Fix

.gitignore

@@ -1,2 +1,3 @@
 **/venv
-**/__pycache__
+**/__pycache__
+**/.env

app.py

@@ -3,7 +3,7 @@ from dotenv import load_dotenv
 import gradio as gr
 from gradio.components import Textbox, Button, Slider, Image
 from AinaTheme import AinaGradioTheme
-from meteocat_appv4 import generate
+from meteocat_app import generate
 import csv
 
 load_dotenv()
@@ -33,7 +33,7 @@ def submit_input(input_, repetition_penalty, temperature):
                    És possible que no hagi trobat el lloc o la data. 
                    Només puc respondre a preguntes sobre el temps a alguna localitat en concret.
                    """)
-        return "", "", ""
+        return None, None, None
     
     data_as_dict = csv_to_dict("./code2simbol.csv")
     codes = outputs["context"]
@@ -203,8 +203,5 @@ with gr.Blocks(**AinaGradioTheme().get_kwargs()) as demo:
         outputs=[output_answer, output_image, output_CCMA]
     )
 
-    # clear_btn.click(fn=clean, inputs=[], outputs=[input_, output_answer, output_context, output_CCMA,  repetition_penalty, temperature], queue=False)
-    # submit_btn.click(fn=submit_input, inputs=[input_, repetition_penalty, temperature], outputs=[output_answer, output_context, output_CCMA])
 
-    # demo.queue(concurrency_count=1, api_open=False)
-    demo.launch(show_api=True, share=True, debug=True, max_threads=1)
+    demo.launch(show_api=True)

