hamaadayubkhan commited on
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Create app.py

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  1. app.py +84 -0
app.py ADDED
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+ import gradio as gr
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+ from huggingface_hub import InferenceClient
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+ from sentence_transformers import SentenceTransformer
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+ import faiss
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+ import numpy as np
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+ import pdfplumber
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+
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+ # Initialize the InferenceClient
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+ client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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+
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+ # Function to extract text from PDFs
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+ def extract_text_from_pdf(pdf_path):
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+ text = ""
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+ with pdfplumber.open(pdf_path) as pdf:
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+ for page in pdf.pages:
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+ page_text = page.extract_text()
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+ if page_text:
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+ text += page_text
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+ return text
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+
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+ # Load and preprocess book PDFs
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+ pdf_files = ["Diagnostic and statistical manual of mental disorders _ DSM-5 ( PDFDrive.com ).pdf"]
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+ all_texts = [extract_text_from_pdf(pdf) for pdf in pdf_files]
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+
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+ # Split text into chunks
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+ def chunk_text(text, chunk_size=300):
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+ sentences = text.split('. ')
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+ chunks, current_chunk = [], ""
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+ for sentence in sentences:
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+ if len(current_chunk) + len(sentence) <= chunk_size:
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+ current_chunk += sentence + ". "
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+ else:
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+ chunks.append(current_chunk.strip())
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+ current_chunk = sentence + ". "
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+ if current_chunk:
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+ chunks.append(current_chunk.strip())
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+ return chunks
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+
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+ # Prepare embeddings for each book
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+ model = SentenceTransformer("all-MiniLM-L6-v2")
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+ index = faiss.IndexFlatL2(model.get_sentence_embedding_dimension())
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+ chunked_texts = [chunk_text(text) for text in all_texts]
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+ all_chunks = [chunk for chunks in chunked_texts for chunk in chunks]
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+ embeddings = model.encode(all_chunks, convert_to_tensor=True).detach().cpu().numpy()
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+ index.add(embeddings)
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+
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+ # Function to generate response
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+ def respond(message, history, system_message, max_tokens, temperature, top_p):
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+ # Step 1: Retrieve relevant chunks based on user message
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+ query_embedding = model.encode([message], convert_to_tensor=True).detach().cpu().numpy()
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+ k = 5
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+ _, indices = index.search(query_embedding, k)
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+ relevant_chunks = " ".join([all_chunks[idx] for idx in indices[0]])
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+
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+ # Step 2: Create prompt for the model
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+ prompt = f"{system_message}\n\nUser Query: {message}\n\nRelevant Information: {relevant_chunks}"
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+ response = ""
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+
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+ # Step 3: Generate response
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+ for message in client.chat_completion(
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+ [{"role": "system", "content": system_message}, {"role": "user", "content": message}],
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+ max_tokens=max_tokens,
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+ stream=True,
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+ temperature=temperature,
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+ top_p=top_p,
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+ ):
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+ token = message.choices[0].delta.content
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+ response += token
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+ yield response
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+
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+ # Gradio ChatInterface with additional inputs
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+ demo = gr.ChatInterface(
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+ respond,
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+ additional_inputs=[
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+ gr.Textbox(value="You are a helpful and empathetic mental health assistant.", label="System message"),
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+ gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
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+ gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
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+ gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
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+ ],
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+ )
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+
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+ # Launch the Gradio interface
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+ if __name__ == "__main__":
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+ demo.launch()