Create app.py

app.py

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+
+import os
+import gradio as gr
+import keras_hub
+import os
+
+import re
+
+
+
+
+model_path="kaggle://asunsada/gemma2_2b_it_en_roleplay/keras/football_coach_11042024_epoch15" # kaggle
+
+
+class GemmaChatbot:
+    def __init__(self):
+        # Initialize the model
+        #preset = "gemma_instruct_2b_en"  # name of pretrained Gemma 2
+        #self.gemma_llm = keras_hub.models.GemmaCausalLM.from_preset(preset)
+        # Load your custom model
+        self.gemma_llm = keras_hub.models.GemmaCausalLM.from_preset(model_path)
+        print(self.gemma_llm)
+
+    def format_prompt(self, message, history):
+        # Format conversation history into a single string
+        formatted_history = ""
+        for user_msg, assistant_msg in history:
+            formatted_history += f"User: {user_msg}\nAssistant: {assistant_msg}\n"
+
+        # Add the current message
+        prompt = formatted_history + f"User: {message}\nAssistant:"
+        return prompt
+
+    def generate_response(self, message, history):
+        # Format the prompt with history
+
+        #prompt = self.format_prompt(message, history)
+        prompt= message
+        # Generate response
+        output = self.gemma_llm.generate(prompt,256)
+        output = clean_incomplete_sentences(output)  # remove  incomplete sentences (typically
+        # last one or any question at the end.
+        return output.replace(prompt, "").strip()
+
+# remove  incomplete sentences (typically last one or any question at the end.)
+def clean_incomplete_sentences(text):
+    # Split text into sentences using regular expressions
+    sentences = re.split(r'(?<=\.) |(?<=\?) |(?<=!) ', text)
+
+    # Filter out incomplete sentences (those not ending with ".", "?" or "!")
+    complete_sentences = [s for s in sentences if re.search(r'[.!?]$', s)]
+
+    # Remove the last sentence if it ends with a question mark
+    if complete_sentences and complete_sentences[-1].endswith('?'):
+        complete_sentences = complete_sentences[:-1]
+
+    # Join sentences back into a single string
+    cleaned_text = ' '.join(complete_sentences)
+    return cleaned_text
+
+def create_chatbot():
+    # Initialize the chatbot
+    chatbot = GemmaChatbot()
+
+    # Create the Gradio interface
+    chat_interface = gr.ChatInterface(
+        fn=chatbot.generate_response,
+        title="🏈 Tackle Tutor Chatbot 🏈 💬",
+        description="I'm Tackle Tutor, the head coach of the greatest football team around! "
+            "With over 20 years of coaching experience and numerous championships under my belt, "
+            "I've also had the honor of coaching and playing in the NFL. \n\n"
+            "Ask me how to tackle any challenge, on the field or in life, and I'll guide you through it",
+        examples=[
+            "Why is resilience important in football and life?",
+            "How can I keep trying when something is really hard?",
+            "How do famous coaches inspire?",
+            "What can I learn from coach Barry Switzer?",
+            "How can football teach us about teamwork?",
+            "Why is preparation essential for success?",
+            "Why should we always give our best effort?",
+        ],
+        theme=gr.themes.Soft()
+    )
+
+    return chat_interface
+
+
+# Launch the chatbot
+if __name__ == "__main__":
+    # Create and launch the interface
+    chat_interface = create_chatbot()
+    chat_interface.launch(share=True)