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app.py

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+from langchain.llms import CTransformers
+from langchain.chains import LLMChain 
+from langchain import PromptTemplate
+import os
+import io
+import gradio as gr
+import time
+
+custom_prompt_template = """
+You are an AI coding assistant and your task is to resolve coding issues and return code snippets for the same based on the user's given query.
+Query : {query}
+You just return the helpful code and the related details.
+Helpful code and related details:
+"""
+
+def set_custom_prompt():
+    prompt=PromptTemplate(
+        template = custom_prompt_template,
+        input_variables = ['query']
+    )
+
+    return prompt
+
+def load_model():
+    llm = CTransformers(
+        model = 'codellama-7b-instruct.ggmlv3.Q4_0.bin',
+        model_type = 'llama',
+        max_new_tokens = 1096,
+        temperature = 0.2,
+        repetition_penalty = 1.13,
+        #gpu_layers = 3
+    )
+    return llm
+
+def chain_pipeline():
+    llm = load_model()
+    qa_prompt = set_custom_prompt()
+    qa_chain = LLMChain(
+        prompt = qa_prompt,
+        llm = llm
+    )
+    return qa_chain
+
+llmchain = chain_pipeline()
+
+def bot(query):
+    llm_response = llmchain.run({"query":query})
+    return llm_response
+
+with gr.Blocks(title="Code Llama Srini") as demo:
+    gr.Markdown("# Code Llama Demo")
+    chatbot = gr.Chatbot([], elem_id="chatbot",height=700)
+    msg = gr.Textbox()
+    clear = gr.ClearButton([msg,chatbot])
+
+
+    def respond(message,chat_history):
+        bot_message = bot(message)
+        chat_history.append((message,bot_message))
+        time.sleep(2)
+        return "", chat_history
+
+    msg.submit(respond,[msg,chatbot],[msg,chatbot])
+
+
+demo.launch()