Create app.py

Created chatbot with RAG model

app.py

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+import os
+import PyPDF2
+import faiss
+import torch
+import streamlit as st
+from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer
+from sentence_transformers import SentenceTransformer
+
+# Load embedding model
+embedding_model = SentenceTransformer("sentence-transformers/paraphrase-MiniLM-L3-v2")
+
+# Load a powerful LLM (e.g., Mistral-7B, GPT-4 API, T5-based model)
+llm_model_name = "google/flan-t5-small"
+llm_tokenizer = AutoTokenizer.from_pretrained(llm_model_name)
+#llm_model = AutoModelForSeq2SeqLM.from_pretrained(llm_model_name)
+llm_model = AutoModelForSeq2SeqLM.from_pretrained(llm_model_name, torch_dtype=torch.float16)
+
+
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf_path):
+    """Extract text from a research paper (PDF)."""
+    text = ""
+    with open(pdf_path, "rb") as f:
+        reader = PyPDF2.PdfReader(f)
+        for page in reader.pages:
+            text += page.extract_text() + "\n"
+    return text
+
+# Function to chunk text into sections
+def chunk_text(text, chunk_size=300):
+    """Splits text into sections based on paper structure."""
+    sections = {}
+    split_text = text.split("\n")
+    #chunk_size = min(chunk_size, len(split_text))
+    current_section = "Other"
+    sections[current_section] = []
+
+    for line in split_text:
+        line = line.strip()
+        if line.lower().startswith("abstract"):
+            current_section = "Abstract"
+            sections[current_section] = []
+        elif line.lower().startswith("introduction"):
+            current_section = "Introduction"
+            sections[current_section] = []
+        elif line.lower().startswith("conclusion"):
+            current_section = "Conclusion"
+            sections[current_section] = []
+        
+        sections[current_section].append(line)
+
+    # Convert sections to chunks
+    for section in sections:
+        sections[section] = " ".join(sections[section])
+
+    return sections
+
+# Function to create FAISS vector database
+def build_vector_database(sections):
+    """Builds FAISS vector index for research paper sections."""
+    chunk_texts = list(sections.values())
+    embeddings = embedding_model.encode(chunk_texts)
+    dim = embeddings.shape[1]
+    index = faiss.IndexFlatL2(dim)
+    index.add(embeddings)
+    return index, embeddings, list(sections.keys()), chunk_texts
+
+# Function to retrieve relevant context
+def retrieve_context(query, index, embeddings, section_titles, section_texts, top_k=1):
+    """Retrieves most relevant sections for a query."""
+    query_embedding = embedding_model.encode([query])
+    embeddings = torch.tensor(embeddings)
+    distances, indices = index.search(query_embedding, top_k)
+    retrieved_contexts = [f"**{section_titles[idx]}**: {section_texts[idx]}" for idx in indices[0]]
+    return "\n".join(retrieved_contexts)
+
+
+
+# Function to generate a concise answer
+def generate_answer_rag(question, context, max_length=512):
+    """Truncate input text to prevent exceeding model token limit."""
+    input_text = f"Question: {question}\nContext: {context[:max_length]}"
+    input_ids = llm_tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512).input_ids
+    output_ids = llm_model.generate(input_ids, max_length=150)
+    return llm_tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+
+
+
+# Streamlit UI
+def main():
+    st.title("AI Research Paper RAG Chatbot")
+
+    uploaded_pdf = st.file_uploader("Upload a Research Paper (PDF)", type=["pdf"])
+    if uploaded_pdf is not None:
+        pdf_path = "uploaded_paper.pdf"
+        with open(pdf_path, "wb") as f:
+            f.write(uploaded_pdf.read())
+        st.success("PDF uploaded successfully!")
+
+        # Extract and preprocess text
+        text = extract_text_from_pdf(pdf_path)
+        text_sections = chunk_text(text)
+
+        # Build FAISS vector database
+        index, embeddings, section_titles, section_texts = build_vector_database(text_sections)
+        st.write(f"Paper processed into {len(text_sections)} sections for efficient retrieval.")
+
+        # User query input
+        user_question = st.text_input("Ask a question about the paper:")
+        if user_question:
+            context = retrieve_context(user_question, index, embeddings, section_titles, section_texts)
+            answer = generate_answer_rag(user_question, context)
+            
+            st.write(f"**Retrieved Context:**\n{context}")
+            st.write(f"**Generated Answer:**\n{answer}")
+
+if __name__ == "__main__":
+    main()