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Browse files- .gitattributes +36 -35
- .ragatouille/colbert/indexes/cbseclass10index/0.codes.pt +3 -0
- .ragatouille/colbert/indexes/cbseclass10index/0.metadata.json +6 -0
- .ragatouille/colbert/indexes/cbseclass10index/0.residuals.pt +3 -0
- .ragatouille/colbert/indexes/cbseclass10index/avg_residual.pt +3 -0
- .ragatouille/colbert/indexes/cbseclass10index/buckets.pt +3 -0
- .ragatouille/colbert/indexes/cbseclass10index/centroids.pt +3 -0
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- .ragatouille/colbert/indexes/cbseclass10index/metadata.json +70 -0
- .ragatouille/colbert/indexes/cbseclass10index/pid_docid_map.json +1630 -0
- .ragatouille/colbert/indexes/cbseclass10index/plan.json +64 -0
- README.md +13 -12
- app.py +393 -0
- backend/query_llm.py +161 -0
- backend/semantic_search.py +28 -0
- lancedb/cbse.lance/_latest.manifest +0 -0
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- lancedb/cbse.lance/data/615853d2-bbd1-453c-88c7-8df0edb43e5e.lance +3 -0
- logo.png +0 -0
- requirements.txt +12 -0
- templates/template.j2 +8 -0
- templates/template_html.j2 +103 -0
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"config":{
|
| 3 |
+
"query_token_id":"[unused0]",
|
| 4 |
+
"doc_token_id":"[unused1]",
|
| 5 |
+
"query_token":"[Q]",
|
| 6 |
+
"doc_token":"[D]",
|
| 7 |
+
"ncells":null,
|
| 8 |
+
"centroid_score_threshold":null,
|
| 9 |
+
"ndocs":null,
|
| 10 |
+
"load_index_with_mmap":false,
|
| 11 |
+
"index_path":null,
|
| 12 |
+
"index_bsize":32,
|
| 13 |
+
"nbits":8,
|
| 14 |
+
"kmeans_niters":20,
|
| 15 |
+
"resume":false,
|
| 16 |
+
"similarity":"cosine",
|
| 17 |
+
"bsize":64,
|
| 18 |
+
"accumsteps":1,
|
| 19 |
+
"lr":0.00001,
|
| 20 |
+
"maxsteps":400000,
|
| 21 |
+
"save_every":null,
|
| 22 |
+
"warmup":20000,
|
| 23 |
+
"warmup_bert":null,
|
| 24 |
+
"relu":false,
|
| 25 |
+
"nway":64,
|
| 26 |
+
"use_ib_negatives":true,
|
| 27 |
+
"reranker":false,
|
| 28 |
+
"distillation_alpha":1.0,
|
| 29 |
+
"ignore_scores":false,
|
| 30 |
+
"model_name":null,
|
| 31 |
+
"query_maxlen":32,
|
| 32 |
+
"attend_to_mask_tokens":false,
|
| 33 |
+
"interaction":"colbert",
|
| 34 |
+
"dim":128,
|
| 35 |
+
"doc_maxlen":256,
|
| 36 |
+
"mask_punctuation":true,
|
| 37 |
+
"checkpoint":"colbert-ir/colbertv2.0",
|
| 38 |
+
"triples":"/future/u/okhattab/root/unit/experiments/2021.10/downstream.distillation.round2.2_score/round2.nway6.cosine.ib/examples.64.json",
|
| 39 |
+
"collection":[
|
| 40 |
+
"list with 1628 elements starting with...",
|
| 41 |
+
[
|
| 42 |
+
"Chapter 5 - Classification_Assessment of Projects Imports_ - Para 4 - _4. Clearance of goods after registration_.txt\n3.1 In terms of Regulation 4 of the Project Import Regulations, 1986 (PIR), the basic requirement for \navailing the benefit of assessment under Tariff Heading No. 98.01 is that the importer should have \nentered into one or more contracts with the su ppliers of the goods for setting up a project. Such \ncontracts should be registered, prior to clearance, in the Custom House through which the goods \nare expected to be imported. The importer shall apply for such registration in writing to the proper \nofficer of Customs.",
|
| 43 |
+
"Such \ncontracts should be registered, prior to clearance, in the Custom House through which the goods \nare expected to be imported. The importer shall apply for such registration in writing to the proper \nofficer of Customs. \n3.2 Regulation 5 provides in the manner of registering contracts, which is as follows: \n(i) Before any order is made by the proper officer of Customs permitting the clearance of the \ngoods for home consumption; \n(ii) In the case of goods cleared for hom e consumption without payment of duty subject to re -\nexport in respect of fairs, exhibitions, demonstrations, seminars, congresses and \nconferences, duly sponsored or approved by the Government of India or Trade fair Authority \nof India, as the case may be, b efore the date of payment of duty. \n3.3 To expedite registration, the importers are advised to submit the following documents along with \nthe application for registration: \n(i) Original deed of contract together with true copy thereof.",
|
| 44 |
+
"Chapter 5 - Classification_Assessment of Projects Imports_ - Para 4 - _4. Clearance of goods after registration_.txt\n(ii) Industrial License and letter of intent, SSI Certificate granted by the appropriate authority \nwith a copy thereof. \n \n(iii) Original Import licence, if any, with a list of items showing the dimensions, specifications, \nquantity, quality, value of each item duly attested by the Licensing Authority and a copy \nthereof."
