uploaded the model and changed the inference library

app.py

@@ -0,0 +1,228 @@
+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.llms import LlamaCpp
+from langchain.vectorstores import Qdrant
+from qdrant_client.http import models
+# from langchain.llms import CTransformers
+from ctransformers import AutoModelForCausalLM
+
+
+# loading the embedding model - 
+
+encoder = SentenceTransformer('jinaai/jina-embedding-b-en-v1')
+
+print("embedding model loaded.............................")
+print("####################################################")
+
+# loading the LLM 
+
+callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+
+print("loading the LLM......................................")
+
+llm = LlamaCpp(
+    model_path="./llama-2-7b-chat.Q3_K_S.gguf",
+    n_ctx=2048,
+    f16_kv=True,  # MUST set to True, otherwise you will run into problem after a couple of calls
+    callback_manager=callback_manager,
+    verbose=True,
+)
+
+# 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........................................")
+print("################################################################")
+
+# def get_chunks(text):
+#     text_splitter = RecursiveCharacterTextSplitter(
+#         # seperator = "\n",
+#         chunk_size = 250,
+#         chunk_overlap = 50,
+#         length_function = len,
+#     )
+
+#     chunks = text_splitter.split_text(text)
+#     return chunks
+
+
+# pdf_path = './100 Weird Facts About the Human Body.pdf'
+
+
+# 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)
+# print(chunks)
+# print("Chunks are ready.....................................")
+# print("######################################################")
+
+# client = QdrantClient(path = "./db")
+# print("db  created................................................")
+# print("#####################################################################")
+
+# client.recreate_collection(
+#     collection_name="my_facts",
+#     vectors_config=models.VectorParams(
+#         size=encoder.get_sentence_embedding_dimension(),  # Vector size is defined by used model
+#         distance=models.Distance.COSINE,
+#     ),
+# )
+
+# print("Collection created........................................")
+# print("#########################################################")
+
+
+
+# li = []
+# for i in range(len(chunks)):
+#     li.append(i)
+ 
+# dic = zip(li, chunks)
+# dic= dict(dic)
+
+# client.upload_records(
+#     collection_name="my_facts",
+#     records=[
+#         models.Record(
+#             id=idx,
+#             vector=encoder.encode(dic[idx]).tolist(),
+#             payload= {dic[idx][:5] : dic[idx]}
+#         ) for idx in dic.keys()
+#     ],
+# )
+
+# print("Records uploaded........................................")
+# print("###########################################################")
+
+def chat(file, question):
+    def get_chunks(text):
+        text_splitter = RecursiveCharacterTextSplitter(
+            # seperator = "\n",
+            chunk_size = 250,
+            chunk_overlap = 50,
+            length_function = len,
+        )
+
+        chunks = text_splitter.split_text(text)
+        return chunks
+
+
+    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)
+    # print(chunks)
+# print("Chunks are ready.....................................")
+# print("######################################################")
+
+    client = QdrantClient(path = "./db")
+# print("db  created................................................")
+# print("#####################################################################")
+
+    client.recreate_collection(
+        collection_name="my_facts",
+        vectors_config=models.VectorParams(
+            size=encoder.get_sentence_embedding_dimension(),  # Vector size is defined by used model
+            distance=models.Distance.COSINE,
+        ),
+    )
+
+# print("Collection created........................................")
+# print("#########################################################")
+
+
+
+    li = []
+    for i in range(len(chunks)):
+        li.append(i)
+ 
+    dic = zip(li, chunks)
+    dic= dict(dic)
+
+    client.upload_records(
+        collection_name="my_facts",
+        records=[
+            models.Record(
+                id=idx,
+                vector=encoder.encode(dic[idx]).tolist(),
+                payload= {dic[idx][:5] : dic[idx]}
+            ) for idx in dic.keys()
+        ],
+    )
+
+# print("Records uploaded........................................")
+# print("###########################################################")
+
+
+    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 = context[0] + context[1] + context[2]
+
+    system_prompt = """You are a helpful assistant, you will use the provided context to answer user questions.
+    Read the given context before answering questions and think step by step. If you can not 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
+
+    result = llm(prompt_template)
+    return result 
+
+
+screen = gr.Interface(
+    fn = chat,
+    inputs = [PDF(label="Upload a PDF", interactive=True), 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 PDF 👩🏻‍💻📓✍🏻💡",
+description="This app facilitates a conversation with PDFs available on https://www.delo.si/assets/media/other/20110728/100%20Weird%20Facts%20About%20the%20Human%20Body.pdf💡",
+    theme="soft",
+    # examples=["Hello", "what is the speed of human nerve impulses?"],
+)
+
+screen.launch()