update

app.py

@@ -1,33 +1,39 @@
 from fastapi import FastAPI
 from llama_cpp import Llama
+import streamlit as st
+
+llm = Llama(
+    model_path="Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf",
+)
+prompt = st.chat_input("Say something")
+if prompt:
+    st.write(f"User has sent the following prompt: {prompt}")
 
 ## create a new FASTAPI app instance
-app=FastAPI()
+# app=FastAPI()
 
 # Initialize the text generation pipeline
 #pipe = pipeline("text2text-generation", model="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF",token=os.getenv('HF_KEY'))
-llm = Llama(
-    model_path="Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf",
-)
 
-@app.get("/")
-def home():
-    print("helloe here")
-    output= llm("What is the difference btw RAG and Fine tunning", max_tokens=1000)
-    print(output["choices"][0]["text"])
-    ## return the generate text in Json reposne
-    return {"output":output["choices"][0]["text"]}
-
-# Define a function to handle the GET request at `/generate`
-
-
-@app.get("/generate")
-def generate(text:str):
-    ## use the pipeline to generate text from given input text
-    print("Recieved prompt "+str(text))
-    output= llm(text, max_tokens=1000)
-    print(output["choices"][0]["text"])
-    ## return the generate text in Json reposne
-    return {"output":output["choices"][0]["text"]}
+
+# @app.get("/")
+# def home():
+#     print("helloe here")
+#     output= llm("What is the difference btw RAG and Fine tunning", max_tokens=1000)
+#     print(output["choices"][0]["text"])
+#     ## return the generate text in Json reposne
+#     return {"output":output["choices"][0]["text"]}
+
+# # Define a function to handle the GET request at `/generate`
+
+
+# @app.get("/generate")
+# def generate(text:str):
+#     ## use the pipeline to generate text from given input text
+#     print("Recieved prompt "+str(text))
+#     output= llm(text, max_tokens=1000)
+#     print(output["choices"][0]["text"])
+#     ## return the generate text in Json reposne
+#     return {"output":output["choices"][0]["text"]}