|
| 45 |
+
]
|
| 46 |
+
],
|
| 47 |
+
"queries":"/future/u/okhattab/data/MSMARCO/queries.train.tsv",
|
| 48 |
+
"index_name":"cbseclass10index",
|
| 49 |
+
"overwrite":false,
|
| 50 |
+
"root":".ragatouille/",
|
| 51 |
+
"experiment":"colbert",
|
| 52 |
+
"index_root":null,
|
| 53 |
+
"name":"2024-08/15/14.23.27",
|
| 54 |
+
"rank":0,
|
| 55 |
+
"nranks":1,
|
| 56 |
+
"amp":true,
|
| 57 |
+
"gpus":1,
|
| 58 |
+
"avoid_fork_if_possible":false
|
| 59 |
+
},
|
| 60 |
+
"num_chunks":1,
|
| 61 |
+
"num_partitions":4096,
|
| 62 |
+
"num_embeddings":202637,
|
| 63 |
+
"avg_doclen":124.4699017199,
|
| 64 |
+
"RAGatouille":{
|
| 65 |
+
"index_config":{
|
| 66 |
+
"index_type":"PLAID",
|
| 67 |
+
"index_name":"cbseclass10index"
|
| 68 |
+
}
|
| 69 |
+
}
|
| 70 |
+
}
|
.ragatouille/colbert/indexes/cbseclass10index/pid_docid_map.json
ADDED
|
@@ -0,0 +1,1630 @@
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| 1609 |
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|
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|
| 1618 |
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| 1619 |
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|
| 1620 |
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|
| 1621 |
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|
| 1622 |
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"1620":"5029fdc5-9e13-4221-b888-2eb8764ede6c",
|
| 1623 |
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|
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|
| 1625 |
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|
| 1626 |
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|
| 1627 |
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|
| 1628 |
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|
| 1629 |
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"1627":"17dc8801-a1dc-486f-8ebf-814bc37467fb"
|
| 1630 |
+
}
|
.ragatouille/colbert/indexes/cbseclass10index/plan.json
ADDED
|
@@ -0,0 +1,64 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"config": {
|
| 3 |
+
"query_token_id": "[unused0]",
|
| 4 |
+
"doc_token_id": "[unused1]",
|
| 5 |
+
"query_token": "[Q]",
|
| 6 |
+
"doc_token": "[D]",
|
| 7 |
+
"ncells": null,
|
| 8 |
+
"centroid_score_threshold": null,
|
| 9 |
+
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|
| 10 |
+
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|
| 11 |
+
"index_path": null,
|
| 12 |
+
"index_bsize": 32,
|
| 13 |
+
"nbits": 8,
|
| 14 |
+
"kmeans_niters": 20,
|
| 15 |
+
"resume": false,
|
| 16 |
+
"similarity": "cosine",
|
| 17 |
+
"bsize": 64,
|
| 18 |
+
"accumsteps": 1,
|
| 19 |
+
"lr": 1e-5,
|
| 20 |
+
"maxsteps": 400000,
|
| 21 |
+
"save_every": null,
|
| 22 |
+
"warmup": 20000,
|
| 23 |
+
"warmup_bert": null,
|
| 24 |
+
"relu": false,
|
| 25 |
+
"nway": 64,
|
| 26 |
+
"use_ib_negatives": true,
|
| 27 |
+
"reranker": false,
|
| 28 |
+
"distillation_alpha": 1.0,
|
| 29 |
+
"ignore_scores": false,
|
| 30 |
+
"model_name": null,
|
| 31 |
+
"query_maxlen": 32,
|
| 32 |
+
"attend_to_mask_tokens": false,
|
| 33 |
+
"interaction": "colbert",
|
| 34 |
+
"dim": 128,
|
| 35 |
+
"doc_maxlen": 256,
|
| 36 |
+
"mask_punctuation": true,
|
| 37 |
+
"checkpoint": "colbert-ir\/colbertv2.0",
|
| 38 |
+
"triples": "\/future\/u\/okhattab\/root\/unit\/experiments\/2021.10\/downstream.distillation.round2.2_score\/round2.nway6.cosine.ib\/examples.64.json",
|
| 39 |
+
"collection": [
|
| 40 |
+
"list with 1628 elements starting with...",
|
| 41 |
+
[
|
| 42 |
+
"Chapter 5 - Classification_Assessment of Projects Imports_ - Para 4 - _4. Clearance of goods after registration_.txt\n3.1 In terms of Regulation 4 of the Project Import Regulations, 1986 (PIR), the basic requirement for \navailing the benefit of assessment under Tariff Heading No. 98.01 is that the importer should have \nentered into one or more contracts with the su ppliers of the goods for setting up a project. Such \ncontracts should be registered, prior to clearance, in the Custom House through which the goods \nare expected to be imported. The importer shall apply for such registration in writing to the proper \nofficer of Customs.",
|
| 43 |
+
"Such \ncontracts should be registered, prior to clearance, in the Custom House through which the goods \nare expected to be imported. The importer shall apply for such registration in writing to the proper \nofficer of Customs. \n3.2 Regulation 5 provides in the manner of registering contracts, which is as follows: \n(i) Before any order is made by the proper officer of Customs permitting the clearance of the \ngoods for home consumption; \n(ii) In the case of goods cleared for hom e consumption without payment of duty subject to re -\nexport in respect of fairs, exhibitions, demonstrations, seminars, congresses and \nconferences, duly sponsored or approved by the Government of India or Trade fair Authority \nof India, as the case may be, b efore the date of payment of duty. \n3.3 To expedite registration, the importers are advised to submit the following documents along with \nthe application for registration: \n(i) Original deed of contract together with true copy thereof.",
|
| 44 |
+
"Chapter 5 - Classification_Assessment of Projects Imports_ - Para 4 - _4. Clearance of goods after registration_.txt\n(ii) Industrial License and letter of intent, SSI Certificate granted by the appropriate authority \nwith a copy thereof. \n \n(iii) Original Import licence, if any, with a list of items showing the dimensions, specifications, \nquantity, quality, value of each item duly attested by the Licensing Authority and a copy \nthereof."
|
| 45 |
+
]
|
| 46 |
+
],
|
| 47 |
+
"queries": "\/future\/u\/okhattab\/data\/MSMARCO\/queries.train.tsv",
|
| 48 |
+
"index_name": "cbseclass10index",
|
| 49 |
+
"overwrite": false,
|
| 50 |
+
"root": ".ragatouille\/",
|
| 51 |
+
"experiment": "colbert",
|
| 52 |
+
"index_root": null,
|
| 53 |
+
"name": "2024-08\/15\/14.23.27",
|
| 54 |
+
"rank": 0,
|
| 55 |
+
"nranks": 1,
|
| 56 |
+
"amp": true,
|
| 57 |
+
"gpus": 1,
|
| 58 |
+
"avoid_fork_if_possible": false
|
| 59 |
+
},
|
| 60 |
+
"num_chunks": 1,
|
| 61 |
+
"num_partitions": 4096,
|
| 62 |
+
"num_embeddings_est": 202637.00051879883,
|
| 63 |
+
"avg_doclen_est": 124.46990203857422
|
| 64 |
+
}
|
README.md
CHANGED
|
@@ -1,12 +1,13 @@
|
|
| 1 |
-
---
|
| 2 |
-
title:
|
| 3 |
-
emoji:
|
| 4 |
-
colorFrom:
|
| 5 |
-
colorTo: yellow
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version:
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned:
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
title: CHEERFUL CBSE-GyanQuizBot_Science_10
|
| 3 |
+
emoji: 📚
|
| 4 |
+
colorFrom: red
|
| 5 |
+
colorTo: yellow
|
| 6 |
+
sdk: gradio
|
| 7 |
+
sdk_version: 4.41.0
|
| 8 |
+
app_file: app.py
|
| 9 |
+
pinned: true
|
| 10 |
+
short_description: 10 standard students generate Quiz by themselves
|
| 11 |
+
---
|
| 12 |
+
|
| 13 |
+
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
app.py
ADDED
|
@@ -0,0 +1,393 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
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|
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|
|
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|
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|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
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|
|
|
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|
| 1 |
+
import gradio as gr
|
| 2 |
+
from pathlib import Path
|
| 3 |
+
from tempfile import NamedTemporaryFile
|
| 4 |
+
from sentence_transformers import CrossEncoder
|
| 5 |
+
import numpy as np
|
| 6 |
+
from time import perf_counter
|
| 7 |
+
import pandas as pd
|
| 8 |
+
from pydantic import BaseModel, Field
|
| 9 |
+
from phi.agent import Agent
|
| 10 |
+
from phi.model.groq import Groq
|
| 11 |
+
import os
|
| 12 |
+
import logging
|
| 13 |
+
|
| 14 |
+
# Set up logging
|
| 15 |
+
logging.basicConfig(level=logging.INFO)
|
| 16 |
+
logger = logging.getLogger(__name__)
|
| 17 |
+
|
| 18 |
+
# API Key setup
|
| 19 |
+
api_key = os.getenv("GROQ_API_KEY")
|
| 20 |
+
if not api_key:
|
| 21 |
+
gr.Warning("GROQ_API_KEY not found. Set it in 'Repository secrets'.")
