Update main.py

main.py

@@ -1,50 +1,27 @@
 from fastapi import FastAPI
-# from transformers import pipeline
-from txtai.embeddings import Embeddings
-from txtai.pipeline import Extractor
+from pydantic import BaseModel
 
 # NOTE - we configure docs_url to serve the interactive Docs at the root path
 # of the app. This way, we can use the docs as a landing page for the app on Spaces.
 app = FastAPI(docs_url="/")
 
-# Create embeddings model with content support
-embeddings = Embeddings({"path": "sentence-transformers/all-MiniLM-L6-v2", "content": True})
-embeddings.load('index')
+class ModelOutputEvaluate(BaseModel):
+    question: str
+    answer: str 
+    context: str | None = None
+    prompt: str
+    
 
 # Create extractor instance
-extractor = Extractor(embeddings, "google/flan-t5-base")
-
-# pipe = pipeline("text2text-generation", model="google/flan-t5-small")
-
-
-# @app.get("/generate")
-# def generate(text: str):
-#     """
-#     Using the text2text-generation pipeline from `transformers`, generate text
-#     from the given input text. The model used is `google/flan-t5-small`, which
-#     can be found [here](https://huggingface.co/google/flan-t5-small).
-#     """
-#     output = pipe(text)
-#     return {"output": output[0]["generated_text"]}
-
-
-def prompt(question):
-  return f"""Answer the following question using only the context below. Say 'no answer' when the question can't be answered.
-            Question: {question}
-            Context: """
-
-
-def search(query, question=None):
-  # Default question to query if empty
-  if not question:
-    question = query
-
-  return extractor([("answer", query, prompt(question), False)])[0][1]
-
-
-@app.get("/rag")
-def rag(question: str):
-    # question = "what is the document about?"
-    answer = search(question)
-    # print(question, answer)
-    return {answer}
+@app.post("/evaluate/")
+async def create_evaluation_scenario(item: ModelOutputEvaluate):
+    output = {
+        "input": item,
+        "score" : "0"
+    }
+    return output
+# def evaluate(question: str):
+#     # question = "what is the document about?"
+#     answer = search(question)
+#     # print(question, answer)
+#     return {answer}