Upload 2 files

app.py

@@ -1,7 +1,5 @@
-from langchain_huggingface import HuggingFaceEndpoint
-repo_id="mistralai/Mistral-7B-Instruct-v0.2"
-
-llm=HuggingFaceEndpoint(repo_id=repo_id,max_length=128,temperature=0.7)
+from fastapi import FastAPI
+from transformers import pipeline
  
 # Create a new FastAPI app instance
 app = FastAPI()
@@ -9,19 +7,8 @@ app = FastAPI()
 # Initialize the text generation pipeline
 # This function will be able to generate text
 # given an input.
-
-
-from langchain import PromptTemplate, LLMChain
-
-
-template = """Question: {question}
-Answer: Let's think step by step."""
-prompt = PromptTemplate(template=template, input_variables=["question"])
-llm_chain=LLMChain(llm=llm,prompt=prompt)
-
-@app.get("/")
-def read_root():
-    return {"Hello": "World"}
+pipe = pipeline("text2text-generation", 
+model="google/flan-t5-small")
  
 # Define a function to handle the GET request at `/generate`
 # The generate() function is defined as a FastAPI route that takes a 
@@ -35,7 +22,7 @@ def generate(text: str):
     can be found [here](<https://huggingface.co/google/flan-t5-small>).
     """
     # Use the pipeline to generate text from the given input text
-    output=llm_chain.invoke(text)
+    output = pipe(text)
      
     # Return the generated text in a JSON response
-    return {"output": output["text"]}
+    return {"output": output[0]["generated_text"]}

requirements.txt

@@ -4,5 +4,3 @@ uvicorn[standard]==0.17.*
 sentencepiece==0.1.*
 torch==1.11.*
 transformers==4.*
-langchain-huggingface
-langchain