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GradioChatBot

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AmadouDiaV commited on Mar 11

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8a64278

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1 Parent(s): 50e1270

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app copy.py +157 -0
app.py +27 -32
requirements.txt +0 -4
weight.h5 +0 -3

app copy.py ADDED Viewed

	@@ -0,0 +1,157 @@

+# # Mount Google Drive
+# from google.colab import drive
+# drive.mount('/content/drive')
+import gradio as gr
+from transformers import AutoModel, TFAutoModel
+# Modèle Hugging Face à partir de l'identifiant du modèle
+model_name = "gpt2"
+huggingface_model = AutoModel.from_pretrained(model_name)
+# Spécifiez l'identifiant de référentiel correct sans informations supplémentaires
+repo_name = "motofanacc/monModel"
+# Chemin vers les poids de votre modèle dans votre référentiel
+model_checkpoint = "main"
+# Charger le modèle à partir de Hugging Face Hub
+model = TFAutoModel.from_pretrained(repo_name, from_pt=True, model_checkpoint=model_checkpoint)
+#a
+# # Charger les poids TensorFlow
+# tensorflow_weights_path = "motofanacc/GradioChatBot/tree/main/Checkpoints"
+# tensorflow_model = TFAutoModel.from_pretrained(tensorflow_weights_path)
+def generate_text(model, input_text, max_length=50):
+    return model.generate(input_text, max_length=max_length)
+# preprocessor = keras_nlp.models.GPT2CausalLMPreprocessor.from_preset(
+#     "gpt2_base_en",
+#     sequence_length=128,
+# )
+# gpt2_lm = keras_nlp.models.GPT2CausalLM.from_preset(
+#     "gpt2_base_en",
+#     preprocessor=preprocessor,
+# )
+# gpt2_lm.load_weights('/content/drive/MyDrive/Checkpoints/weights')
+# Gradio app
+# <a href="https://www.freepik.com/icon/user_456212#fromView=search&term=avatar&track=ais&page=1&position=22&uuid=48125587-eeb5-4fe3-9eb2-f9fe7330f4fe">Icon by Freepik</a>
+# <a href="https://www.freepik.com/icon/ai_2814666#fromView=search&term=robot&track=ais&page=1&position=20&uuid=58780fb9-dab6-4fb1-9928-479b2926a242">Icon by Freepik</a>
+theme = gr.themes.Soft().set(
+    background_fill_primary='white',
+    background_fill_primary_dark='white',
+)
+with gr.Blocks(theme=theme,css="""
+    .gradio-container {
+        background-color: white;
+        width: 70vw;
+    }
+    #chatbot{
+        background-image: url("https://png.pngtree.com/thumb_back/fh260/background/20201014/pngtree-breast-cancer-awareness-pink-ribbons-background-design-image_417234.jpg");
+    }
+    #chatbot .bubble-wrap::-webkit-scrollbar {
+        width: 20px;
+    }
+    #chatbot .bubble-wrap::-webkit-scrollbar-thumb {
+        background-color: whitesmoke;
+        border-radius: 20px;
+        border: 6px solid transparent;
+        background-clip: content-box;
+    }
+    #chatbot .bubble-wrap::-webkit-scrollbar-thumb:hover {
+        background-color: grey;
+    }
+    #chatbot .bubble-wrap::-webkit-scrollbar-track {
+        background-color: transparent;
+    }
+    #chatbot .message p{
+        text-align: start;
+        color: white;
+    }
+    h1, p {
+        text-align: center;
+        color: black;
+    }
+    body #footer_note {
+        text-align: center;
+        font-size: x-small;
+        font-weight:bold;
+    }
+    .label {
+        display:none;
+    }
+    textarea, .gallery-item, .gallery-item:hover {
+      color: black;
+      border: 1px black solid;
+      background-color: white;
+    }
+    .user {
+      background-color: #374151;
+    }
+    .user {
+      background-color: #111827;
+    }
+    .gallery-item:hover {
+      color: white;
+      border: 1px black solid;
+      background-color: black;
+    }
+    body gradio-app  {
+      background-color: white;
+    }
+    """) as demo:
+    gr.HTML(f"""
+        <html>
+        <body>
+            <h1>Welcome, I'm CancerBot 🤖</h1>
+            <p>Here you can ask all questions about cancer</p>
+        </body>
+        </html>
+    """)
+    def return_message(message, history, model=huggingface_model, max_length=128):
+        if len(message) <= 1:
+            gr.Warning('Please enter a message with more than one character.')
+        elif len(message) > max_length:
+            gr.Warning(f"Input should not exceed {max_length} characters.")
+        else:
+            cancer_answer = generate_text(model, message)
+            message = "**You**\n" + message
+            history.append([message, f"**CancerBot**\n{cancer_answer}"])
+        return "", history
+    chatbot = gr.Chatbot(
+        height="60vh",
+        bubble_full_width=True,
+        avatar_images=(["/content/drive/MyDrive/Data/avatar.png", "/content/drive/MyDrive/Data/robot.png"]),
+        show_copy_button=True,
+        likeable=True,
+        layout='bubble',
+        elem_id='chatbot',
+        show_label=False,
+    )
+    with gr.Row():
+        input_box = gr.Textbox(placeholder="Message CancerBot...", container=False, scale=9)
+        submit_btn = gr.Button(value="⬆", scale=1)
+        submit_btn.click(return_message, [input_box, chatbot],[input_box, chatbot])
+    examples = gr.Examples(examples=["What is a thyroid cancer ?", "How can I know that I have a lung cancer ?",
+			"How many types of cancer ?"], inputs=[input_box], label="")
+    input_box.submit(return_message, [input_box, chatbot],[input_box, chatbot])
+    gr.HTML(f"""
+            <html>
+            <body>
+            <p id="footer_note">CancerBot is based on cancer documents. Consider checking important information.</p>
+            </body>
+            </html>
+            """)
+demo.queue(default_concurrency_limit=34) # 32 students, 2 teachers
+demo.launch(share=True)