|
| 22 |
+
logger.error("GROQ_API_KEY not found.")
|
| 23 |
+
else:
|
| 24 |
+
os.environ["GROQ_API_KEY"] = api_key
|
| 25 |
+
|
| 26 |
+
# Pydantic Model for Quiz Structure
|
| 27 |
+
class QuizItem(BaseModel):
|
| 28 |
+
question: str = Field(..., description="The quiz question")
|
| 29 |
+
choices: list[str] = Field(..., description="List of 4 multiple-choice options")
|
| 30 |
+
correct_answer: str = Field(..., description="The correct choice (e.g., 'C1')")
|
| 31 |
+
|
| 32 |
+
class QuizOutput(BaseModel):
|
| 33 |
+
items: list[QuizItem] = Field(..., description="List of 10 quiz items")
|
| 34 |
+
|
| 35 |
+
# Initialize Agents
|
| 36 |
+
groq_agent = Agent(model=Groq(model="llama3-70b-8192", api_key=api_key), markdown=True)
|
| 37 |
+
|
| 38 |
+
quiz_generator = Agent(
|
| 39 |
+
name="Quiz Generator",
|
| 40 |
+
role="Generates structured quiz questions and answers",
|
| 41 |
+
instructions=[
|
| 42 |
+
"Create 10 questions with 4 choices each based on the provided topic and documents.",
|
| 43 |
+
"Use the specified difficulty level (easy, average, hard) to adjust question complexity.",
|
| 44 |
+
"Ensure questions are derived only from the provided documents.",
|
| 45 |
+
"Return the output in a structured format using the QuizOutput Pydantic model.",
|
| 46 |
+
"Each question should have a unique correct answer from the choices (labeled C1, C2, C3, C4)."
|
| 47 |
+
],
|
| 48 |
+
model=Groq(id="llama3-70b-8192", api_key=api_key),
|
| 49 |
+
response_model=QuizOutput,
|
| 50 |
+
markdown=True
|
| 51 |
+
)
|
| 52 |
+
|
| 53 |
+
VECTOR_COLUMN_NAME = "vector"
|
| 54 |
+
TEXT_COLUMN_NAME = "text"
|
| 55 |
+
proj_dir = Path.cwd()
|
| 56 |
+
|
| 57 |
+
# Calling functions from backend (assuming they exist)
|
| 58 |
+
from backend.semantic_search import table, retriever
|
| 59 |
+
|
| 60 |
+
def generate_quiz_data(question_difficulty, topic, documents_str):
|
| 61 |
+
prompt = f"""Generate a quiz with {question_difficulty} difficulty on topic '{topic}' using only the following documents:\n{documents_str}"""
|
| 62 |
+
try:
|
| 63 |
+
response = quiz_generator.run(prompt)
|
| 64 |
+
return response.content
|
| 65 |
+
except Exception as e:
|
| 66 |
+
logger.error(f"Failed to generate quiz: {e}")
|
| 67 |
+
return None
|
| 68 |
+
|
| 69 |
+
def json_to_excel(quiz_data):
|
| 70 |
+
data = []
|
| 71 |
+
gr.Warning('Generating Shareable file link..', duration=30)
|
| 72 |
+
for i, item in enumerate(quiz_data.items, 1):
|
| 73 |
+
data.append([
|
| 74 |
+
item.question,
|
| 75 |
+
"Multiple Choice",
|
| 76 |
+
item.choices[0],
|
| 77 |
+
item.choices[1],
|
| 78 |
+
item.choices[2],
|
| 79 |
+
item.choices[3],
|
| 80 |
+
'', # Option 5 (empty)
|
| 81 |
+
item.correct_answer.replace('C', ''),
|
| 82 |
+
30,
|
| 83 |
+
''
|
| 84 |
+
])
|
| 85 |
+
df = pd.DataFrame(data, columns=[
|
| 86 |
+
"Question Text", "Question Type", "Option 1", "Option 2", "Option 3", "Option 4", "Option 5", "Correct Answer", "Time in seconds", "Image Link"
|
| 87 |
+
])
|
| 88 |
+
temp_file = NamedTemporaryFile(delete=True, suffix=".xlsx")
|
| 89 |
+
df.to_excel(temp_file.name, index=False)
|
| 90 |
+
return temp_file.name
|
| 91 |
+
|
| 92 |
+
colorful_theme = gr.themes.Default(primary_hue="cyan", secondary_hue="yellow", neutral_hue="purple")
|
| 93 |
+
|
| 94 |
+
with gr.Blocks(title="Quiz Maker", theme=colorful_theme) as QUIZBOT:
|
| 95 |
+
with gr.Row():
|
| 96 |
+
with gr.Column(scale=2):
|
| 97 |
+
gr.Image(value='logo.png', height=200, width=200)
|
| 98 |
+
with gr.Column(scale=6):
|
| 99 |
+
gr.HTML("""
|
| 100 |
+
<center>
|
| 101 |
+
<h1><span style="color: purple;">GOVERNMENT HIGH SCHOOL,SUTHUKENY</span> STUDENTS QUIZBOT </h1>
|
| 102 |
+
<h2>Generative AI-powered Capacity building for STUDENTS</h2>
|
| 103 |
+
<i>⚠️ Students can create quiz from any topic from 9th Science and evaluate themselves! ⚠️</i>
|
| 104 |
+
</center>
|
| 105 |
+
""")
|
| 106 |
+
|
| 107 |
+
topic = gr.Textbox(label="Enter the Topic for Quiz", placeholder="Write any topic/details from 9TH Science CBSE")
|
| 108 |
+
with gr.Row():
|
| 109 |
+
difficulty_radio = gr.Radio(["easy", "average", "hard"], label="How difficult should the quiz be?")
