Create app.py

app.py

@@ -0,0 +1,69 @@
+import gradio as gr
+import os
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+from langchain.embeddings import SentenceTransformerEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.llms.huggingface_pipeline import HuggingFacePipeline
+from langchain.chains import RetrievalQA
+from langchain.document_loaders import PDFMinerLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from chromadb.config import Settings
+
+# Initialize Chroma settings once
+CHROMA_SETTINGS = Settings(
+    chroma_db_impl='duckdb+parquet',
+    persist_directory="db",
+    anonymized_telemetry=False
+)
+
+# Initialize the Chroma database on app start (assuming the database will be initialized only once)
+def init_db_if_not_exists(pdf_path):
+    try:
+        # Check if the database exists and load it
+        db = Chroma(persist_directory=CHROMA_SETTINGS.persist_directory, client_settings=CHROMA_SETTINGS)
+        db.get_collection()  # This line will raise an error if the collection doesn't exist
+    except Exception:
+        # If not, initialize the database
+        loader = PDFMinerLoader(pdf_path)
+        documents = loader.load()
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+        texts = text_splitter.split_documents(documents)
+        embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+        db = Chroma.from_documents(texts, embeddings, persist_directory=CHROMA_SETTINGS.persist_directory)
+        db.persist()
+
+# Load model and create pipeline once
+checkpoint = "MBZUAI/LaMini-Flan-T5-783M"
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+base_model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, device_map="auto", torch_dtype=torch.float32)
+llm_pipeline = HuggingFacePipeline(pipeline=pipeline("text2text-generation", model=base_model, tokenizer=tokenizer))
+
+def process_answer(instruction):
+    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+    vectordb = Chroma(persist_directory=CHROMA_SETTINGS.persist_directory, embedding_function=embeddings)
+    retriever = vectordb.as_retriever()
+    qa = RetrievalQA.from_chain_type(llm=llm_pipeline, chain_type="stuff", retriever=retriever)
+    generated_text = qa(instruction)
+    return generated_text["result"]
+
+def chatbot(pdf_file, user_question):
+    if pdf_file:  # Only initialize if a new PDF is uploaded
+        init_db_if_not_exists(pdf_file.name)
+    try:
+        answer = process_answer(user_question)
+        return answer
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+
+# Create Gradio Interface
+iface = gr.Interface(
+    fn=chatbot,
+    inputs=[gr.inputs.File(type="file", label="Upload your PDF"), gr.inputs.Textbox(lines=1, label="Ask a Question")],
+    outputs="text",
+    title="PDF Chatbot",
+    description="Upload a PDF and ask questions about its content.",
+)
+
+# Run the Gradio interface
+iface.launch()