Upload app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+description = """
+<p>
+<center>
+This bot was trained on a dataset of 1000 movie reviews from IMDB. It can suggest movies similar to the one you liked!
+<img src="https://huggingface.co/spaces/kingabzpro/Rick_and_Morty_Bot/resolve/main/img/rick.png" alt="rick" width="200"/>
+</center>
+</p>
+"""
+model = pipeline("text-generation", model="charoori/llm4movies")
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+base_model_id = "mistralai/Mistral-7B-v0.1"
+tokenizer = AutoTokenizer.from_pretrained(
+    base_model_id,
+    add_bos_token=True,
+)
+model = AutoModelForCausalLM.from_pretrained("charoori/llm4movies")
+
+def predict(input, history=[]):
+    # tokenize the new input sentence
+    new_user_input_ids = tokenizer.encode(input, return_tensors='pt')
+
+    # append the new user input tokens to the chat history
+    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
+
+    # generate a response 
+    # model_input = tokenizer(eval_prompt, return_tensors="pt")
+    history = model.generate(bot_input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id).tolist()
+
+    # convert the tokens to text, and then split the responses into lines
+    response = tokenizer.decode(model.generate(**bot_input_ids, max_new_tokens=256, repetition_penalty=1.15)[0], skip_special_tokens=True)
+
+    # response = tokenizer.decode(history[0]).split("<|endoftext|>")
+    #print('decoded_response-->>'+str(response))
+    response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
+    #print('response-->>'+str(response))
+    return response, history
+
+
+interface = gr.Interface(
+    fn=predict,
+	title = "Find your next movie!",
+    inputs="textbox",
+    outputs="text",
+    description=description,
+    examples=[["I liked the movie Matrix because it was very interesting and had a great story. Suggest something similar"]]
+)
+interface.launch()
+