|
| 110 |
+
model_radio = gr.Radio(choices=['(ACCURATE) BGE reranker'], value='(ACCURATE) BGE reranker', label="Embeddings") # Removed ColBERT option
|
| 111 |
+
|
| 112 |
+
generate_quiz_btn = gr.Button("Generate Quiz!🚀")
|
| 113 |
+
quiz_msg = gr.Textbox(label="Status", interactive=False)
|
| 114 |
+
question_display = gr.HTML(visible=False)
|
| 115 |
+
download_excel = gr.File(label="Download Excel")
|
| 116 |
+
|
| 117 |
+
@generate_quiz_btn.click(inputs=[difficulty_radio, topic, model_radio], outputs=[quiz_msg, question_display, download_excel])
|
| 118 |
+
def generate_quiz(question_difficulty, topic, cross_encoder):
|
| 119 |
+
top_k_rank = 10
|
| 120 |
+
documents = []
|
| 121 |
+
gr.Warning('Generating Quiz may take 1-2 minutes. Please wait.', duration=60)
|
| 122 |
+
|
| 123 |
+
document_start = perf_counter()
|
| 124 |
+
query_vec = retriever.encode(topic)
|
| 125 |
+
documents = [doc[TEXT_COLUMN_NAME] for doc in table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank).to_list()]
|
| 126 |
+
if cross_encoder == '(ACCURATE) BGE reranker':
|
| 127 |
+
cross_encoder1 = CrossEncoder('BAAI/bge-reranker-base')
|
| 128 |
+
query_doc_pair = [[topic, doc] for doc in documents]
|
| 129 |
+
cross_scores = cross_encoder1.predict(query_doc_pair)
|
| 130 |
+
sim_scores_argsort = list(reversed(np.argsort(cross_scores)))
|
| 131 |
+
documents = [documents[idx] for idx in sim_scores_argsort[:top_k_rank]]
|
| 132 |
+
|
| 133 |
+
documents_str = '\n'.join(documents)
|
| 134 |
+
quiz_data = generate_quiz_data(question_difficulty, topic, documents_str)
|
| 135 |
+
if not quiz_data or not quiz_data.items:
|
| 136 |
+
return ["Error: Failed to generate quiz.", gr.HTML(visible=False), None]
|
| 137 |
+
|
| 138 |
+
excel_file = json_to_excel(quiz_data)
|
| 139 |
+
html_content = "<div>" + "".join(f"<h3>{i}. {item.question}</h3><p>{'<br>'.join(item.choices)}</p>" for i, item in enumerate(quiz_data.items[:10], 1)) + "</div>"
|
| 140 |
+
return ["Quiz Generated!", gr.HTML(value=html_content, visible=True), excel_file]
|
| 141 |
+
|
| 142 |
+
check_button = gr.Button("Check Score")
|
| 143 |
+
score_textbox = gr.Markdown()
|
| 144 |
+
|
| 145 |
+
@check_button.click(inputs=question_display, outputs=score_textbox)
|
| 146 |
+
def compare_answers(html_content):
|
| 147 |
+
if not quiz_data or not quiz_data.items:
|
| 148 |
+
return "Please generate a quiz first."
|
| 149 |
+
# Placeholder for user answers (adjust based on actual UI implementation)
|
| 150 |
+
user_answers = [] # Implement parsing logic if using radio inputs
|
| 151 |
+
correct_answers = [item.correct_answer for item in quiz_data.items[:10]]
|
| 152 |
+
score = sum(1 for u, c in zip(user_answers, correct_answers) if u == c)
|
| 153 |
+
if score > 7:
|
| 154 |
+
message = f"### Excellent! You got {score} out of 10!"
|
| 155 |
+
elif score > 5:
|
| 156 |
+
message = f"### Good! You got {score} out of 10!"
|
| 157 |
+
else:
|
| 158 |
+
message = f"### You got {score} out of 10! Don't worry. You can prepare well and try better next time!"
|
| 159 |
+
return message
|
| 160 |
+
|
| 161 |
+
if __name__ == "__main__":
|
| 162 |
+
QUIZBOT.queue().launch(debug=True)
|
| 163 |
+
|
| 164 |
+
# # Importing libraries
|
| 165 |
+
# import pandas as pd
|
| 166 |
+
# import json
|
| 167 |
+
# import gradio as gr
|
| 168 |
+
# from pathlib import Path
|
| 169 |
+
# from ragatouille import RAGPretrainedModel
|
| 170 |
+
# from gradio_client import Client
|
| 171 |
+
# from tempfile import NamedTemporaryFile
|
| 172 |
+
# from sentence_transformers import CrossEncoder
|
| 173 |
+
# import numpy as np
|
| 174 |
+
# from time import perf_counter
|
| 175 |
+
# from sentence_transformers import CrossEncoder
|
| 176 |
+
|
| 177 |
+
# #calling functions from other files - to call the knowledge database tables (lancedb for accurate mode) for creating quiz
|
| 178 |
+
# from backend.semantic_search import table, retriever
|
| 179 |
+
|
| 180 |
+
# VECTOR_COLUMN_NAME = "vector"
|
| 181 |
+
# TEXT_COLUMN_NAME = "text"
|
| 182 |
+
# proj_dir = Path.cwd()
|
| 183 |
+
|
| 184 |
+
# # Set up logging
|
| 185 |
+
# import logging
|
| 186 |
+
# logging.basicConfig(level=logging.INFO)
|
| 187 |
+
# logger = logging.getLogger(__name__)
|
| 188 |
+
|
| 189 |
+
# # Replace Mixtral client with Qwen Client
|
| 190 |
+
# client = Client("Qwen/Qwen1.5-110B-Chat-demo")
|
| 191 |
+
|
| 192 |
+
# def system_instructions(question_difficulty, topic, documents_str):
|
| 193 |
+
# return f"""<s> [INST] You are a great teacher and your task is to create 10 questions with 4 choices with {question_difficulty} difficulty about the topic request "{topic}" only from the below given documents, {documents_str}. Then create answers. Index in JSON format, the questions as "Q#":"" to "Q#":"", the four choices as "Q#:C1":"" to "Q#:C4":"", and the answers as "A#":"Q#:C#" to "A#":"Q#:C#". Example: 'A10':'Q10:C3' [/INST]"""
|
| 194 |
+
|
| 195 |
+
# # Ragatouille database for Colbert ie highly accurate mode
|
| 196 |
+
# RAG_db = gr.State()
|
| 197 |
+
# quiz_data = None
|
| 198 |
+
|
| 199 |
+
|
| 200 |
+
# #defining a function to convert json file to excel file
|
| 201 |
+
# def json_to_excel(output_json):
|
| 202 |
+
# # Initialize list for DataFrame
|
| 203 |
+
# data = []
|
| 204 |
+
# gr.Warning('Generating Shareable file link..', duration=30)
|
| 205 |
+
# for i in range(1, 11): # Assuming there are 10 questions
|
| 206 |
+
# question_key = f"Q{i}"
|
| 207 |
+
# answer_key = f"A{i}"
|
| 208 |
+
|
| 209 |
+
# question = output_json.get(question_key, '')
|
| 210 |
+
# correct_answer_key = output_json.get(answer_key, '')
|
| 211 |
+
# #correct_answer = correct_answer_key.split(':')[-1] if correct_answer_key else ''
|
| 212 |
+
# correct_answer = correct_answer_key.split(':')[-1].replace('C', '').strip() if correct_answer_key else ''
|
