Synchronisation Production

README.md

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+---
+title: SAE-GPT2
+emoji: ❤️
+colorFrom: indigo
+colorTo: red
+sdk: gradio
+sdk_version: 3.50.2
+app_file: app.py
+pinned: false
+---
+
+<hr/>
+
+<h4> Environnement de développement commun </h4>
+
+<br/>
+
+| Nom        | Lien                                                  |
+|------------|-------------------------------------------------------|
+| Production | https://huggingface.co/spaces/FFatih/SAE-GPT2-PROD    |
+| Recette    | https://huggingface.co/spaces/FFatih/SAE-GPT2-RECETTE |
+
+<hr/>
+
+<h4> Environnement de développement personnel </h4>
+
+<br/>
+
+| Prenom            | Lien                                                       |
+|-------------------|------------------------------------------------------------|
+| Fatih             | https://huggingface.co/spaces/FFatih/SAE-GPT2-FATIH        |
+| Bastien           | https://huggingface.co/spaces/BastienHot/SAE-GPT2-BASTIEN  |
+| Pascal            | https://huggingface.co/spaces/PascalZhan/SAE-GPT2-PASCAL   |
+| Tamij             | https://huggingface.co/spaces/Tamij/SAE-GPT2-TAMIJ         |
+| Kevin             | https://huggingface.co/spaces/Kemasu/SAE-GPT2-KEVIN        |
+| Lilian            | https://huggingface.co/spaces/Solialiranes/SAE-GPT2-LILIAN |
+| Evan              | https://huggingface.co/spaces/Evanparis240/SAE-GPT2-EVAN   |

app.py

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+# Author: Bastien
+# Date: 2/25/2024
+# Project: SAE-GPT2 | BUT 3 Informatique - Semester 5
+
+# Import of required libraries 
+import os
+
+os.system("pip install --upgrade pip")
+os.system("pip install googletrans-py")
+os.system("pip install tensorflow==2.15.0")
+os.system("pip install keras-nlp")
+os.system("pip install -q --upgrade keras") # Upgrade Keras to version 3
+
+import time
+import keras
+import keras_nlp
+import pandas as pd
+import gradio as gr
+from googletrans import Translator
+
+# Set Keras Backend to Tensorflow
+os.environ["KERAS_BACKEND"] = "tensorflow"
+
+# Load the fine-tuned model
+model = keras.models.load_model("LoRA_Model_V2.keras")
+
+translator = Translator() # Create Translator Instance
+
+# Function to generate responses from the model
+def generate_responses(question):
+    language = translator.detect(question).lang.upper() # Verify the language of the prompt
+
+    if language != "EN":
+        question = translator.translate(question, src=language, dest="en").text # Translation of user text to english for the model
+    
+    prompt = f"Patient: \"{question}\"\nDoctor:"
+    # Generate the answer from the model and then clean and extract the real model's response from the prompt engineered string
+    output = clean_answer_text(model.generate(prompt, max_length=1024))
+
+    if language != "EN":
+        output = Translator().translate(output, src="en", dest=language).text # Translation of model's text to user's language
+
+    return output
+
+
+# Function clean the output of the model from the prompt engineering done in the "generate_responses" function
+def clean_answer_text(text: str) -> str:
+    # Define the start marker for the model's response
+    doctor_response_start = text.find("Doctor:") + len("Doctor:")
+
+    # Extract everything after "Doctor:"
+    response_text = text[doctor_response_start:].strip()
+
+    # If there's a follow-up "Patient:" in the response, cut the response there
+    follow_up_index = response_text.find("\nPatient:")
+    if follow_up_index != -1:
+        response_text = response_text[:follow_up_index]
+
+    # If there's no follow-up "Patient:", cut the response to the last dot (.)
+    else:
+        last_dot_index = response_text.rfind(".")
+        if last_dot_index != -1:
+            response_text = response_text[:last_dot_index + 1]
+
+    # Additional cleaning if necessary (e.g., removing leading/trailing spaces or new lines)
+    response_text = response_text.strip()
+
+    response_text = response_text.replace("Doctor: ","")
+
+    return response_text
+    
+# Initialize an empty DataFrame to keep track of question-answer history
+history_df = pd.DataFrame(columns=["Question", "Réponse"])
+
+# Define a Gradio interface
+def chat_interface(question):
+    global history_df
+    response = generate_responses(question)
+    # Insert the new question and response at the beginning of the DataFrame
+    history_df = pd.concat([pd.DataFrame({"Question": [question], "Réponse": [response]}), history_df], ignore_index=True)
+    return response, history_df
+
+with gr.Blocks() as demo:
+    gr.HTML("""
+        <div style='width: 100%; height: 200px; background: url("https://github.com/BastienHot/SAE-GPT2/raw/70fb88500a2cc168d71e8ed635fc54492beb6241/image/logo.png") no-repeat center center; background-size: contain;'>
+            <h1 style='text-align:center; width=100%'>DracolIA - AI Question Answering for Healthcare</h1>
+        </div>
+    """)
+    with gr.Row():
+        question = gr.Textbox(label="Votre Question", placeholder="Saisissez ici...")
+        submit_btn = gr.Button("Envoyer")
+    response = gr.Textbox(label="Réponse", interactive=False)
+    history_display = gr.Dataframe(headers=["Question", "Réponse"], values=[])
+
+    submit_btn.click(fn=chat_interface, inputs=question, outputs=[response, history_display])
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio