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+ "1627":"17dc8801-a1dc-486f-8ebf-814bc37467fb"
1630
+ }
.ragatouille/colbert/indexes/cbseclass10index/plan.json ADDED
@@ -0,0 +1,64 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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": 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 +1,12 @@
1
- ---
2
- title: QUIZBOT SCIENCE
3
- emoji: 😻
4
- colorFrom: red
5
- colorTo: red
6
- sdk: gradio
7
- sdk_version: 4.44.0
8
- app_file: app.py
9
- pinned: false
10
- ---
11
-
12
- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
1
+ ---
2
+ title: Adwitiya Quizbot 2.0
3
+ emoji: 📚
4
+ colorFrom: red
5
+ colorTo: indigo
6
+ sdk: gradio
7
+ sdk_version: 4.41.0
8
+ app_file: app.py
9
+ pinned: false
10
+ ---
11
+
12
+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
@@ -0,0 +1,230 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Importing libraries
2
+ import pandas as pd
3
+ import json
4
+ import gradio as gr
5
+ from pathlib import Path
6
+ from ragatouille import RAGPretrainedModel
7
+ from gradio_client import Client
8
+ from tempfile import NamedTemporaryFile
9
+ from sentence_transformers import CrossEncoder
10
+ import numpy as np
11
+ from time import perf_counter
12
+ from sentence_transformers import CrossEncoder
13
+
14
+ #calling functions from other files - to call the knowledge database tables (lancedb for accurate mode) for creating quiz
15
+ from backend.semantic_search import table, retriever
16
+
17
+ VECTOR_COLUMN_NAME = "vector"
18
+ TEXT_COLUMN_NAME = "text"
19
+ proj_dir = Path.cwd()
20
+
21
+ # Set up logging
22
+ import logging
23
+ logging.basicConfig(level=logging.INFO)
24
+ logger = logging.getLogger(__name__)
25
+
26
+ # Replace Mixtral client with Qwen Client
27
+ client = Client("Qwen/Qwen1.5-110B-Chat-demo")
28
+
29
+ def system_instructions(question_difficulty, topic, documents_str):
30
+ 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]"""
31
+
32
+ # Ragatouille database for Colbert ie highly accurate mode
33
+ RAG_db = gr.State()
34
+ quiz_data = None
35
+
36
+
37
+ #defining a function to convert json file to excel file
38
+ def json_to_excel(output_json):
39
+ # Initialize list for DataFrame
40
+ data = []
41
+ gr.Warning('Generating Shareable file link..', duration=30)
42
+ for i in range(1, 11): # Assuming there are 10 questions
43
+ question_key = f"Q{i}"
44
+ answer_key = f"A{i}"
45
+
46
+ question = output_json.get(question_key, '')
47
+ correct_answer_key = output_json.get(answer_key, '')
48
+ #correct_answer = correct_answer_key.split(':')[-1] if correct_answer_key else ''
49
+ correct_answer = correct_answer_key.split(':')[-1].replace('C', '').strip() if correct_answer_key else ''
50
+
51
+ # Extract options
52
+ option_keys = [f"{question_key}:C{i}" for i in range(1, 6)]
53
+ options = [output_json.get(key, '') for key in option_keys]
54
+
55
+ # Add data row
56
+ data.append([
57
+ question, # Question Text
58
+ "Multiple Choice", # Question Type
59
+ options[0], # Option 1
60
+ options[1], # Option 2
61
+ options[2] if len(options) > 2 else '', # Option 3
62
+ options[3] if len(options) > 3 else '', # Option 4
63
+ options[4] if len(options) > 4 else '', # Option 5
64
+ correct_answer, # Correct Answer
65
+ 30, # Time in seconds
66
+ '' # Image Link
67
+ ])
68
+
69
+ # Create DataFrame
70
+ df = pd.DataFrame(data, columns=[
71
+ "Question Text",
72
+ "Question Type",
73
+ "Option 1",
74
+ "Option 2",
75
+ "Option 3",
76
+ "Option 4",
77
+ "Option 5",
78
+ "Correct Answer",
79
+ "Time in seconds",
80
+ "Image Link"
81
+ ])
82
+
83
+ temp_file = NamedTemporaryFile(delete=False, suffix=".xlsx")
84
+ df.to_excel(temp_file.name, index=False)
85
+ return temp_file.name
86
+ # Define a colorful theme
87
+ colorful_theme = gr.themes.Default(
88
+ primary_hue="cyan", # Set a bright cyan as primary color
89
+ secondary_hue="yellow", # Set a bright magenta as secondary color
90
+ neutral_hue="purple" # Optionally set a neutral color
91
+
92
+ )
93
+
94
+ #gradio app creation for a user interface
95
+ with gr.Blocks(title="Quiz Maker", theme=colorful_theme) as QUIZBOT:
96
+
97
+
98
+ # Create a single row for the HTML and Image
99
+ with gr.Row():
100
+ with gr.Column(scale=2):
101
+ gr.Image(value='logo.png', height=200, width=200)
102
+ with gr.Column(scale=6):
103
+ gr.HTML("""
104
+ <center>
105
+ <h1><span style="color: purple;">GOVERNMENT HIGH SCHOOL,SUTHUKENY</span>STUDENTS QUIZBOT</h1>
106
+ <h2>Generative AI-powered Capacity building for STUDENTS</h2>
107
+ <i>⚠️ NACIN Faculties create quiz from any topic dynamically for classroom evaluation after their sessions ! ⚠️</i>
108
+ </center>
109
+ """)
110
+
111
+
112
+
113
+
114
+ topic = gr.Textbox(label="Enter the Topic for Quiz", placeholder="Write any topic/details from Customs Manual")
115
+
116
+ with gr.Row():
117
+ difficulty_radio = gr.Radio(["easy", "average", "hard"], label="How difficult should the quiz be?")
