Create app.py

app.py

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+
+import gradio as gr
+from langchain.llms import LlamaCpp
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain.callbacks.manager import CallbackManager
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from huggingface_hub import hf_hub_download
+callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+
+repo_id="TheBloke/Mistral-7B-OpenOrca-GGUF"
+model_name="mistral-7b-openorca.Q5_K_M.gguf"
+
+hf_hub_download(repo_id=repo_id,
+                filename=model_name,local_dir =".")
+
+
+
+def format_prompt(message, history):
+  prompt = "<s>"
+  for user_prompt, bot_response in history:
+    prompt += f"[INST] {user_prompt} [/INST]"
+    prompt += f" {bot_response}</s> "
+  prompt += f"[INST] {message} [/INST]"
+  return prompt
+
+def generate(
+    prompt, history, temperature=0.9, top_p=0.95,
+):
+    
+    temperature = float(temperature)
+    if temperature < 1e-2:
+        temperature = 1e-2
+    top_p = float(top_p)
+    
+ 
+    formatted_prompt = format_prompt(prompt, history)
+    
+    llm = LlamaCpp(
+        model_path=model_name,
+        temperature=temperature,
+        max_tokens=2000,
+        top_p=top_p,
+        callback_manager=callback_manager, 
+        verbose=True, # Verbose is required to pass to the callback manager
+    )
+    # stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+    output = ""
+    output=llm(formatted_prompt)
+    # for response in stream:
+    #     output += response.token.text
+    #     yield output
+    return output
+
+
+additional_inputs=[
+    gr.Slider(
+        label="Temperature",
+        value=0.9,
+        minimum=0.0,
+        maximum=1.0,
+        step=0.05,
+        interactive=True,
+        info="Higher values produce more diverse outputs",
+    ),
+    gr.Slider(
+        label="Max new tokens",
+        value=256,
+        minimum=0,
+        maximum=1048,
+        step=64,
+        interactive=True,
+        info="The maximum numbers of new tokens",
+    ),
+    gr.Slider(
+        label="Top-p (nucleus sampling)",
+        value=0.90,
+        minimum=0.0,
+        maximum=1,
+        step=0.05,
+        interactive=True,
+        info="Higher values sample more low-probability tokens",
+    ),
+    gr.Slider(
+        label="Repetition penalty",
+        value=1.2,
+        minimum=1.0,
+        maximum=2.0,
+        step=0.05,
+        interactive=True,
+        info="Penalize repeated tokens",
+    )
+]
+
+css = """
+  #mkd {
+    height: 500px; 
+    overflow: auto; 
+    border: 1px solid #ccc; 
+  }
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.HTML("<h1><center>Mistral 7B Instruct<h1><center>")
+    gr.HTML("<h3><center>In this demo, you can chat with <a href='https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1'>Mistral-7B-Instruct</a> model. 💬<h3><center>")
+    gr.HTML("<h3><center>Learn more about the model <a href='https://huggingface.co/docs/transformers/main/model_doc/mistral'>here</a>. 📚<h3><center>")
+    gr.ChatInterface(
+        generate,
+        additional_inputs=additional_inputs,
+        examples=[["What is the secret to life?"], ["Write me a recipe for pancakes."]]
+    )
+
+demo.queue().launch(debug=True)