Update app.py

app.py

@@ -1,63 +1,235 @@
+!pip install gradio
+
+
+!pip install datasets tqdm pandas matplotlib langchain sentence_transformers faiss-gpu langchain-community torch accelerate
+
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+import pandas as pd
+from tqdm.notebook import tqdm
+from datasets import Dataset
+import matplotlib.pyplot as plt
+from langchain.docstore.document import Document as LangchainDocument
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from sentence_transformers import SentenceTransformer
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from langchain.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores.utils import DistanceStrategy
+import torch
+
+
+# Set display option for pandas
+pd.set_option("display.max_colwidth", None)
+
+# Open and read the first file
+with open("/content/iplteams_info.txt", "r") as fp1:
+    content1 = fp1.read()
+
+# Open and read the second file
+with open("/content/match_summaries_sentences.txt", "r") as fp2:
+    content2 = fp2.read()
+
+# Open and read the second file
+with open("/content/formatted_playersinfo.txt", "r") as fp3:
+    content3 = fp3.read()
+
+
+# Combine contents of both files, separated by three newlines
+combined_content = content1 + "\n\n\n" + content2 + "\n\n\n" + content3
+
+# Split the combined content into sections
+s = combined_content.split("\n\n\n")
+
+# Print the first section and the number of sections
+print(s[0])
+print(len(s))
+
+# Create a RAW_KNOWLEDGE_BASE using LangchainDocument
+RAW_KNOWLEDGE_BASE = [
+    LangchainDocument(page_content=doc)
+    for doc in tqdm(s)
+]
+
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from sentence_transformers import SentenceTransformer
+from transformers import AutoTokenizer
+
+MARKDOWN_SEPARATORS = [
+    "\n#{1,6}",
+    "```\n",
+    "\n\\*\\*\\*+\n",
+    "\n---+\n",
+    "\n__+\n",
+    "\n\n",
+    "\n",
+    " ",
+    ""
+]
+
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size=1000,
+    chunk_overlap=100,
+    add_start_index=True,
+    strip_whitespace=True,
+    separators=MARKDOWN_SEPARATORS,
+)
+
+docs_processed = []
+for doc in RAW_KNOWLEDGE_BASE:
+    docs_processed += text_splitter.split_documents([doc])
+
+tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-small")
+lengths = [len(tokenizer.encode(doc.page_content)) for doc in tqdm(docs_processed)]
+
+fig = pd.Series(lengths).hist()
+fig.set_title("Histogram of Document Lengths")
+plt.title("Distribution")
+plt.show()
+
+
+from typing import Optional, List
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from transformers import AutoTokenizer
+
+EMBEDDING_MODEL_NAME = "thenlper/gte-small"
+
+def split_documents(
+        chunk_size: int,
+        knowledge_base: list[LangchainDocument],
+        tokenizer_name: Optional[str] = EMBEDDING_MODEL_NAME,
+) -> List[LangchainDocument]:
+    text_splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(
+        AutoTokenizer.from_pretrained(tokenizer_name),
+        chunk_size=chunk_size,
+        chunk_overlap=int(chunk_size / 10),
+        add_start_index=True,
+        strip_whitespace=True,
+        separators=MARKDOWN_SEPARATORS,
+    )
+    docs_processed = []
+    for doc in knowledge_base:
+        docs_processed += text_splitter.split_documents([doc])
+
+    unique_texts = {}
+    docs_processed_unique = []
+    for doc in docs_processed:
+        if doc.page_content not in unique_texts:
+            unique_texts[doc.page_content] = True
+            docs_processed_unique.append(doc)
+    return docs_processed_unique
+
+docs_processed = split_documents(512, RAW_KNOWLEDGE_BASE, tokenizer_name=EMBEDDING_MODEL_NAME)
+print(len(docs_processed))
+print(docs_processed[0:3])
+
+
+from langchain.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores.utils import DistanceStrategy
+import torch
+
+print(torch.cuda.is_available())
+
+embedding_model = HuggingFaceEmbeddings(
+    model_name=EMBEDDING_MODEL_NAME,
+    multi_process=True,
+    model_kwargs={"device": "cuda"},
+    encode_kwargs={"normalize_embeddings": True},
+)
+
+KNOWLEDGE_VECTOR_DATABASE = FAISS.from_documents(
+    docs_processed,
+    embedding_model,
+    distance_strategy=DistanceStrategy.COSINE,
+)
+
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+torch.random.manual_seed(0)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "microsoft/Phi-3-mini-128k-instruct",
+    device_map="cuda",
+    torch_dtype="auto",
+    trust_remote_code=True,
+)
+tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-instruct")
+
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+)
+
+generation_args = {
+    "max_new_tokens": 500,
+    "return_full_text": False,
+    "temperature": 0.0,
+    "do_sample": False,
+}
+
+
+prompt_chat=[
+    {
+        "role":"system",
+        "content":"""Using the information contained in the context,
+Give a comprehensive answer to the question.
+Respond only to the question asked , response should be concise and relevant to the question.
+provide the number of the source document when relevant.
+If the answer cannot be deduced from the context, do not give an answer""",
+
+    },
+    {
+        "role":"user",
+        "content":"""Context:
+{context}
+---
+Now here is the Question you need to answer.
+Question:{question}
+        """,
+    },
+]
+RAG_PROMPT_TEMPLATE = tokenizer.apply_chat_template(
+    prompt_chat,tokenize = False,add_generation_prompt=True,
+
+)
+print(RAG_PROMPT_TEMPLATE)
+
+u_query = "give the match summary of royal challengers bengaluru and mumbai indians in 2024"
+# ret_text = KNOWLEDGE_VECTOR_DATABASE.similarity_search(query=u_query,k=3)
+retrieved_docs = KNOWLEDGE_VECTOR_DATABASE.similarity_search(query=u_query,k=3)
+
+context = retrieved_docs[0].page_content
+final_prompt = RAG_PROMPT_TEMPLATE.format(
+    question= u_query, context = context
+)
+
+output = pipe(final_prompt, **generation_args)
+print("YOUR QUESTION:\n",u_query,"\n")
+print("MICROSOFT 128K ANSWER: \n",output[0]['generated_text'])
+
+# Define the function to handle queries
+def handle_query(question):
+    retrieved_docs = KNOWLEDGE_VECTOR_DATABASE.similarity_search(query=question, k=3)
+    context = retrieved_docs[0].page_content
+    final_prompt = RAG_PROMPT_TEMPLATE.format(
+        question=question, context=context
+    )
+    output = pipe(final_prompt, **generation_args)
+    return output[0]['generated_text']
+
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=handle_query,
+    inputs="text",
+    outputs="text",
+    title="IPL Match Summary Generator",
+    description="Get the match summary of IPL teams based on your query.",
+)
+
+interface.launch(share=True)
+