app.py CHANGED Viewed

@@ -1,43 +1,38 @@
-# # Mount Google Drive
-# from google.colab import drive
-# drive.mount('/content/drive')
-import gradio as gr
-from transformers import AutoModel, TFAutoModel
-# Modèle Hugging Face à partir de l'identifiant du modèle
-model_name = "gpt2"
-huggingface_model = AutoModel.from_pretrained(model_name)
-# Spécifiez l'identifiant de référentiel correct sans informations supplémentaires
-repo_name = "motofanacc/monModel"
-# Chemin vers les poids de votre modèle dans votre référentiel
-model_checkpoint = "main"
-# Charger le modèle à partir de Hugging Face Hub
-model = TFAutoModel.from_pretrained(repo_name, from_pt=True, model_checkpoint=model_checkpoint)
-#a
-# # Charger les poids TensorFlow
-# tensorflow_weights_path = "motofanacc/GradioChatBot/tree/main/Checkpoints"
-# tensorflow_model = TFAutoModel.from_pretrained(tensorflow_weights_path)
 def generate_text(model, input_text, max_length=50):
     return model.generate(input_text, max_length=max_length)
-# preprocessor = keras_nlp.models.GPT2CausalLMPreprocessor.from_preset(
-#     "gpt2_base_en",
-#     sequence_length=128,
-# )
-# gpt2_lm = keras_nlp.models.GPT2CausalLM.from_preset(
-#     "gpt2_base_en",
-#     preprocessor=preprocessor,
-# )
-# gpt2_lm.load_weights('/content/drive/MyDrive/Checkpoints/weights')
 # Gradio app
 # <a href="https://www.freepik.com/icon/user_456212#fromView=search&term=avatar&track=ais&page=1&position=22&uuid=48125587-eeb5-4fe3-9eb2-f9fe7330f4fe">Icon by Freepik</a>
@@ -118,7 +113,7 @@ with gr.Blocks(theme=theme,css="""
         </html>
     """)
-    def return_message(message, history, model=huggingface_model, max_length=128):
         if len(message) <= 1:
             gr.Warning('Please enter a message with more than one character.')
         elif len(message) > max_length:
@@ -154,4 +149,4 @@ with gr.Blocks(theme=theme,css="""
             </html>
             """)
 demo.queue(default_concurrency_limit=34) # 32 students, 2 teachers
-demo.launch(share=True)

+# -*- coding: utf-8 -*-
+"""GradioApp - Final.ipynb
+Automatically generated by Colaboratory.
+Original file is located at
+    https://colab.research.google.com/drive/13x0cApCbqR5GKWE2nrk7DV4rcgJcO_HF
+"""
+import subprocess
+import sys
+def install(package):
+    subprocess.check_call([sys.executable, "-m", "pip", "install", package])
+install("typing-extensions")
+install("gradio")
+install("keras_nlp")
+from tensorflow import keras
+import keras_nlp
+import gradio as gr
 def generate_text(model, input_text, max_length=50):
     return model.generate(input_text, max_length=max_length)
+preprocessor = keras_nlp.models.GPT2CausalLMPreprocessor.from_preset(
+    "gpt2_medium_en",
+    sequence_length=128,
+)
+gpt2_lm = keras_nlp.models.GPT2CausalLM.from_preset(
+    "gpt2_medium_en",
+    preprocessor=preprocessor,
+)
+gpt2_lm.load_weights('./Checkpoints')
 # Gradio app
 # <a href="https://www.freepik.com/icon/user_456212#fromView=search&term=avatar&track=ais&page=1&position=22&uuid=48125587-eeb5-4fe3-9eb2-f9fe7330f4fe">Icon by Freepik</a>
         </html>
     """)
+    def return_message(message, history, model=gpt2_lm, max_length=128):
         if len(message) <= 1:
             gr.Warning('Please enter a message with more than one character.')
         elif len(message) > max_length:
             </html>
             """)
 demo.queue(default_concurrency_limit=34) # 32 students, 2 teachers
+demo.launch(share=True,favicon_path="/content/drive/MyDrive/Data/robot.png")

requirements.txt DELETED Viewed

@@ -1,4 +0,0 @@
-transformers
-tensorflow
-torch
-tf-keras

weight.h5 DELETED Viewed

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:318750929cbd308cef9661edb78764506fece63ddd58286cc8a7d561f680012b
-size 245506128