Create app.py

app.py

@@ -0,0 +1,85 @@
+import gradio as gr
+
+from langchain_community.llms import LlamaCpp
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain_core.callbacks import StreamingStdOutCallbackHandler
+from langchain.retrievers import TFIDFRetriever
+from langchain.chains import RetrievalQA
+from langchain.memory import ConversationBufferMemory
+
+
+callbacks = [StreamingStdOutCallbackHandler()]
+print("creating ll started")
+llm = LlamaCpp(
+    model_path="aliyasir_Llama-3-8B-Instruct-Finance-RAG_adapt_basic_model_16bit.gguf",
+    temperature=0.75,
+    max_tokens=100,
+    top_p=4,
+    callback_manager=callbacks,
+    verbose=True,  # Verbose is required to pass to the callback manager
+)
+# print("creating ll ended")
+
+
+
+
+
+
+def greet(question, model_type):
+    print(f"question is {question}")
+    if model_type == "With memory":
+        retriever = TFIDFRetriever.from_texts(
+            ["Finatial AI"])
+        
+        
+        template = """You are the Finiantial expert:
+        {history}
+        {context}
+        ### Instruction:
+        {question}
+        
+        ### Input:
+        
+        
+        ### Response:
+        """
+        
+        prompt1 = PromptTemplate(
+            input_variables=["history", "context", "question"],
+            template=template,
+        )
+        
+        llm_chain_model = RetrievalQA.from_chain_type(
+            llm=llm,
+            chain_type='stuff',
+            retriever=retriever,
+            verbose=False,
+            chain_type_kwargs={
+                "verbose": False,
+                "prompt": prompt1,
+                "memory": ConversationBufferMemory(
+                    memory_key="history",
+                    input_key="question"),
+            }
+        )
+        print("creating model created")
+    else:
+        template = """You are the Finiantial expert:
+        ### Instruction:
+        {question}
+        ### Input:
+        ### Response:
+        """
+        
+        prompt = PromptTemplate(template=template, input_variables=["question"])
+        
+        llm_chain_model = LLMChain(prompt=prompt, llm=llm)
+    out_gen = llm_chain_model.run(question)  
+    print(f"out is: {out_gen}")
+    return out_gen
+
+demo = gr.Interface(fn=greet, inputs=["text", gr.Dropdown(
+            ["With memory", "Without memory"], label="Memory status", info="With using memory, the output will be slow but strong"
+        ),], outputs="text")
+demo.launch(debug=True, share=True)