Update app.py

app.py

@@ -1,66 +1,66 @@
-from langchain_community.llms.ctransformers import CTransformers
-from langchain.chains.llm import LLMChain
-from langchain.prompts import PromptTemplate
-import os
-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 the user's query. Below is the user's query.
-Query:{query}
-You just return the helpful code and 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_K_M.bin',
-        model_type='llama',
-        max_new_tokens=1096,
-        temperature=0.2,
-        repetition_penalty=1.13
-                
-    )
-    
-    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="Can AI code ? ") 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])
-    
-
+from langchain_community.llms.ctransformers import CTransformers
+from langchain.chains.llm import LLMChain
+from langchain.prompts import PromptTemplate
+import os
+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 the user's query. Below is the user's query.
+Query:{query}
+You just return the helpful code and 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='https://huggingface.co/TheBloke/CodeLlama-7B-Instruct-GGML/blob/main/codellama-7b-instruct.ggmlv3.Q4_K_M.bin',
+        model_type='llama',
+        max_new_tokens=1096,
+        temperature=0.2,
+        repetition_penalty=1.13
+                
+    )
+    
+    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="Can AI code ? ") 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()