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.ipynb_checkpoints/test_gradio-checkpoint.py

@@ -1,24 +1,47 @@
-# import gradio as gr
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain.prompts import PromptTemplate
+from langchain.schema import AIMessage, HumanMessage
+import gradio as gr
+import os
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+repo_id = "mistralai/Mistral-7B-Instruct-v0.2"
+llm = HuggingFaceEndpoint(
+    repo_id = repo_id, 
+    huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
+)
+
+llm_chain = LLMChain(llm=llm, prompt=prompt)
+print(llm_chain.invoke(question)['text'])
+
+template = """You're a good chatbot. Answer this request: {question}
+Answer: Let's think step by step."""
+prompt = PromptTemplate.from_template(template=template)
+
+
+def predict(message, history):
+    history_langchain_format = []
+    # for human, ai in history:
+    #     history_langchain_format.append(HumanMessage(content=human))
+    #     history_langchain_format.append(AIMessage(content=ai))
+    # history_langchain_format.append(HumanMessage(content=message))
+    # gpt_response = llm(history_langchain_format)
+    response = llm_chain(message)
+    return response
+
+gr.ChatInterface(predict).launch()
+
+
+
+
+
 
-# def greet(name, intensity):
-#     return "Hello, " + name + "!" * int(intensity)
 
-# def llm_inference(next):
-    
-    
-# demo = gr.Interface(
-#     fn=greet,
-#     inputs=["text"],
-#     outputs=["text"],
-# )
 
-# demo.launch(share=True)
 
-import random
 
-def random_response(message, history):
-    return random.choice(["Yes", "No"])
 
-import gradio as gr
 
-gr.ChatInterface(random_response).launch()

test_gradio.py

@@ -1,24 +1,47 @@
-# import gradio as gr
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain.prompts import PromptTemplate
+from langchain.schema import AIMessage, HumanMessage
+import gradio as gr
+import os
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+repo_id = "mistralai/Mistral-7B-Instruct-v0.2"
+llm = HuggingFaceEndpoint(
+    repo_id = repo_id, 
+    huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
+)
+
+llm_chain = LLMChain(llm=llm, prompt=prompt)
+print(llm_chain.invoke(question)['text'])
+
+template = """You're a good chatbot. Answer this request: {question}
+Answer: Let's think step by step."""
+prompt = PromptTemplate.from_template(template=template)
+
+
+def predict(message, history):
+    history_langchain_format = []
+    # for human, ai in history:
+    #     history_langchain_format.append(HumanMessage(content=human))
+    #     history_langchain_format.append(AIMessage(content=ai))
+    # history_langchain_format.append(HumanMessage(content=message))
+    # gpt_response = llm(history_langchain_format)
+    response = llm_chain(message)
+    return response
+
+gr.ChatInterface(predict).launch()
+
+
+
+
+
 
-# def greet(name, intensity):
-#     return "Hello, " + name + "!" * int(intensity)
 
-# def llm_inference(next):
-    
-    
-# demo = gr.Interface(
-#     fn=greet,
-#     inputs=["text"],
-#     outputs=["text"],
-# )
 
-# demo.launch(share=True)
 
-import random
 
-def random_response(message, history):
-    return random.choice(["Yes", "No"])
 
-import gradio as gr
 
-gr.ChatInterface(random_response).launch()