| 213 |
+
|
| 214 |
+
# # Extract options
|
| 215 |
+
# option_keys = [f"{question_key}:C{i}" for i in range(1, 6)]
|
| 216 |
+
# options = [output_json.get(key, '') for key in option_keys]
|
| 217 |
+
|
| 218 |
+
# # Add data row
|
| 219 |
+
# data.append([
|
| 220 |
+
# question, # Question Text
|
| 221 |
+
# "Multiple Choice", # Question Type
|
| 222 |
+
# options[0], # Option 1
|
| 223 |
+
# options[1], # Option 2
|
| 224 |
+
# options[2] if len(options) > 2 else '', # Option 3
|
| 225 |
+
# options[3] if len(options) > 3 else '', # Option 4
|
| 226 |
+
# options[4] if len(options) > 4 else '', # Option 5
|
| 227 |
+
# correct_answer, # Correct Answer
|
| 228 |
+
# 30, # Time in seconds
|
| 229 |
+
# '' # Image Link
|
| 230 |
+
# ])
|
| 231 |
+
|
| 232 |
+
# # Create DataFrame
|
| 233 |
+
# df = pd.DataFrame(data, columns=[
|
| 234 |
+
# "Question Text",
|
| 235 |
+
# "Question Type",
|
| 236 |
+
# "Option 1",
|
| 237 |
+
# "Option 2",
|
| 238 |
+
# "Option 3",
|
| 239 |
+
# "Option 4",
|
| 240 |
+
# "Option 5",
|
| 241 |
+
# "Correct Answer",
|
| 242 |
+
# "Time in seconds",
|
| 243 |
+
# "Image Link"
|
| 244 |
+
# ])
|
| 245 |
+
|
| 246 |
+
# temp_file = NamedTemporaryFile(delete=False, suffix=".xlsx")
|
| 247 |
+
# df.to_excel(temp_file.name, index=False)
|
| 248 |
+
# return temp_file.name
|
| 249 |
+
# # Define a colorful theme
|
| 250 |
+
# colorful_theme = gr.themes.Default(
|
| 251 |
+
# primary_hue="cyan", # Set a bright cyan as primary color
|
| 252 |
+
# secondary_hue="yellow", # Set a bright magenta as secondary color
|
| 253 |
+
# neutral_hue="purple" # Optionally set a neutral color
|
| 254 |
+
|
| 255 |
+
# )
|
| 256 |
+
|
| 257 |
+
# #gradio app creation for a user interface
|
| 258 |
+
# with gr.Blocks(title="Quiz Maker", theme=colorful_theme) as QUIZBOT:
|
| 259 |
+
|
| 260 |
+
|
| 261 |
+
# # Create a single row for the HTML and Image
|
| 262 |
+
# with gr.Row():
|
| 263 |
+
# with gr.Column(scale=2):
|
| 264 |
+
# gr.Image(value='logo.png', height=200, width=200)
|
| 265 |
+
# with gr.Column(scale=6):
|
| 266 |
+
# gr.HTML("""
|
| 267 |
+
# <center>
|
| 268 |
+
# <h1><span style="color: purple;">GOVERNMENT HIGH SCHOOL,SUTHUKENY</span> STUDENTS QUIZBOT </h1>
|
| 269 |
+
# <h2>Generative AI-powered Capacity building for STUDENTS</h2>
|
| 270 |
+
# <i>⚠️ Students can create quiz from any topic from 10 science and evaluate themselves! ⚠️</i>
|
| 271 |
+
# </center>
|
| 272 |
+
# """)
|
| 273 |
+
|
| 274 |
+
|
| 275 |
+
|
| 276 |
+
|
| 277 |
+
# topic = gr.Textbox(label="Enter the Topic for Quiz", placeholder="Write any CHAPTER NAME")
|
| 278 |
+
|
| 279 |
+
# with gr.Row():
|
| 280 |
+
# difficulty_radio = gr.Radio(["easy", "average", "hard"], label="How difficult should the quiz be?")
|
| 281 |
+
# model_radio = gr.Radio(choices=[ '(ACCURATE) BGE reranker', '(HIGH ACCURATE) ColBERT'],
|
| 282 |
+
# value='(ACCURATE) BGE reranker', label="Embeddings",
|
| 283 |
+
# info="First query to ColBERT may take a little time")
|
| 284 |
+
|
| 285 |
+
# generate_quiz_btn = gr.Button("Generate Quiz!🚀")
|
| 286 |
+
# quiz_msg = gr.Textbox()
|
| 287 |
+
|
| 288 |
+
# question_radios = [gr.Radio(visible=False) for _ in range(10)]
|
| 289 |
+
|
| 290 |
+
# @generate_quiz_btn.click(inputs=[difficulty_radio, topic, model_radio], outputs=[quiz_msg] + question_radios + [gr.File(label="Download Excel")])
|
| 291 |
+
# def generate_quiz(question_difficulty, topic, cross_encoder):
|
| 292 |
+
# top_k_rank = 10
|
| 293 |
+
# documents = []
|
| 294 |
+
# gr.Warning('Generating Quiz may take 1-2 minutes. Please wait.', duration=60)
|
| 295 |
+
|
| 296 |
+
# if cross_encoder == '(HIGH ACCURATE) ColBERT':
|
| 297 |
+
# gr.Warning('Retrieving using ColBERT.. First-time query will take 2 minute for model to load.. please wait',duration=100)
|
| 298 |
+
# RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
|
| 299 |
+
# RAG_db.value = RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index')
|
| 300 |
+
# documents_full = RAG_db.value.search(topic, k=top_k_rank)
|
| 301 |
+
# documents = [item['content'] for item in documents_full]
|
| 302 |
+
|
| 303 |
+
# else:
|
| 304 |
+
# document_start = perf_counter()
|
| 305 |
+
# query_vec = retriever.encode(topic)
|
| 306 |
+
# doc1 = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank)
|
| 307 |
+
|
| 308 |
+
# documents = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank).to_list()
|
| 309 |
+
# documents = [doc[TEXT_COLUMN_NAME] for doc in documents]
|
| 310 |
+
|
| 311 |
+
# query_doc_pair = [[topic, doc] for doc in documents]
|
| 312 |
+
|
| 313 |
+
# # if cross_encoder == '(FAST) MiniLM-L6v2':
|
| 314 |
+
# # cross_encoder1 = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
|
| 315 |
+
# if cross_encoder == '(ACCURATE) BGE reranker':
|
| 316 |
+
# cross_encoder1 = CrossEncoder('BAAI/bge-reranker-base')
|
| 317 |
+
|
| 318 |
+
# cross_scores = cross_encoder1.predict(query_doc_pair)
|
| 319 |
+
# sim_scores_argsort = list(reversed(np.argsort(cross_scores)))
|
| 320 |
+
# documents = [documents[idx] for idx in sim_scores_argsort[:top_k_rank]]
|
| 321 |
+
|
| 322 |
+
# #creating a text prompt to Qwen model combining the documents and system instruction
|
| 323 |
+
# formatted_prompt = system_instructions(question_difficulty, topic, '\n'.join(documents))
|
| 324 |
+
# print(' Formatted Prompt : ' ,formatted_prompt)
|
| 325 |
+
# try:
|
| 326 |
+
# response = client.predict(query=formatted_prompt, history=[], system="You are a helpful assistant.", api_name="/model_chat")
|
| 327 |
+
# response1 = response[1][0][1]
|
| 328 |
+
|
| 329 |
+
# # Extract JSON
|
| 330 |
+
# start_index = response1.find('{')
|
| 331 |
+
# end_index = response1.rfind('}')
|
| 332 |
+