118
+ model_radio = gr.Radio(choices=[ '(ACCURATE) BGE reranker', '(HIGH ACCURATE) ColBERT'],
119
+ value='(ACCURATE) BGE reranker', label="Embeddings",
120
+ info="First query to ColBERT may take a little time")
121
+
122
+ generate_quiz_btn = gr.Button("Generate Quiz!🚀")
123
+ quiz_msg = gr.Textbox()
124
+
125
+ question_radios = [gr.Radio(visible=False) for _ in range(10)]
126
+
127
+ @generate_quiz_btn.click(inputs=[difficulty_radio, topic, model_radio], outputs=[quiz_msg] + question_radios + [gr.File(label="Download Excel")])
128
+ def generate_quiz(question_difficulty, topic, cross_encoder):
129
+ top_k_rank = 10
130
+ documents = []
131
+ gr.Warning('Generating Quiz may take 1-2 minutes. Please wait.', duration=60)
132
+
133
+ if cross_encoder == '(HIGH ACCURATE) ColBERT':
134
+ gr.Warning('Retrieving using ColBERT.. First-time query will take 2 minute for model to load.. please wait',duration=100)
135
+ RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
136
+ RAG_db.value = RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index')
137
+ documents_full = RAG_db.value.search(topic, k=top_k_rank)
138
+ documents = [item['content'] for item in documents_full]
139
+
140
+ else:
141
+ document_start = perf_counter()
142
+ query_vec = retriever.encode(topic)
143
+ doc1 = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank)
144
+
145
+ documents = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank).to_list()
146
+ documents = [doc[TEXT_COLUMN_NAME] for doc in documents]
147
+
148
+ query_doc_pair = [[topic, doc] for doc in documents]
149
+
150
+ # if cross_encoder == '(FAST) MiniLM-L6v2':
151
+ # cross_encoder1 = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
152
+ if cross_encoder == '(ACCURATE) BGE reranker':
153
+ cross_encoder1 = CrossEncoder('BAAI/bge-reranker-base')
154
+
155
+ cross_scores = cross_encoder1.predict(query_doc_pair)
156
+ sim_scores_argsort = list(reversed(np.argsort(cross_scores)))
157
+ documents = [documents[idx] for idx in sim_scores_argsort[:top_k_rank]]
158
+
159
+ #creating a text prompt to Qwen model combining the documents and system instruction
160
+ formatted_prompt = system_instructions(question_difficulty, topic, '\n'.join(documents))
161
+ print(' Formatted Prompt : ' ,formatted_prompt)
162
+ try:
163
+ response = client.predict(query=formatted_prompt, history=[], system="You are a helpful assistant.", api_name="/model_chat")
164
+ response1 = response[1][0][1]
165
+
166
+ # Extract JSON
167
+ start_index = response1.find('{')
168
+ end_index = response1.rfind('}')
169
+ cleaned_response = response1[start_index:end_index + 1] if start_index != -1 and end_index != -1 else ''
170
+ print('Cleaned Response :',cleaned_response)
171
+ output_json = json.loads(cleaned_response)
172
+ # Assign the extracted JSON to quiz_data for use in the comparison function
173
+ global quiz_data
174
+ quiz_data = output_json
175
+ # Generate the Excel file
176
+ excel_file = json_to_excel(output_json)
177
+
178
+
179
+ #Create a Quiz display in app
180
+ question_radio_list = []
181
+ for question_num in range(1, 11):
182
+ question_key = f"Q{question_num}"
183
+ answer_key = f"A{question_num}"
184
+
185
+ question = output_json.get(question_key)
186
+ answer = output_json.get(output_json.get(answer_key))
187
+
188
+ if not question or not answer:
189
+ continue
190
+
191
+ choice_keys = [f"{question_key}:C{i}" for i in range(1, 5)]
192
+ choice_list = [output_json.get(choice_key, "Choice not found") for choice_key in choice_keys]
193
+
194
+ radio = gr.Radio(choices=choice_list, label=question, visible=True, interactive=True)
195
+ question_radio_list.append(radio)
196
+
197
+ return ['Quiz Generated!'] + question_radio_list + [excel_file]
198
+
199
+ except json.JSONDecodeError as e:
200
+ print(f"Failed to decode JSON: {e}")
201
+
202
+ check_button = gr.Button("Check Score")
203
+ score_textbox = gr.Markdown()
204
+
205
+ @check_button.click(inputs=question_radios, outputs=score_textbox)
206
+ def compare_answers(*user_answers):
207
+ user_answer_list = list(user_answers)
208
+ answers_list = []
209
+
210
+ for question_num in range(1, 20):
211
+ answer_key = f"A{question_num}"
212
+ answer = quiz_data.get(quiz_data.get(answer_key))
213
+ if not answer:
214
+ break
215
+ answers_list.append(answer)
216
+
217
+ score = sum(1 for item in user_answer_list if item in answers_list)
218
+
219
+ if score > 7:
220
+ message = f"### Excellent! You got {score} out of 10!"
221
+ elif score > 5:
222
+ message = f"### Good! You got {score} out of 10!"
223
+ else:
224
+ message = f"### You got {score} out of 10! Don't worry. You can prepare well and try better next time!"
225
+
226
+ return message
227
+
228
+ QUIZBOT.queue()
229
+ QUIZBOT.launch(debug=True)
230
+
backend/query_llm.py ADDED
@@ -0,0 +1,161 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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)
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1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1611699c7fdfa82b6674466912c66b6b5dbf1f21fba597cf5fc01381aacb828b
3
+ size 16009653
logo.png ADDED
requirements.txt ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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
10
+ openpyxl
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&amp;display=swap">
8
+ <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;600&amp;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>