api con gradio

README.md

@@ -1,12 +1,12 @@
 ---
-title: Real State Api G
-emoji: 🦀
-colorFrom: red
+title: Real State Api 
+emoji: 🏢
+colorFrom: yellow
 colorTo: green
-sdk: gradio
-sdk_version: 4.15.0
-app_file: app.py
+sdk: docker
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# D360 Challenge
+
+

app/agents/real_state_agent.py

@@ -0,0 +1,16 @@
+from langchain.agents.agent_types import AgentType
+from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
+from langchain_openai import ChatOpenAI
+from app.utils.data_preparation import load_data
+
+
+df = load_data()
+
+
+real_state_agent = create_pandas_dataframe_agent(
+    llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613"),
+    df=df,
+    # prefix = prefix,
+    verbose=True,
+    agent_type=AgentType.OPENAI_FUNCTIONS,
+)

app/models/prediction_models.py

@@ -0,0 +1,9 @@
+from pydantic import BaseModel
+
+
+class PredictionRequest(BaseModel):
+    question: str
+
+
+class PredictionResponse(BaseModel):
+    response: str

app/routes/home.py

@@ -0,0 +1,8 @@
+from fastapi import APIRouter
+
+router = APIRouter()
+
+
+@router.get("/")
+def read_root():
+    return {"message": "D360 Challenge by Santiago Battezzati"}

app/routes/prediction.py

@@ -0,0 +1,24 @@
+from fastapi import APIRouter
+from app.models.prediction_models import PredictionRequest, PredictionResponse
+from typing import List
+from app.agents.real_state_agent import real_state_agent
+
+
+import os
+import openai
+
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+
+router = APIRouter()
+
+
+## MODIFICAR ESTO:
+@router.post("/", response_model=List[PredictionResponse])
+def predict(request: PredictionRequest):
+    response = real_state_agent.run(request.question)
+
+    # ver el type esto sigue siendo list, pero ahora dije que queiro una response string:
+    response_data = [{"response": response}]
+
+    return response_data

app/utils/data_preparation.py

@@ -0,0 +1,6 @@
+import pandas as pd
+
+
+def load_data():
+    df = pd.read_csv("app/data/listings.csv")
+    return df

main.py

@@ -0,0 +1,25 @@
+from fastapi import FastAPI
+from app.routes import prediction
+from app.routes.prection import predict
+
+from app.routes import home
+import gradio as gr
+
+
+app = FastAPI()
+
+
+app.include_router(
+    home.router,
+    tags=["home"],
+)
+
+
+app.include_router(
+    prediction.router,
+    prefix="/predict",
+    tags=["prediction"],
+)
+
+iface = gr.Interface(fn=predict, inputs="text", outputs="text")
+iface.launch()

requirements.txt

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+fastapi==0.108.0
+langchain==0.1.1
+langchain-experimental==0.0.49
+langchain-openai==0.0.3
+openai==1.8.0
+pandas==2.1.4
+pydantic==2.5.3
+pydantic_core==2.14.6
+#jupyterlab==4.0.10
+tabulate==0.9.0
+uvicorn==0.25.0
+
+
+### 
+## agregar open ai y langchain y langchain experimental... 
+## tabulate... 
+