# cleaned_response = response1[start_index:end_index + 1] if start_index != -1 and end_index != -1 else ''
|
| 333 |
+
# print('Cleaned Response :',cleaned_response)
|
| 334 |
+
# output_json = json.loads(cleaned_response)
|
| 335 |
+
# # Assign the extracted JSON to quiz_data for use in the comparison function
|
| 336 |
+
# global quiz_data
|
| 337 |
+
# quiz_data = output_json
|
| 338 |
+
# # Generate the Excel file
|
| 339 |
+
# excel_file = json_to_excel(output_json)
|
| 340 |
+
|
| 341 |
+
|
| 342 |
+
# #Create a Quiz display in app
|
| 343 |
+
# question_radio_list = []
|
| 344 |
+
# for question_num in range(1, 11):
|
| 345 |
+
# question_key = f"Q{question_num}"
|
| 346 |
+
# answer_key = f"A{question_num}"
|
| 347 |
+
|
| 348 |
+
# question = output_json.get(question_key)
|
| 349 |
+
# answer = output_json.get(output_json.get(answer_key))
|
| 350 |
+
|
| 351 |
+
# if not question or not answer:
|
| 352 |
+
# continue
|
| 353 |
+
|
| 354 |
+
# choice_keys = [f"{question_key}:C{i}" for i in range(1, 5)]
|
| 355 |
+
# choice_list = [output_json.get(choice_key, "Choice not found") for choice_key in choice_keys]
|
| 356 |
+
|
| 357 |
+
# radio = gr.Radio(choices=choice_list, label=question, visible=True, interactive=True)
|
| 358 |
+
# question_radio_list.append(radio)
|
| 359 |
+
|
| 360 |
+
# return ['Quiz Generated!'] + question_radio_list + [excel_file]
|
| 361 |
+
|
| 362 |
+
# except json.JSONDecodeError as e:
|
| 363 |
+
# print(f"Failed to decode JSON: {e}")
|
| 364 |
+
|
| 365 |
+
# check_button = gr.Button("Check Score")
|
| 366 |
+
# score_textbox = gr.Markdown()
|
| 367 |
+
|
| 368 |
+
# @check_button.click(inputs=question_radios, outputs=score_textbox)
|
| 369 |
+
# def compare_answers(*user_answers):
|
| 370 |
+
# user_answer_list = list(user_answers)
|
| 371 |
+
# answers_list = []
|
| 372 |
+
|
| 373 |
+
# for question_num in range(1, 11):
|
| 374 |
+
# answer_key = f"A{question_num}"
|
| 375 |
+
# answer = quiz_data.get(quiz_data.get(answer_key))
|
| 376 |
+
# if not answer:
|
| 377 |
+
# break
|
| 378 |
+
# answers_list.append(answer)
|
| 379 |
+
|
| 380 |
+
# score = sum(1 for item in user_answer_list if item in answers_list)
|
| 381 |
+
|
| 382 |
+
# if score > 7:
|
| 383 |
+
# message = f"### Excellent! You got {score} out of 10!"
|
| 384 |
+
# elif score > 5:
|
| 385 |
+
# message = f"### Good! You got {score} out of 10!"
|
| 386 |
+
# else:
|
| 387 |
+
# message = f"### You got {score} out of 10! Don't worry. You can prepare well and try better next time!"
|
| 388 |
+
|
| 389 |
+
# return message
|
| 390 |
+
|
| 391 |
+
# QUIZBOT.queue()
|
| 392 |
+
# QUIZBOT.launch(debug=True)
|
| 393 |
+
|
backend/query_llm.py
ADDED
|
@@ -0,0 +1,161 @@
|
|
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|
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|
|
|
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|
|
|
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|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
|
| 2 |
+
|
| 3 |
+
import openai
|
| 4 |
+
import gradio as gr
|
| 5 |
+
|
| 6 |
+
from os import getenv
|
| 7 |
+
from typing import Any, Dict, Generator, List
|
| 8 |
+
|
| 9 |
+
from huggingface_hub import InferenceClient
|
| 10 |
+
from transformers import AutoTokenizer
|
| 11 |
+
|
| 12 |
+
#tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
|
| 13 |
+
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1")
|
| 14 |
+
#tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x22B-Instruct-v0.1")
|
| 15 |
+
temperature = 0.5
|
| 16 |
+
top_p = 0.7
|
| 17 |
+
repetition_penalty = 1.2
|
| 18 |
+
|
| 19 |
+
OPENAI_KEY = getenv("OPENAI_API_KEY")
|
| 20 |
+
HF_TOKEN = getenv("HUGGING_FACE_HUB_TOKEN")
|
| 21 |
+
|
| 22 |
+
# hf_client = InferenceClient(
|
| 23 |
+
# "mistralai/Mistral-7B-Instruct-v0.1",
|
| 24 |
+
# token=HF_TOKEN
|
| 25 |
+
# )
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
hf_client = InferenceClient(
|
| 29 |
+
"mistralai/Mixtral-8x7B-Instruct-v0.1",
|
| 30 |
+
token=HF_TOKEN
|
| 31 |
+
)
|
| 32 |
+
def format_prompt(message: str, api_kind: str):
|
| 33 |
+
"""
|
| 34 |
+
Formats the given message using a chat template.
|
| 35 |
+
|
| 36 |
+
Args:
|
| 37 |
+
message (str): The user message to be formatted.
|
| 38 |
+
|
| 39 |
+
Returns:
|
| 40 |
+
str: Formatted message after applying the chat template.
|
| 41 |
+
"""
|
| 42 |
+
|
| 43 |
+
# Create a list of message dictionaries with role and content
|
| 44 |
+
messages: List[Dict[str, Any]] = [{'role': 'user', 'content': message}]
|
| 45 |
+
|
| 46 |
+
if api_kind == "openai":
|
| 47 |
+
return messages
|
| 48 |
+
elif api_kind == "hf":
|
| 49 |
+
return tokenizer.apply_chat_template(messages, tokenize=False)
|
| 50 |
+
elif api_kind:
|
| 51 |
+
raise ValueError("API is not supported")
|
| 52 |
+
|
| 53 |
+
|
| 54 |
+
def generate_hf(prompt: str, history: str, temperature: float = 0.5, max_new_tokens: int = 4000,
|
| 55 |
+
top_p: float = 0.95, repetition_penalty: float = 1.0) -> Generator[str, None, str]:
|
| 56 |
+
"""
|
| 57 |
+
Generate a sequence of tokens based on a given prompt and history using Mistral client.
|
| 58 |
+
|
| 59 |
+
Args:
|
| 60 |
+
prompt (str): The initial prompt for the text generation.
|
| 61 |
+
history (str): Context or history for the text generation.
|
| 62 |
+
temperature (float, optional): The softmax temperature for sampling. Defaults to 0.9.
|
| 63 |
+
max_new_tokens (int, optional): Maximum number of tokens to be generated. Defaults to 256.
|
| 64 |
+
top_p (float, optional): Nucleus sampling probability. Defaults to 0.95.
|
| 65 |
+
repetition_penalty (float, optional): Penalty for repeated tokens. Defaults to 1.0.
|
| 66 |
+
|
| 67 |
+
Returns:
|
| 68 |
+
Generator[str, None, str]: A generator yielding chunks of generated text.
|
| 69 |
+
Returns a final string if an error occurs.
