Update app.py

app.py

@@ -1,3 +1,58 @@
 import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-gr.load("models/mouryachinta/llama-2-7b-mourya").launch()
+def initialize_model_and_tokenizer(model_name="mouryachinta/llama-2-7b-mourya"):
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    return model, tokenizer
+model, tokenizer = initialize_model_and_tokenizer()
+
+from langchain.llms.base import LLM
+
+class CustomLLM(LLM):
+    def _call(self, prompt, stop=None, run_manager=None) -> str:
+        inputs = tokenizer(prompt, return_tensors="pt")
+        result = model.generate(input_ids=inputs.input_ids, max_new_tokens=20)
+        result = tokenizer.decode(result[0])
+        return result
+
+    @property
+    def _llm_type(self) -> str:
+        return "custom"
+
+llm = CustomLLM()
+
+from langchain import PromptTemplate
+
+template = """Question: {question}
+Answer: Let's think step by step."""
+prompt = PromptTemplate(template=template, input_variables=["question"])
+
+from langchain import LLMChain
+
+llm_chain = LLMChain(prompt=prompt, llm=llm)
+
+import gradio as gr
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox()
+    clear = gr.Button("Clear")
+    llm_chain, llm = init_chain(model, tokenizer)
+
+    def user(user_message, history):
+        return "", history + [[user_message, None]]
+
+    def bot(history):
+        print("Question: ", history[-1][0])
+        bot_message = llm_chain.run(question=history[-1][0])
+        print("Response: ", bot_message)
+        history[-1][1] = ""
+        history[-1][1] += bot_message
+        return history
+
+    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(bot, chatbot, chatbot)
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+demo.queue()
+demo.launch()