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Update app.py
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app.py
CHANGED
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import gradio as gr
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from huggingface_hub import InferenceClient
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import pandas as pd
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import torch
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"""
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client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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def respond(
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message,
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temperature = 0.7,
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top_p = 0.95,
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):
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messages = [{"role": "system", "content": "You are a moslem bot that always give answer based on quran and hadith!"}]
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df = pd.read_csv("moslem-bot-reference.csv")
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for val in history:
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if val[0]:
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messages.append({"role": "user", "content": val[0]})
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if val[1]:
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messages.append({"role": "assistant", "content": val[1]})
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messages.append({"role": "user", "content": "I want you to answer strictly based on quran and hadith"})
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messages.append({"role": "assistant", "content": "I'd be happy to help! Please go ahead and provide the sentence you'd like me to analyze. Please specify whether you're referencing a particular verse or hadith (Prophetic tradition) from the Quran or Hadith, or if you're asking me to analyze a general statement."})
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for index, row in df.iterrows():
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messages.append({"role": "user", "content": row['user']})
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messages.append({"role": "assistant", "content": row['assistant']})
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messages.append({"role": "user", "content": message})
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response = ""
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@@ -92,6 +118,5 @@ demo = gr.ChatInterface(
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],
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)
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if __name__ == "__main__":
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demo.launch()
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import gradio as gr
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from sentence_transformers import SentenceTransformer
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from huggingface_hub import InferenceClient
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import pandas as pd
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import torch
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"""
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client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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def get_detailed_instruct(task_description: str, query: str) -> str:
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return f'Instruct: {task_description}\nQuery: {query}'
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def respond(
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message,
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temperature = 0.7,
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top_p = 0.95,
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):
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#system role
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messages = [{"role": "system", "content": "You are a moslem bot that always give answer based on quran and hadith!"}]
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#make a moslem bot
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messages.append({"role": "user", "content": "I want you to answer strictly based on quran and hadith"})
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messages.append({"role": "assistant", "content": "I'd be happy to help! Please go ahead and provide the sentence you'd like me to analyze. Please specify whether you're referencing a particular verse or hadith (Prophetic tradition) from the Quran or Hadith, or if you're asking me to analyze a general statement."})
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#adding references
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df = pd.read_csv("moslem-bot-reference.csv")
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for index, row in df.iterrows():
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messages.append({"role": "user", "content": row['user']})
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messages.append({"role": "assistant", "content": row['assistant']})
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#adding more references
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selected_references = torch.load('selected_references.sav', map_location=torch.device('cpu'))
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encoded_questions = torch.load('encoded_questions.sav', map_location=torch.device('cpu'))
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task = 'Given a web search query, retrieve relevant passages that answer the query'
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queries = [
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get_detailed_instruct(task, message)
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]
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model = SentenceTransformer('intfloat/multilingual-e5-large-instruct')
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query_embeddings = model.encode(queries, convert_to_tensor=True, normalize_embeddings=True)
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scores = (query_embeddings @ encoded_questions.T) * 100
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selected_references['similarity'] = scores.tolist()[0]
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sorted_references = selected_references.sort_values(by='similarity', ascending=False)
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sorted_references = sorted_references.head(3)
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sorted_references = selected_references.sort_values(by='similarity', ascending=True)
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from googletrans import Translator
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translator = Translator()
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for index, row in sorted_references.iterrows():
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print(index)
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print(row['user'])
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user = translator.translate(row['user'])
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print(user)
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print(row['assistant'])
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assistant = translator.translate(row['assistant'])
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print(assistant)
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messages.append({"role": "user", "content":user })
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messages.append({"role": "assistant", "content": assistant})
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#history from chat session
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for val in history:
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if val[0]:
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messages.append({"role": "user", "content": val[0]})
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if val[1]:
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messages.append({"role": "assistant", "content": val[1]})
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#latest user question
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messages.append({"role": "user", "content": message})
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print(messages)
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response = ""
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],
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)
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if __name__ == "__main__":
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demo.launch()
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