|
| 70 |
+
"""
|
| 71 |
+
|
| 72 |
+
temperature = max(float(temperature), 1e-2) # Ensure temperature isn't too low
|
| 73 |
+
top_p = float(top_p)
|
| 74 |
+
|
| 75 |
+
generate_kwargs = {
|
| 76 |
+
'temperature': temperature,
|
| 77 |
+
'max_new_tokens': max_new_tokens,
|
| 78 |
+
'top_p': top_p,
|
| 79 |
+
'repetition_penalty': repetition_penalty,
|
| 80 |
+
'do_sample': True,
|
| 81 |
+
'seed': 42,
|
| 82 |
+
}
|
| 83 |
+
|
| 84 |
+
formatted_prompt = format_prompt(prompt, "hf")
|
| 85 |
+
|
| 86 |
+
try:
|
| 87 |
+
stream = hf_client.text_generation(formatted_prompt, **generate_kwargs,
|
| 88 |
+
stream=True, details=True, return_full_text=False)
|
| 89 |
+
output = ""
|
| 90 |
+
for response in stream:
|
| 91 |
+
output += response.token.text
|
| 92 |
+
yield output
|
| 93 |
+
|
| 94 |
+
except Exception as e:
|
| 95 |
+
if "Too Many Requests" in str(e):
|
| 96 |
+
print("ERROR: Too many requests on Mistral client")
|
| 97 |
+
gr.Warning("Unfortunately Mistral is unable to process")
|
| 98 |
+
return "Unfortunately, I am not able to process your request now."
|
| 99 |
+
elif "Authorization header is invalid" in str(e):
|
| 100 |
+
print("Authetification error:", str(e))
|
| 101 |
+
gr.Warning("Authentication error: HF token was either not provided or incorrect")
|
| 102 |
+
return "Authentication error"
|
| 103 |
+
else:
|
| 104 |
+
print("Unhandled Exception:", str(e))
|
| 105 |
+
gr.Warning("Unfortunately Mistral is unable to process")
|
| 106 |
+
return "I do not know what happened, but I couldn't understand you."
|
| 107 |
+
|
| 108 |
+
|
| 109 |
+
def generate_openai(prompt: str, history: str, temperature: float = 0.9, max_new_tokens: int = 256,
|
| 110 |
+
top_p: float = 0.95, repetition_penalty: float = 1.0) -> Generator[str, None, str]:
|
| 111 |
+
"""
|
| 112 |
+
Generate a sequence of tokens based on a given prompt and history using Mistral client.
|
| 113 |
+
|
| 114 |
+
Args:
|
| 115 |
+
prompt (str): The initial prompt for the text generation.
|
| 116 |
+
history (str): Context or history for the text generation.
|
| 117 |
+
temperature (float, optional): The softmax temperature for sampling. Defaults to 0.9.
|
| 118 |
+
max_new_tokens (int, optional): Maximum number of tokens to be generated. Defaults to 256.
|
| 119 |
+
top_p (float, optional): Nucleus sampling probability. Defaults to 0.95.
|
| 120 |
+
repetition_penalty (float, optional): Penalty for repeated tokens. Defaults to 1.0.
|
| 121 |
+
|
| 122 |
+
Returns:
|
| 123 |
+
Generator[str, None, str]: A generator yielding chunks of generated text.
|
| 124 |
+
Returns a final string if an error occurs.
|
| 125 |
+
"""
|
| 126 |
+
|
| 127 |
+
temperature = max(float(temperature), 1e-2) # Ensure temperature isn't too low
|
| 128 |
+
top_p = float(top_p)
|
| 129 |
+
|
| 130 |
+
generate_kwargs = {
|
| 131 |
+
'temperature': temperature,
|
| 132 |
+
'max_tokens': max_new_tokens,
|
| 133 |
+
'top_p': top_p,
|
| 134 |
+
'frequency_penalty': max(-2., min(repetition_penalty, 2.)),
|
| 135 |
+
}
|
| 136 |
+
|
| 137 |
+
formatted_prompt = format_prompt(prompt, "openai")
|
| 138 |
+
|
| 139 |
+
try:
|
| 140 |
+
stream = openai.ChatCompletion.create(model="gpt-3.5-turbo-0301",
|
| 141 |
+
messages=formatted_prompt,
|
| 142 |
+
**generate_kwargs,
|
| 143 |
+
stream=True)
|
| 144 |
+
output = ""
|
| 145 |
+
for chunk in stream:
|
| 146 |
+
output += chunk.choices[0].delta.get("content", "")
|
| 147 |
+
yield output
|
| 148 |
+
|
| 149 |
+
except Exception as e:
|
| 150 |
+
if "Too Many Requests" in str(e):
|
| 151 |
+
print("ERROR: Too many requests on OpenAI client")
|
| 152 |
+
gr.Warning("Unfortunately OpenAI is unable to process")
|
| 153 |
+
return "Unfortunately, I am not able to process your request now."
|
| 154 |
+
elif "You didn't provide an API key" in str(e):
|
| 155 |
+
print("Authetification error:", str(e))
|
| 156 |
+
gr.Warning("Authentication error: OpenAI key was either not provided or incorrect")
|
| 157 |
+
return "Authentication error"
|
| 158 |
+
else:
|
| 159 |
+
print("Unhandled Exception:", str(e))
|
| 160 |
+
gr.Warning("Unfortunately OpenAI is unable to process")
|
| 161 |
+
return "I do not know what happened, but I couldn't understand you."
