Delete app.py

app.py

@@ -1,91 +0,0 @@
-import gradio as gr
-import os
-import PyPDF2  # Import PyPDF2 for PDF text extraction
-import nltk
-from nltk.tokenize import word_tokenize
-from nltk.corpus import stopwords
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-
-# Load NLTK resources
-nltk.download('punkt')
-nltk.download('stopwords')
-
-# Function to extract text from PDFs using PyPDF2
-def extract_text_from_pdf(pdf_path):
-    pdf_text = ""
-    with open(pdf_path, 'rb') as pdf_file:
-        pdf_reader = PyPDF2.PdfFileReader(pdf_file)
-        for page_num in range(pdf_reader.getNumPages()):
-            page = pdf_reader.getPage(page_num)
-            pdf_text += page.extractText()
-    return pdf_text
-
-# Function to clean and tokenize text
-def clean_and_tokenize(text):
-    tokens = word_tokenize(text.lower())
-    tokens = [word for word in tokens if word.isalnum() and word not in stopwords.words('english')]
-    return ' '.join(tokens)
-
-# Function to preprocess the documents in the specified directory
-def preprocess_documents(dataset_dir):
-    documents = []
-    for filename in os.listdir(dataset_dir):
-        if filename.endswith('.pdf'):
-            pdf_path = os.path.join(dataset_dir, filename)
-            pdf_text = extract_text_from_pdf(pdf_path)
-            clean_text = clean_and_tokenize(pdf_text)
-            documents.append(clean_text)
-    return documents
-
-# Function to perform relevance matching and return top N documents
-def perform_relevance_matching(query, *uploaded_files, dataset_dir):
-    # Preprocess the documents in the specified dataset directory
-    documents = preprocess_documents(dataset_dir)
-    
-    # Combine the user-uploaded files into a single document
-    uploaded_documents = []
-    for file in uploaded_files:
-        uploaded_text = extract_text_from_pdf(file.name)
-        uploaded_documents.append(uploaded_text)
-    
-    # Combine the uploaded documents and query
-    combined_documents = uploaded_documents + [query]
-    
-    # Vectorize the combined documents
-    tfidf_vectorizer = TfidfVectorizer()
-    tfidf_matrix = tfidf_vectorizer.fit_transform(documents + combined_documents)
-    
-    # Calculate cosine similarities between the combined documents and the dataset
-    cosine_similarities = cosine_similarity(tfidf_matrix[-len(combined_documents):], tfidf_matrix[:-len(combined_documents)])
-    
-    # Rank documents by similarity score
-    document_scores = list(enumerate(cosine_similarities[0]))
-    sorted_documents = sorted(document_scores, key=lambda x: x[1], reverse=True)
-    
-    # Extract the top N relevant documents
-    top_n = 5
-    top_documents = []
-    for i in range(min(top_n, len(sorted_documents))):
-        doc_index, score = sorted_documents[i]
-        document_text = documents[doc_index][:500]  # Extract the first 500 characters of the document
-        top_documents.append((f"Document {doc_index + 1} (Similarity Score: {score:.4f})", document_text))
-    
-    return top_documents
-
-# Create a Gradio interface
-iface = gr.Interface(
-    fn=perform_relevance_matching,
-    inputs=[
-        "text",  # Query input
-        gr.File(multiple=True),  # Allow multiple file uploads
-        "text"  # Dataset directory input
-    ],
-    outputs=gr.Table(),
-    live=True,
-    title="Legal Research Assistant",
-    description="Enter your legal query, upload files, and specify the dataset directory.",
-)
-
-# Launch the Gradio interface
-iface.launch()