['strength_word_count'] +
-        weights['weakness_word_count'] * df['weakness_word_count'] +
-        weights['discussion_word_count'] * df['discussion_word_count']
-    )
-
-    return df
-
-def classify_review_quality(row):
-    if row['composite_score'] > 12: 
-        return 'Excellent Review Quality'
-    elif row['composite_score'] < 3:  
-        return 'Poor Review Quality'
-    else:
-        return 'Moderate Review Quality'
-
-def determine_review_quality(df):
-
-    df['normalized_length'] = df['total_word_count'] / df['total_word_count'].max()
-    df['unique_insights_per_word'] = df['unique_key_points'] / df['normalized_length']
-    df['adjusted_argument_strength'] = df['argument_strength'] / (1 + df['sentence_complexity'])
-
-    df['review_quality'] = df.apply(classify_review_quality, axis=1)
-
-    return df
-
-def heuristic_optimization(row):
-    suggestions = []
-
-    if row["strength_word_count"] > 100 and row["strength_argument_score"] < THRESHOLDS["adjusted_argument_strength"]:
-        suggestions.append("Summarize redundant strengths.")
-    elif row["strength_word_count"] < 50 and row["strength_argument_score"] < THRESHOLDS["adjusted_argument_strength"]:
-        suggestions.append("Add more impactful strengths.")
-
-    if row["weakness_word_count"] > 100 and row["weakness_argument_score"] < THRESHOLDS["adjusted_argument_strength"]:
-        suggestions.append("Remove repetitive criticisms.")
-    elif row["weakness_word_count"] < 50 and row["weakness_argument_score"] < THRESHOLDS["adjusted_argument_strength"]:
-        suggestions.append("Add specific, actionable weaknesses.")
-
-    if row["discussion_word_count"] < 100 and row["information_density"] < THRESHOLDS["information_density"][0]:
-        suggestions.append("Elaborate with new insights or examples.")
-    elif row["discussion_word_count"] > 300 and row["information_density"] > THRESHOLDS["information_density"][1]:
-        suggestions.append("Summarize key discussion points.")
-
-    if row["normalized_length"] < THRESHOLDS["normalized_length"][0]:
-        suggestions.append("Expand sections for better coverage.")
-    elif row["normalized_length"] > THRESHOLDS["normalized_length"][1]:
-        suggestions.append("Condense content to improve readability.")
-
-    if row["unique_key_points"] < THRESHOLDS["unique_key_points"][0]:
-        suggestions.append("Add more unique insights.")
-    elif row["unique_key_points"] > THRESHOLDS["unique_key_points"][1]:
-        suggestions.append("Streamline ideas for clarity.")
-
-    if row["composite_score"] < THRESHOLDS["composite_score"]:
-        suggestions.append("Enhance clarity, evidence, and argumentation.")
-
-    if row["review_quality"] == "Low":
-        suggestions.append("Significant revisions required.")
-    elif row["review_quality"] == "Moderate":
-        suggestions.append("Minor refinements recommended.")
-
-    return suggestions
-
-def pipeline(comment, abstract):
-    df = preprocess(comment, abstract)
-    df = calculate_composite_score(df)
-    df = determine_review_quality(df)
-    df["optimization_suggestions"] = df.apply(heuristic_optimization, axis=1)
-    return df["composite_score"][0], " ".join(df["optimization_suggestions"][0])
-
-demo = gr.Interface(fn=pipeline, inputs=["text", "text"], outputs=["text", "text"])
-demo.launch()
+import gradio as gr # type: ignore
+import pandas as pd
+import re
+import spacy # type: ignore
+from sklearn.cluster import KMeans
+from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sentence_transformers import SentenceTransformer, util # type: ignore
+from transformers import pipeline, AutoTokenizer
+import textstat # type: ignore
+
+sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
+
+nlp = spacy.load("en_core_web_sm")
+
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+weights = {
+    "information_density": 0.2,  
+    "unique_key_points": 0.8,   
+    "strength_word_count": 0.002,  
+    "weakness_word_count": 0.004, 
+    "discussion_word_count": 0.01  
+}
+
+THRESHOLDS = {
+    "normalized_length": (0.15, 0.25),
+    "unique_key_points": (3, 10),
+    "information_density": (0.01, 0.02),
+    "unique_insights_per_word": 0.002,
+    "optimization_score": 0.7,
+    "composite_score": 5,
+    "adjusted_argument_strength": 0.75
+}
+
+def chunk_text(text, max_length):
+    tokens = tokenizer(text, return_tensors="pt", truncation=False)["input_ids"].squeeze(0).tolist()
+    return [tokenizer.decode(tokens[i:i+max_length]) for i in range(0, len(tokens), max_length)]
+
+def analyze_text(texts):
+    results = []
+    for text in texts:
+        chunks = chunk_text(text, max_length=200)
+        chunk_results = sentiment_analyzer(chunks)  
+        overall_sentiment = {
+            "label": "POSITIVE" if sum(1 for res in chunk_results if res["label"] == "POSITIVE") >= len(chunk_results) / 2 else "NEGATIVE",
+            "score": sum(res["score"] for res in chunk_results) / len(chunk_results),
+        }
+        results.append(overall_sentiment)
+    return results
+
+def word_count(text):
+    return len(text.split()) if isinstance(text, str) else 0
+
+def count_citations(text):
+    doc = nlp(text)
+    return sum(1 for ent in doc.ents if ent.label_ in ['WORK_OF_ART', 'ORG', 'GPE'])
+
+def calculate_unique_insights_per_word(text):
+    sentences = text.split('.')  