meteocat_app.py

@@ -0,0 +1,175 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Aug 17 12:11:26 2023
+
+@author: crodrig1
+"""
+from optparse import OptionParser
+import sys, re, os
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from pymongo import MongoClient
+from pprint import pprint
+import torch
+import warnings
+import re, string
+from dotenv import load_dotenv
+
+load_dotenv()
+
+MONGO_URI = os.environ.get("MONGO_URI")
+
+
+warnings.filterwarnings("ignore")
+
+tokenizer = AutoTokenizer.from_pretrained("crodri/bloom1.3_meteo")
+
+
+from transformers import BitsAndBytesConfig
+
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    llm_int8_enable_fp32_cpu_offload=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_use_double_quant=True,
+)
+model_4bit = AutoModelForCausalLM.from_pretrained(
+        "crodri/bloom1.3_meteo",
+        model_type="BloomForCausalLM",
+        device_map="cpu",
+#        verbose=False,
+        # quantization_config=quantization_config,
+        trust_remote_code=True)
+# #tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# tokenizer = AutoTokenizer.from_pretrained(model_4bit)
+
+llm_pipeline = pipeline(
+        "text-generation",
+        model=model_4bit,
+        tokenizer=tokenizer,
+        use_cache=True,
+        device_map="auto",
+        #max_length=800,
+        do_sample=True,
+        top_k=10,
+        num_return_sequences=1,
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.eos_token_id,
+)
+
+
+def retrieveFor(result):
+    def retrievehighest(key,result):
+        try:
+            candidates = [x for x in result if (x["entity_group"] == key)]
+            topone  = max(candidates, key=lambda x:x['score'])
+            return topone['word'].strip()
+        except ValueError:
+            return []
+    def removeend(frase):
+        frase = re.sub("\?","",frase)
+        frase = re.sub(",","",frase)
+        return frase
+        
+    intervalo = [x["word"].strip() for x in result if (x["entity_group"] == "interval")]
+    client = MongoClient(MONGO_URI)
+    db = client['aina']
+    collection = db['new_ccma_meteo']
+    try:
+        location = removeend(retrievehighest("location",result))
+        day = removeend(retrievehighest("day",result))
+    except TypeError:
+        print("No hem trobat el lloc o la data. Torna a provar")
+        return None
+    record = collection.find({"location":location.strip(),"day":day.lower().strip()})
+    try:
+        j = record.next()
+        if intervalo:
+            return (j,intervalo[0])
+        return (j,'tot el dia')
+    except:
+        print("No hem trobat el lloc o la data. Torna a provar")
+        return None
+#context": "Day: dilluns | Location: Sant Salvador de Guardiola | mati: la nuvolositat anirà en augment | tarda: els núvols alts taparan el cel | nit: cel clar | temp: Lleugera pujada de les temperatures"
+
+pipe = pipeline("token-classification", model="crodri/ccma_ner",aggregation_strategy='first')
+
+intent = pipeline("text-classification", model="projecte-aina/roberta-large-ca-v2-massive")
+
+
+def pipeIt(jresponse):
+    regex = re.compile('[%s]' % re.escape(string.punctuation))
+    d = jresponse[0]
+    #i = jresponse[-1]
+    #i = regex.sub('', i)
+    #context = i +" del "+ db["day"]+" a "+db["location"]+" al mati "+db["mati"]+", "+"a la tarda "+db["tarde"]+", a la nit "+db["nit"] +", i "+db["temperature"]
+    #context = d["day"]+" a "+d["location"]+": al mati "+d["mati"]+", "+"a la tarda "+d["tarde"]+", a la nit "+d["nit"] +", i "+d["temperature"]
+    return d["context"]
+
+#question = "Quin temps farà a la tarda a Algete dijous?"
+
+def givePrediction(question, context,temperature,repetition):
+    instruction = question
+    text = f"### Instruction\n{{instruction}}\n\n### Context\n{{context}}\n\n### Answer\n"
+    response = llm_pipeline(text.format(instruction=instruction, context=context),temperature=temperature,repetition_penalty=repetition, max_new_tokens=100)[0]["generated_text"]
+    answer = response.split("###")[-1][8:]
+    return answer
+
+def assistant(question):
+    is_intent = intent(question)[0]
+    if is_intent['label'] == 'weather_query':
+        result = pipe(question)
+        jresponse = retrieveFor(result)
+        if jresponse:
+            context = jresponse[0]['context']#pipeIt(jresponse)
+            #jresponse[0]['context'] = context
+            print("Context: ",context)
+            print()
+            return jresponse
+    elif is_intent['label'] in ["general_greet","general_quirky"]:
+        print("Hola, quina es la teva consulta meteorològica?")
+        #sys.exit(0)
+    else:
+        print(is_intent['label'])
+        print("Ho sento. Jo només puc respondre a preguntes sobre el temps a alguna localitat en concret ...")
+        #sys.exit(0)
+    return None
+def generate(question,temperature,repetition):
+    jresponse = assistant(question)
+    #print(jresponse)
+    if jresponse:
+        codes = jresponse[0]['codis']
+        interval = jresponse[1]
+        context = {"codis": codes, "interval": interval}
+        # context = jresponse[0]['context']
+        ccma_response = jresponse[0]['response']
+        answer = givePrediction(question, context,temperature,repetition)
+        print("CCMA generated: ",ccma_response)
+        print("="*16)
+        print("LLM answer: ",answer)
+        print()
+
+        return {"context": context, "ccma_response": ccma_response, "model_answer": answer}
+    else:
+        print("No response")
+        return None
+
+def main():
+    parser = OptionParser()
+    parser.add_option("-q", "--question", dest="question", type="string",
+                  help="question to test", default="Quin temps farà a la tarda a Algete dijous?")
+    parser.add_option("-t", "--temperature", dest="temperature", type="float",
+                  help="temperature generation", default=1.0)
+    parser.add_option("-r", "--repetition", dest="repetition", type="float",
+                  help="repetition penalty", default=1.0)
+    (options, args) = parser.parse_args(sys.argv)
+    print(options)
+    #question = options.question
+    #print(question)
+    answer = generate(options.question,options.temperature,options.repetition)
+    #print(answer)
+
+if __name__ == "__main__":
+    main()