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 qdrant_client.http import models
+from ctransformers import AutoModelForCausalLM
+
+# Loading the embedding model
+encoder = SentenceTransformer('jinaai/jina-embedding-b-en-v1')
+print("Embedding model loaded...")
+
+# Loading 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...")
+
+def chat(files, question):
+    def get_chunks(text):
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=250,
+            chunk_overlap=50,
+            length_function=len,
+        )
+        chunks = text_splitter.split_text(text)
+        return chunks
+
+    all_chunks = []
+    
+    for file in files:
+        pdf_path = file
+        reader = PdfReader(pdf_path)
+        text = ""
+        num_of_pages = len(reader.pages)
+
+        for page in range(num_of_pages):
+            current_page = reader.pages[page]
+            text += current_page.extract_text()
+
+        chunks = get_chunks(text)
+        all_chunks.extend(chunks)
+    
+    print(f"Total chunks: {len(all_chunks)}")
+    print("Chunks are ready...")
+
+    client = QdrantClient(path="./db")
+    print("DB created...")
+
+    client.recreate_collection(
+        collection_name="my_facts",
+        vectors_config=models.VectorParams(
+            size=encoder.get_sentence_embedding_dimension(),
+            distance=models.Distance.COSINE,
+        ),
+    )
+
+    print("Collection created...")
+
+    li = list(range(len(all_chunks)))
+    dic = dict(zip(li, all_chunks))
+
+    client.upload_records(
+        collection_name="my_facts",
+        records=[
+            models.Record(
+                id=idx,
+                vector=encoder.encode(dic[idx]).tolist(),
+                payload={f"chunk_{idx}": dic[idx]}
+            ) for idx in dic.keys()
+        ],
+    )
+
+    print("Records uploaded...")
+
+    hits = client.search(
+        collection_name="my_facts",
+        query_vector=encoder.encode(question).tolist(),
+        limit=3
+    )
+    context = []
+    for hit in hits:
+        context.append(list(hit.payload.values())[0])
+    
+    context = " ".join(context)
+
+    system_prompt = """You are a helpful co-worker, you will use the provided context to answer user questions.
+    Read the given context before answering questions and think step by step. If you cannot answer a user question based on 
+    the provided context, inform the user. Do not use any other information for answering user. Provide a detailed answer to the question."""
+
+    B_INST, E_INST = "[INST]", "[/INST]"
+    B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
+
+    SYSTEM_PROMPT = B_SYS + system_prompt + E_SYS
+
+    instruction = f""" 
+    Context: {context}
+    User: {question}"""
+
+    prompt_template = B_INST + SYSTEM_PROMPT + instruction + E_INST
+    print(prompt_template)
+    result = llm(prompt_template)
+    return result
+
+screen = gr.Interface(
+    fn=chat,
+    inputs=[gr.File(label="Upload PDFs", file_count="multiple"), gr.Textbox(lines=10, placeholder="Enter your question here 👉")],
+    outputs=gr.Textbox(lines=10, placeholder="Your answer will be here soon 🚀"),
+    title="Q&A with PDFs 👩🏻‍💻📓✍🏻💡",
+    description="This app facilitates a conversation with PDFs uploaded💡",
+    theme="soft",
+)
+
+screen.launch()