Update app.py

app.py

@@ -6,73 +6,167 @@ 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
+
+# loading the embedding model - 
+
 encoder = SentenceTransformer('jinaai/jina-embedding-b-en-v1')
-print("Embedding model loaded...")
 
-# Loading the LLM
+print("embedding model loaded.............................")
+print("####################################################")
+
+# 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("loading the LLM......................................")
+
+# llm = LlamaCpp(
+#     model_path="TheBloke/Llama-2-7B-Chat-GGUF/llama-2-7b-chat.Q8_0.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("#####################################################################")
 
-print("LLM loaded...")
+# 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("#########################################################")
 
-def chat(files, question):
+
+
+# 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(
-            chunk_size=250,
-            chunk_overlap=50,
-            length_function=len,
+            # seperator = "\n",
+            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...")
+    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(),
+            size=encoder.get_sentence_embedding_dimension(),  # Vector size is defined by used model
             distance=models.Distance.COSINE,
         ),
     )
 
-    print("Collection created...")
+# print("Collection created........................................")
+# print("#########################################################")
+
+
 
-    li = list(range(len(all_chunks)))
-    dic = dict(zip(li, all_chunks))
+    li = []
+    for i in range(len(chunks)):
+        li.append(i)
+ 
+    dic = zip(li, chunks)
+    dic= dict(dic)
 
     client.upload_records(
         collection_name="my_facts",
@@ -80,12 +174,14 @@ def chat(files, question):
             models.Record(
                 id=idx,
                 vector=encoder.encode(dic[idx]).tolist(),
-                payload={f"chunk_{idx}": dic[idx]}
+                payload= {dic[idx][:5] : dic[idx]}
             ) for idx in dic.keys()
         ],
     )
 
-    print("Records uploaded...")
+# print("Records uploaded........................................")
+# print("###########################################################")
+
 
     hits = client.search(
         collection_name="my_facts",
@@ -94,15 +190,17 @@ def chat(files, question):
     )
     context = []
     for hit in hits:
-        context.append(list(hit.payload.values())[0])
+      context.append(list(hit.payload.values())[0])
     
-    context = " ".join(context)
+    context = context[0] + context[1] + context[2]
 
-    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 
+    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
@@ -112,17 +210,19 @@ def chat(files, question):
     User: {question}"""
 
     prompt_template = B_INST + SYSTEM_PROMPT + instruction + E_INST
-    print(prompt_template)
+
     result = llm(prompt_template)
-    return result
+    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💡",
+    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()
+screen.launch()