Create app.py

app.py

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+import gradio as gr
+from gradio_pdf import PDF
+from qdrant_client import models, QdrantClient
+from sentence_transformers import SentenceTransformer
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.callbacks.manager import CallbackManager
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.vectorstores import Qdrant
+from transformers import AutoModelForCausalLM
+
+# Load the embedding model
+encoder = SentenceTransformer('jinaai/jina-embedding-b-en-v1')
+print("Embedding model loaded...")
+
+# Load the LLM
+callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+llm = AutoModelForCausalLM.from_pretrained(
+    "TheBloke/Llama-2-7B-Chat-GGUF",
+    model_file="llama-2-7b-chat.Q3_K_S.gguf",
+    model_type="llama",
+    temperature=0.2,
+    repetition_penalty=1.5,
+    max_new_tokens=300,
+)
+print("LLM loaded...")
+
+client = QdrantClient(path="./db")
+
+def setup_database(files):
+    all_chunks = []
+    for file in files:
+        pdf_path = file
+        reader = PdfReader(pdf_path)
+        text = "".join(page.extract_text() for page in reader.pages if page.extract_text())
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=250, chunk_overlap=50, length_function=len)
+        chunks = text_splitter.split_text(text)
+        all_chunks.extend(chunks)
+    
+    print(f"Total chunks: {len(all_chunks)}")
+    
+    client.recreate_collection(
+        collection_name="my_facts",
+        vectors_config=models.VectorParams(
+            size=encoder.get_sentence_embedding_dimension(),
+            distance=models.Distance.COSINE,
+        ),
+    )
+    
+    print("Collection created...")
+
+    for idx, chunk in enumerate(all_chunks):
+        client.upload_record(
+            collection_name="my_facts",
+            record=models.Record(
+                id=idx,
+                vector=encoder.encode(chunk).tolist(),
+                payload={"text": chunk}
+            )
+        )
+    
+    print("Records uploaded...")
+
+def answer(question):
+    hits = client.search(
+        collection_name="my_facts",
+        query_vector=encoder.encode(question).tolist(),
+        limit=3
+    )
+    
+    context = " ".join(hit.payload["text"] for hit in hits)
+    system_prompt = "You are a helpful co-worker. Use the provided context to answer user questions. Do not use any other information."
+    prompt = f"Context: {context}\nUser: {question}\n{system_prompt}"
+    response = llm(prompt)
+    return response
+
+def chat(messages):
+    if not messages:
+        return "Please upload PDF documents to initialize the database."
+    last_message = messages[-1]
+    return answer(last_message["message"])
+
+screen = gr.Interface(
+    fn=chat,
+    inputs=gr.Textbox(placeholder="Type your question here..."),
+    outputs="chatbot",
+    title="Q&A with PDFs 👩🏻‍💻📓✍🏻💡",
+    description="This app facilitates a conversation with PDFs uploaded💡",
+    theme="soft",
+    live=True,
+    allow_screenshot=False,
+    allow_flagging=False,
+)
+
+# Add a way to upload and setup the database before starting the chat
+screen.launch()