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Browse files- app.py.py +116 -0
- requirements.txt +4 -0
app.py.py
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# -*- coding: utf-8 -*-
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"""[Student]_Module_2_Session_3[1].ipynb
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Automatically generated by Colab.
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Original file is located at
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https://colab.research.google.com/drive/1XTqd7vFIlHX6bgIBkZf2UUTw9ekrOO0T
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Installations
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"""
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"""#Let's build a demo for a sentiment analysis task !
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Import the necessary modules :
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"""
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from transformers import pipeline
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import gradio as gr
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"""Import the pipeline :"""
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sentiment = pipeline("sentiment-analysis")
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"""Test the pipeline on these reviews (you can also test on your own reviews) :"""
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#"I really enjoyed my stay !"
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#"Worst rental I ever got"
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sentiment(["I really enjoyed my stay !","Worst rental I ever got"])
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"""What is the format of the output ? How can you get only the sentiment or the confidence score ?"""
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sentiment(["I really enjoyed my stay !","Worst rental I ever got"])[0]["label"]
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sentiment(["I really enjoyed my stay !","Worst rental I ever got"])[0]["score"]
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"""Create a function that takes a text in input, and returns a sentiment, and a confidence score as 2 different variables"""
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def get_sentiment(text):
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lbl = sentiment(text)[0]["label"]
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scr = sentiment(text)[0]["score"]
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return lbl,scr
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summarizer = pipeline("summarization")
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def get_sum(text):
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return summarizer(text)[0]["summary_text"]
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from gtts import gTTS
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def convert(text):
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tts = gTTS(text)
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path = "audio.mp3"
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tts.save(path)
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return path
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#to get access to the model on hugging face
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from huggingface_hub import InferenceClient
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#You are creating a connection to a model hosted on Hugging Face using their InferenceClient.
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client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.2")
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# Parameters:
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# - message: the new message the user just sent (like "What's the weather?")
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# - history: a list of all the previous messages in the chat
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def respond(message, history):
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#Why use this structure?
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#Because the API expects the messages in an ordered format, where each message has a role (system, user, assistant) and content.
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#We need to keep the conversation order, so a list is used (order matters).
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#Each message has structured data (role and content), so a dictionary is used.
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#The list contains elements, and each element here is a dictionary.
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# This tells the model how to behave (like giving it a "job description").
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messages = [{"role": "system", "content": "You are a helpful formal assistant."}]
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# This loop recreates the entire chat history in a format the model understands.
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# It adds each user question and the assistant's response in order.
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for user, bot in history:
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messages.append({"role": "user", "content": user})
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messages.append({"role": "assistant", "content": bot})
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# Add the new message to the end of the conversation
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messages.append({"role": "user", "content": message})
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# Send the conversation to the Mistral model via Hugging Face
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# max_tokens=300 limits how long the reply can be
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response = client.chat_completion(messages, max_tokens=300)
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# Extract the first reply from the model's response
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bot_reply = response.choices[0].message.content
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return bot_reply
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convo_tab = gr.ChatInterface(fn = respond,title = "Chatbot", description = "feel free to ask anything!")
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conv_tab = gr.Interface(fn = convert, inputs = gr.Textbox(label= "Enter your text here: "), outputs = gr.Audio(label = "Here's the audio: "), title= "Text-to-Speech")
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"""Build an interface for the app using Gradio.
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The customer wants this result :
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"""
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sen_tab = gr.Interface(fn = get_sentiment, inputs = gr.Textbox(label="Enter your review: ",placeholder= "(eg..I love this product)"),outputs = [gr.Textbox(label="Sentiment:"),gr.Number(label="Score:")], title = "Sentiment Analysis" )
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sum_tab = gr.Interface(fn = get_sum, inputs = gr.Textbox(label= "Enter your text to summarize: "), outputs = gr.Textbox(label="Summarization: "), title = "Summarization")
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demo = gr.TabbedInterface(
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[sen_tab, sum_tab, conv_tab, convo_tab],
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["Sentiment-Analysis","Summarization", "Text-To-Speech","Chatbot"],
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theme = "soft"
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)
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demo.launch()
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requirements.txt
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@@ -0,0 +1,4 @@
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gradio
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transformers
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torch
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gtts
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