Initial commit

app.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer
+import gradio as gr
+import torch
+
+
+title = "Mental Health Chatbot"
+description = "This bot is using a fine-tuned version of meta-llama/Llama-2-7b-chat-hf"
+
+tokenizer = AutoTokenizer.from_pretrained("vojay/Llama-2-7b-chat-hf-mental-health")
+model = AutoModelForCausalLM.from_pretrained("vojay/Llama-2-7b-chat-hf-mental-health")
+
+
+def predict(input, history=[]):
+    new_user_input_ids = tokenizer.encode(f"{input}{tokenizer.eos_token}", return_tensors="pt")
+    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
+
+    history = model.generate(
+        bot_input_ids,
+        max_length=4000,
+        pad_token_id=tokenizer.eos_token_id
+    ).tolist()
+
+    response = tokenizer.decode(history[0]).split("<|endoftext|>")
+    response = [
+        (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)
+    ]
+
+    return response, history
+
+
+gr.Interface(
+    fn=predict,
+    title=title,
+    description=description,
+    inputs=["text", "state"],
+    outputs=["chatbot", "state"]
+).launch()

requirements.txt

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+transformers
+torch