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Update app.py
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app.py
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@@ -1,5 +1,6 @@
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import gradio as gr
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from huggingface_hub import InferenceClient
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"""
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For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
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client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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def respond(
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message,
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history: list[tuple[str, str]],
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@@ -15,6 +21,18 @@ def respond(
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temperature,
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top_p,
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):
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messages = [{"role": "system", "content": system_message}]
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for val in history:
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import gradio as gr
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from huggingface_hub import InferenceClient
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from datasets import load_dataset # Import the datasets library
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"""
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For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
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client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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# Load the dataset from Hugging Face
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dataset = load_dataset("samhog/psychology-10k", split="train") # Load the training split
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def respond(
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message,
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history: list[tuple[str, str]],
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temperature,
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top_p,
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):
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# First, attempt to find a response in the dataset
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closest_match = None
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for entry in dataset:
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if message.lower() in entry["question"].lower(): # Adjust "question" to the actual column name
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closest_match = entry["answer"] # Adjust "answer" to the actual column name
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break
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# If a match is found, return the dataset answer
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if closest_match:
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return closest_match
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# If no match is found, use the chatbot model
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messages = [{"role": "system", "content": system_message}]
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for val in history:
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