+    tfidf = TfidfVectorizer().fit_transform(sentences)
+    similarities = cosine_similarity(tfidf)
+    avg_similarity = (similarities.sum() - len(sentences)) / (len(sentences)**2 - len(sentences)) 
+    return 1 - avg_similarity 
+
+def calculate_unique_key_points_and_density(texts):
+    unique_key_points = []
+    information_density = []
+
+    for text in texts:
+        if not isinstance(text, str) or text.strip() == "":
+            unique_key_points.append(0)
+            information_density.append(0)
+            continue
+
+        doc = nlp(text)
+        sentences = [sent.text for sent in doc.sents]
+
+        embeddings = model.encode(sentences)
+
+        n_clusters = max(1, len(sentences) // 5)  
+        kmeans = KMeans(n_clusters=n_clusters, random_state=42)
+        kmeans.fit(embeddings)
+
+        cluster_centers = kmeans.cluster_centers_
+        unique_points_count = len(cluster_centers)
+
+        word_count = len(text.split())
+        density = unique_points_count / word_count if word_count > 0 else 0
+
+        unique_key_points.append(unique_points_count)
+        information_density.append(density)
+
+    return unique_key_points, information_density
+
+def segment_comments(comments):
+    if comments == "N/A":
+        return {"strengths": "", "weaknesses": "", "general_discussion": ""}
+    
+    strengths = re.search(r"- Strengths:\n([\s\S]*?)(\n- Weaknesses:|\Z)", comments)
+    weaknesses = re.search(r"- Weaknesses:\n([\s\S]*?)(\n- General Discussion:|\Z)", comments)
+    general_discussion = re.search(r"- General Discussion:\n([\s\S]*?)\Z", comments)
+    
+    return {
+        "strengths": strengths.group(1).strip() if strengths else "",
+        "weaknesses": weaknesses.group(1).strip() if weaknesses else "",
+        "general_discussion": general_discussion.group(1).strip() if general_discussion else ""
+    }
+
+def preprocess(comment, abstract):
+    df = pd.DataFrame({"comments": [comment]})
+    abstracts = pd.DataFrame({"abstract": [abstract]})
+
+    segmented_reviews = df["comments"].apply(segment_comments)
+    df["strengths"] = segmented_reviews.apply(lambda x: x["strengths"])
+    df["weaknesses"] = segmented_reviews.apply(lambda x: x["weaknesses"])
+    df["general_discussion"] = segmented_reviews.apply(lambda x: x["general_discussion"])
+
+    comments_embeddings = model.encode(df['comments'].tolist(), convert_to_tensor=True)
+    abstract_embeddings = model.encode(abstracts["abstract"].tolist(), convert_to_tensor=True) 
+    df['content_relevance'] = util.cos_sim(comments_embeddings, abstract_embeddings).diagonal()
+
+    df['evidence_support'] = df['comments'].apply(count_citations)
+
+    df['strengths'] = df['strengths'].fillna('').astype(str)
+    texts = df['strengths'].tolist()  
+    results = analyze_text(texts)  
+    df['strength_argument_score'] = [result['score'] for result in results]
+
+    df['weaknesses'] = df['weaknesses'].fillna('').astype(str)
+    texts = df['weaknesses'].tolist()  
+    results = analyze_text(texts)  
+    df['weakness_argument_score'] = [result['score'] for result in results]
+
+    df['argument_strength'] = (df['strength_argument_score'] + df['weakness_argument_score']) / 2
+
+    df['readability_index'] = df['comments'].apply(textstat.flesch_reading_ease)
+    df['sentence_complexity'] = df['comments'].apply(textstat.sentence_count)
+    df['technical_depth'] = df['readability_index'] / df['sentence_complexity']
+
+    df['total_word_count'] = df['comments'].apply(word_count)
+    df['strength_word_count'] = df['strengths'].apply(word_count)
+    df['weakness_word_count'] = df['weaknesses'].apply(word_count)
+    df['discussion_word_count'] = df['general_discussion'].apply(word_count)
+
+    average_length = df['total_word_count'].mean()
+    df['normalized_length'] = df['total_word_count'] / average_length
+    df["unique_key_points"], df["information_density"] = calculate_unique_key_points_and_density(df["comments"])
+
+    df['unique_insights_per_word'] = df['comments'].apply(calculate_unique_insights_per_word) / df['total_word_count']
