Update app.py

app.py

@@ -1,132 +1,40 @@
-import os
-import re
-import torch
+import gradio as gr 
 import pandas as pd
-from PyPDF2 import PdfReader
-from transformers import AutoTokenizer, pipeline, AutoModelForSequenceClassification
-from gradio import Interface, File
-import gradio as gr
-import spaces
-
-# Load the tokenizer and model
-led_tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-reranker-v2-m3")
-summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6", tokenizer="sshleifer/distilbart-cnn-12-6", framework="pt")
-
-# Load the model separately
-model = AutoModelForSequenceClassification.from_pretrained("BAAI/bge-reranker-v2-m3")
-
-# Move the model to CUDA if available
-if torch.cuda.is_available():
-    model = model.to("cuda")
-
-# Function to clean text by keeping only alphanumeric characters and spaces
-def clean_text(text):
-    return re.sub(r'[^a-zA-Z0-9\s]', '', text)
-    
-# Function to extract text from PDF files
-def extract_text(pdf_file):
-    try:
-        pdf_reader = PdfReader(pdf_file)
-        if pdf_reader.is_encrypted:
-            print(f"Skipping encrypted file: {pdf_file}")
-            return None
-        text = ''
-        for page in pdf_reader.pages:
-            text += page.extract_text() or ''
-        return text
-    except Exception as e:
-        print(f"Error extracting text from {pdf_file}: {e}")
-        return None
-
-# Function to split text into chunks of a specified size
-def split_text(text, chunk_size=1024):
-    words = text.split()
-    for i in range(0, len(words), chunk_size):
-        yield ' '.join(words[i:i + chunk_size])
-
-# Function to classify text using LED model
-@spaces.GPU(duration=120)
-def classify_text(text):
-    try:
-        return classifier(text)[0]['label']
-    except IndexError:
-        return "Unable to classify"
-
-# Function to summarize text using the summarizer model
-@spaces.GPU(duration=120)
-def summarize_text(text, max_length=100, min_length=30):
-    try:
-        return summarizer(text, max_length=max_length, min_length=min_length, do_sample=False)[0]['summary_text']
-    except IndexError:
-        return "Unable to summarize"
-
-# Function to extract a title-like summary from the beginning of the text
-@spaces.GPU(duration=120)
-def extract_title(text, max_length=20):
-    try:
-        return summarizer(text, max_length=max_length, min_length=5, do_sample=False)[0]['summary_text']
-    except IndexError:
-        return "Unable to extract title"
-
-# Define the folder path and CSV file path
-# output_folder_path = '/content/drive/My Drive/path_to_output'  # Adjust this to your actual path
-
-# Define the Gradio interface for file upload and download
-@spaces.GPU(duration=120)
-def process_files(pdf_files):
-    data = []
-    for pdf_file in pdf_files:
-        text = extract_text(pdf_file)
-
-        # Skip encrypted files
-        if text is None:
-            continue
-
-        # Extract a title from the beginning of the text
-        title_text = ' '.join(text.split()[:512])  # Take the first 512 tokens for title extraction
-        title = extract_title(title_text)
-
-        # Initialize placeholders for combined results
-        combined_abstract = []
-        combined_cleaned_text = []
-
-        # Split text into chunks and process each chunk
-        for chunk in split_text(text, chunk_size=512):
-            # Summarize the text chunk
-            abstract = summarize_text(chunk)
-            combined_abstract.append(abstract)
-
-            # Clean the text chunk
-            cleaned_text = clean_text(chunk)
-            combined_cleaned_text.append(cleaned_text)
-
-        # Combine results from all chunks
-        final_abstract = ' '.join(combined_abstract)
-        final_cleaned_text = ' '.join(combined_cleaned_text)
-
-        # Append the data to the list
-        data.append([title, final_abstract, final_cleaned_text])
-
-    # Create a DataFrame from the data list
-    df = pd.DataFrame(data, columns=['Title', 'Abstract', 'Content'])
-
-    # Save the DataFrame to a CSV file
-    output_file_path = 'processed_pdfs.csv'
-    df.to_csv(output_file_path, index=False)
-
-    return output_file_path
-    
-# Gradio interface
-pdf_input = gr.File(label="Upload PDF Files", file_types=[".pdf"], file_count="multiple")
-csv_output = gr.File(label="Download CSV")
-
-gr.Interface(
-    fn=process_files, 
-    inputs=pdf_input, 
-    outputs=csv_output,
-    title="Dataset creation",
-    description="Upload PDF files and get a summarized CSV file.",
-    article="""<p>This is an experimental app that allows you to create a dataset from research papers.</p>
-                <p>This app uses the allenai/led-base-16384-multi_lexsum-source-long and sshleifer/distilbart-cnn-12-6 AI models.</p>
-                <p>The output file is a CSV with 3 columns: title, abstract, and content.</p>"""
-).launch(share=True)
+from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration
+
+# Load the tokenizer and retriever
+tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
+retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", use_dummy_dataset=True)
+
+# Load the model
+model = RagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever)
+
+# Tokenize the contexts and responses
+inputs = tokenizer(contexts, return_tensors='pt', padding=True, truncation=True)
+labels = tokenizer(responses, return_tensors='pt', padding=True, truncation=True)
+
+# Load your dataset
+df = pd.read_csv('your_dataset.csv')
+
+# Ensure the dataset has the required columns for RAG
+# For example, it should have 'context' and 'response' columns
+contexts = df['Abstract'].tolist()
+#responses = df['response'].tolist()
+
+def generate_response(input_text):
+    input_ids = tokenizer([input_text], return_tensors='pt')['input_ids']
+    outputs = model.generate(input_ids)
+    response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+    return response
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=generate_response,
+    inputs="text",
+    outputs="text",
+    title="RAG Chatbot",
+    description="A chatbot powered by Retrieval-Augmented Generation (RAG) model."
+)
+
+# Launch the interface
+iface.launch()