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Update app.py
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app.py
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@@ -1,22 +1,35 @@
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import gradio as gr
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from transformers import
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MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
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OFFLOAD_DIR = "./offload"
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
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# Load model with
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_NAME,
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device_map="auto",
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offload_folder=OFFLOAD_DIR,
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)
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#
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generator = pipeline(
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"text-generation",
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model=model,
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@@ -30,12 +43,8 @@ Speak in a friendly, school-spirited, and enthusiastic tone.
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Always provide interesting facts about WPI when asked questions, and stay in character as Gompei.
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"""
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# Chat history
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chat_history = []
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def chatbot(message, history):
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# Build context with system prompt and previous conversation
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context = system_prompt
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for user, bot in history:
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context += f"\nUser: {user}\nGompei: {bot}"
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top_p=0.9
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)
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reply = response[0]["generated_text"].split("Gompei:")[-1].strip()
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# Update history
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chat_history.append((message, reply))
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return reply
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# Gradio interface
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)
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if __name__ == "__main__":
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# Disable SSR for faster build
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demo.launch(server_name="0.0.0.0", server_port=7860, ssr_mode=False)
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import gradio as gr
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from transformers import (
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AutoModelForCausalLM,
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AutoTokenizer,
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pipeline,
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BitsAndBytesConfig
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)
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MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
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OFFLOAD_DIR = "./offload"
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# Configure 8-bit quantization with CPU fallback
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bnb_config = BitsAndBytesConfig(
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load_in_8bit=True,
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llm_int8_threshold=6.0,
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llm_int8_has_fp16_weight=False,
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llm_int8_enable_fp32_cpu_offload=True # ✅ allow CPU fallback
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)
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
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# Load model with quantization + offloading
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_NAME,
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device_map="auto", # spreads across CPU/GPU automatically
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quantization_config=bnb_config,
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offload_folder=OFFLOAD_DIR, # spill to disk if RAM full
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dtype="auto"
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)
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# Build pipeline
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generator = pipeline(
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"text-generation",
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model=model,
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Always provide interesting facts about WPI when asked questions, and stay in character as Gompei.
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"""
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def chatbot(message, history):
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# Build context with system prompt + conversation history
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context = system_prompt
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for user, bot in history:
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context += f"\nUser: {user}\nGompei: {bot}"
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top_p=0.9
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)
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reply = response[0]["generated_text"].split("Gompei:")[-1].strip()
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return reply
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# Gradio interface
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)
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if __name__ == "__main__":
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demo.launch(server_name="0.0.0.0", server_port=7860, ssr_mode=False)
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