|
backend/semantic_search.py
ADDED
|
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
|
| 2 |
+
import logging
|
| 3 |
+
import lancedb
|
| 4 |
+
import os
|
| 5 |
+
from pathlib import Path
|
| 6 |
+
from sentence_transformers import SentenceTransformer
|
| 7 |
+
#from FlagEmbedding import LLMEmbedder, FlagReranker # Al document present here https://github.com/FlagOpen/FlagEmbedding/tree/master
|
| 8 |
+
#EMB_MODEL_NAME = "thenlper/gte-base"
|
| 9 |
+
EMB_MODEL_NAME = 'BAAI/llm-embedder'
|
| 10 |
+
task = "qa" # Encode for a specific task (qa, icl, chat, lrlm, tool, convsearch)
|
| 11 |
+
#EMB_MODEL_NAME = LLMEmbedder('BAAI/llm-embedder', use_fp16=False) # Load model (automatically use GPUs)
|
| 12 |
+
|
| 13 |
+
#reranker_model = FlagReranker('BAAI/bge-reranker-base', use_fp16=True) # use_fp16 speeds up computation with a slight performance degradation
|
| 14 |
+
|
| 15 |
+
|
| 16 |
+
#EMB_MODEL_NAME = "thenlper/gte-base"
|
| 17 |
+
#DB_TABLE_NAME = "Huggingface_docs"
|
| 18 |
+
DB_TABLE_NAME = "cbse"
|
| 19 |
+
# Setting up the logging
|
| 20 |
+
logging.basicConfig(level=logging.INFO)
|
| 21 |
+
logger = logging.getLogger(__name__)
|
| 22 |
+
retriever = SentenceTransformer(EMB_MODEL_NAME)
|
| 23 |
+
|
| 24 |
+
# db
|
| 25 |
+
db_uri = os.path.join(Path(__file__).parents[1], "lancedb")
|
| 26 |
+
print(f'DB URL is {db_uri}')
|
| 27 |
+
db = lancedb.connect(db_uri)
|
| 28 |
+
table = db.open_table(DB_TABLE_NAME)
|
lancedb/cbse.lance/_latest.manifest
ADDED
|
Binary file (236 Bytes). View file
|
|
|
lancedb/cbse.lance/_transactions/0-bf8e63bb-3e01-448f-95c6-f20996e7415f.txn
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
$bf8e63bb-3e01-448f-95c6-f20996e7415f�U text ���������*string084vector ���������*fixed_size_list:float:76808
|
lancedb/cbse.lance/_transactions/1-76260358-b577-4610-83d2-64fb3c14ebc6.txn
ADDED
|
Binary file (98 Bytes). View file
|
|
|
lancedb/cbse.lance/_versions/1.manifest
ADDED
|
Binary file (181 Bytes). View file
|
|
|
lancedb/cbse.lance/_versions/2.manifest
ADDED
|
Binary file (236 Bytes). View file
|
|
|
lancedb/cbse.lance/data/615853d2-bbd1-453c-88c7-8df0edb43e5e.lance
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1611699c7fdfa82b6674466912c66b6b5dbf1f21fba597cf5fc01381aacb828b
|
| 3 |
+
size 16009653
|
logo.png
ADDED
|
requirements.txt
ADDED
|
@@ -0,0 +1,12 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
|
| 2 |
+
sentence-transformers==2.6.1
|
| 3 |
+
firebase_admin
|
| 4 |
+
huggingface-hub==0.19.3
|
| 5 |
+
spaces
|
| 6 |
+
ragatouille
|
| 7 |
+
transformers
|
| 8 |
+
lancedb
|
| 9 |
+
gradio==4.26.0
|
| 10 |
+
openpyxl
|
| 11 |
+
phidata
|
| 12 |
+
groq
|
templates/template.j2
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
Instructions: You are assistant to Customs officers .You have to answer their questions and tasks asked by them.Use the following information in the Context section to answer the Query at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
|
| 2 |
+
Context:
|
| 3 |
+
{% for doc in documents %}
|
| 4 |
+
---
|
| 5 |
+
{{ doc }}
|
| 6 |
+
{% endfor %}
|
| 7 |
+
---
|
| 8 |
+
Query: {{ query }}
|
templates/template_html.j2
ADDED
|
@@ -0,0 +1,103 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
<!DOCTYPE html>
|
| 2 |
+
<html lang="en">
|
| 3 |
+
<head>
|
| 4 |
+
<meta charset="UTF-8">
|
| 5 |
+
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
| 6 |
+
<title>Information Page</title>
|
| 7 |
+
<link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@400;600&display=swap">
|
| 8 |
+
<link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;600&display=swap">
|
| 9 |
+
<style>
|
| 10 |
+
* {
|
| 11 |
+
font-family: "Source Sans Pro";
|
| 12 |
+
}
|
| 13 |
+
|
| 14 |
+
.instructions > * {
|
| 15 |
+
color: #111 !important;
|
| 16 |
+
}
|
| 17 |
+
|
| 18 |
+
details.doc-box * {
|
| 19 |
+
color: #111 !important;
|
| 20 |
+
}
|
| 21 |
+
|
| 22 |
+
.dark {
|
| 23 |
+
background: #111;
|
| 24 |
+
color: white;
|
| 25 |
+
}
|
| 26 |
+
|
| 27 |
+
.doc-box {
|
| 28 |
+
padding: 10px;
|
| 29 |
+
margin-top: 10px;
|
| 30 |
+
background-color: #baecc2;
|
| 31 |
+
border-radius: 6px;
|
| 32 |
+
color: #111 !important;
|
| 33 |
+
max-width: 700px;
|
| 34 |
+
box-shadow: rgba(0, 0, 0, 0.2) 0px 1px 2px 0px;
|
| 35 |
+
}
|
| 36 |
+
|
| 37 |
+
.doc-full {
|
| 38 |
+
margin: 10px 14px;
|
| 39 |
+
line-height: 1.6rem;
|
| 40 |
+
}
|
| 41 |
+
|
| 42 |
+
.instructions {
|
| 43 |
+
color: #111 !important;
|
| 44 |
+
background: #b7bdfd;
|
| 45 |
+
display: block;
|
| 46 |
+
border-radius: 6px;
|
| 47 |
+
padding: 6px 10px;
|
| 48 |
+
line-height: 1.6rem;
|
| 49 |
+
max-width: 700px;
|
| 50 |
+
box-shadow: rgba(0, 0, 0, 0.2) 0px 1px 2px 0px;
|
| 51 |
+
}
|
| 52 |
+
|
| 53 |
+
.query {
|
| 54 |
+
color: #111 !important;
|
| 55 |
+
background: #ffbcbc;
|
| 56 |
+
display: block;
|
| 57 |
+
border-radius: 6px;
|
| 58 |
+
padding: 6px 10px;
|
| 59 |
+
line-height: 1.6rem;
|
| 60 |
+
max-width: 700px;
|
| 61 |
+
box-shadow: rgba(0, 0, 0, 0.2) 0px 1px 2px 0px;
|
| 62 |
+
}
|
| 63 |
+
</style>
|
| 64 |
+
</head>
|
| 65 |
+
<body>
|
| 66 |
+
<div class="prose svelte-1ybaih5" id="component-6">
|
| 67 |
+
<h2>Prompt</h2>
|
| 68 |
+
Below is the prompt that is given to the model. <hr>
|
| 69 |
+
<h2>Instructions</h2>
|
| 70 |
+
<span class="instructions">You are assistant to Customs officers .You have to answer their questions and tasks asked by them.Use the following information in the Context section to answer the Query at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
|
| 71 |
+
<span style="font-weight: bold;">don't try to make up an answer.</span></span><br>
|
| 72 |
+
<h2>Context</h2>
|
| 73 |
+
{% for doc in documents %}
|
| 74 |
+
<details class="doc-box">
|
| 75 |
+
<summary>
|
| 76 |
+
<b>Doc {{ loop.index }}:</b> <span class="doc-short">{{ doc[:100] }}...</span>
|
| 77 |
+
</summary>
|
| 78 |
+
<div class="doc-full">{{ doc }}</div>
|
| 79 |
+
</details>
|
| 80 |
+
{% endfor %}
|
| 81 |
+
|
| 82 |
+
<h2>Query</h2>
|
| 83 |
+
<span class="query">{{ query }}</span>
|
| 84 |
+
</div>
|
| 85 |
+
|
| 86 |
+
<script>
|
| 87 |
+
document.addEventListener("DOMContentLoaded", function() {
|
| 88 |
+
const detailsElements = document.querySelectorAll('.doc-box');
|
| 89 |
+
|
| 90 |
+
detailsElements.forEach(detail => {
|
| 91 |
+
detail.addEventListener('toggle', function() {
|
| 92 |
+
const docShort = this.querySelector('.doc-short');
|
| 93 |
+
if (this.open) {
|
| 94 |
+
docShort.style.display = 'none';
|
| 95 |
+
} else {
|
| 96 |
+
docShort.style.display = 'inline';
|
| 97 |
+
}
|
| 98 |
+
});
|
| 99 |
+
});
|
| 100 |
+
});
|
| 101 |
+
</script>
|
| 102 |
+
</body>
|
| 103 |
+
</html>
|