Create app.py

app.py

@@ -0,0 +1,64 @@
+from langchain.llms import CTransformers
+from langchain.chains import LLMChain
+from langchain.prompts 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 solve coding problems and 
+return code snippets based on user's query. Below is the user's query.
+Query: {query}
+You just return the helpful code and related datails
+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.Q4_K_M.gguf',
+        model_type = 'llama',
+        max_new_tokens = 1096,
+        temperature = 0.2,
+        repetition_penalty = 1.13,
+        gpu_layers = 2
+    )
+    return llm
+
+def chain_pipeline():
+    llm = load_model()
+    qa_prompt = set_custom_prompt()
+    qa_chain = LLMChain(
+        prompt = qa_prompt,
+        llm=llm
+    )
+    return qa_chain
+
+llmcahin = chain_pipeline()
+
+def bot(query):
+    llm_response = llmcahin.run({"query":query})
+    return llm_response
+
+with gr.Blocks(title="code llama 7b") as demo:
+    gr.Markdown("# Code llama")
+    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(share=True)