Update app.py

app.py

@@ -1,88 +1,89 @@
-# Author: Bastien & Pascal
-# 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
-from importHuggingFaceHubModel import from_pretrained_keras
-
-# Set Keras Backend to Tensorflow
-os.environ["KERAS_BACKEND"] = "tensorflow"
-
-# Load the fine-tuned model
-#model = keras.models.load_model("LoRA_Model_V2.keras")
-model = from_pretrained_keras('DracolIA/GPT-2-LoRA-HealthCare')
-
-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"[QUESTION] {question} [ANSWER]"
-    # 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))
-    
-    # Generate the answer from the model and then clean and extract the real model's response from the prompt engineered string
-    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
-    response_start = text.find("[ANSWER]") + len("[ANSWER]")
-
-    # Extract everything after "Doctor:"
-    response_text = text[response_start:].strip()
-    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()
-
-    return response_text
-    
-
-# Define a Gradio interface
-def chat_interface(question, 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)
-    
-    # Initialize an empty DataFrame to keep track of question-answer history
-    history_display = gr.Dataframe(headers=["Question", "Réponse"], values=[], interactive=False)
-
-    submit_btn.click(fn=chat_interface, inputs=[question, history_display], outputs=[response, history_display])
-
-if __name__ == "__main__":
-    demo.launch()
+# Author: Bastien & Pascal
+# 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
+#from importHuggingFaceHubModel import from_pretrained_keras
+from huggingface_hub import from_pretrained_keras
+
+# Set Keras Backend to Tensorflow
+os.environ["KERAS_BACKEND"] = "tensorflow"
+
+# Load the fine-tuned model
+#model = keras.models.load_model("LoRA_Model_V2.keras")
+model = from_pretrained_keras('DracolIA/GPT-2-LoRA-HealthCare')
+
+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"[QUESTION] {question} [ANSWER]"
+    # 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))
+    
+    # Generate the answer from the model and then clean and extract the real model's response from the prompt engineered string
+    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
+    response_start = text.find("[ANSWER]") + len("[ANSWER]")
+
+    # Extract everything after "Doctor:"
+    response_text = text[response_start:].strip()
+    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()
+
+    return response_text
+    
+
+# Define a Gradio interface
+def chat_interface(question, 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)
+    
+    # Initialize an empty DataFrame to keep track of question-answer history
+    history_display = gr.Dataframe(headers=["Question", "Réponse"], values=[], interactive=False)
+
+    submit_btn.click(fn=chat_interface, inputs=[question, history_display], outputs=[response, history_display])
+
+if __name__ == "__main__":
+    demo.launch()