+
+    return df 
+
+def calculate_composite_score(df):
+    df['composite_score'] = (
+        weights['information_density'] * df['information_density'] +
+        weights['unique_key_points'] * df['unique_key_points'] +
+        weights['strength_word_count'] * df['strength_word_count'] +
+        weights['weakness_word_count'] * df['weakness_word_count'] +
+        weights['discussion_word_count'] * df['discussion_word_count']
+    )
+
+    return df
+
+def classify_review_quality(row):
+    if row['composite_score'] > 12: 
+        return 'Excellent Review Quality'
+    elif row['composite_score'] < 3:  
+        return 'Poor Review Quality'
+    else:
+        return 'Moderate Review Quality'
+
+def determine_review_quality(df):
+
+    df['normalized_length'] = df['total_word_count'] / df['total_word_count'].max()
+    df['unique_insights_per_word'] = df['unique_key_points'] / df['normalized_length']
+    df['adjusted_argument_strength'] = df['argument_strength'] / (1 + df['sentence_complexity'])
+
+    df['review_quality'] = df.apply(classify_review_quality, axis=1)
+
+    return df
+
+def heuristic_optimization(row):
+    suggestions = []
+
+    if row["strength_word_count"] > 100 and row["strength_argument_score"] < THRESHOLDS["adjusted_argument_strength"]:
+        suggestions.append("Summarize redundant strengths.")
+    elif row["strength_word_count"] < 50 and row["strength_argument_score"] < THRESHOLDS["adjusted_argument_strength"]:
+        suggestions.append("Add more impactful strengths.")
+
+    if row["weakness_word_count"] > 100 and row["weakness_argument_score"] < THRESHOLDS["adjusted_argument_strength"]:
+        suggestions.append("Remove repetitive criticisms.")
+    elif row["weakness_word_count"] < 50 and row["weakness_argument_score"] < THRESHOLDS["adjusted_argument_strength"]:
+        suggestions.append("Add specific, actionable weaknesses.")
+
+    if row["discussion_word_count"] < 100 and row["information_density"] < THRESHOLDS["information_density"][0]:
+        suggestions.append("Elaborate with new insights or examples.")
+    elif row["discussion_word_count"] > 300 and row["information_density"] > THRESHOLDS["information_density"][1]:
+        suggestions.append("Summarize key discussion points.")
+
+    if row["normalized_length"] < THRESHOLDS["normalized_length"][0]:
+        suggestions.append("Expand sections for better coverage.")
+    elif row["normalized_length"] > THRESHOLDS["normalized_length"][1]:
+        suggestions.append("Condense content to improve readability.")
+
+    if row["unique_key_points"] < THRESHOLDS["unique_key_points"][0]:
+        suggestions.append("Add more unique insights.")
+    elif row["unique_key_points"] > THRESHOLDS["unique_key_points"][1]:
+        suggestions.append("Streamline ideas for clarity.")
+
+    if row["composite_score"] < THRESHOLDS["composite_score"]:
+        suggestions.append("Enhance clarity, evidence, and argumentation.")
+
+    if row["review_quality"] == "Low":
+        suggestions.append("Significant revisions required.")
+    elif row["review_quality"] == "Moderate":
+        suggestions.append("Minor refinements recommended.")
+
+    return suggestions
+
+def pipeline(comment, abstract):
+    df = preprocess(comment, abstract)
+    df = calculate_composite_score(df)
+    df = determine_review_quality(df)
+    df["optimization_suggestions"] = df.apply(heuristic_optimization, axis=1)
+    return df["review_quality"][0], " ".join(df["optimization_suggestions"][0])
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Dynaic Length Optimization of Peer Review")
+    with gr.Row():
+        comment = gr.Textbox(label="Peer Review Comments")
+        abstract = gr.Textbox(label="Paper Abstract")
+        review_quality = gr.Textbox(label="Predicted Review Quality")
+        suggestions = gr.Textbox(label="Suggestions")
+    
+    comment.change(fn=pipeline, inputs=[comment, abstract], outputs=[review_quality, suggestions])
+
+demo.launch()