add groq api

app.py

@@ -7,35 +7,14 @@ from flask import Flask, jsonify, render_template, request, send_file
 import base64
 import numpy as np
 import pytesseract
-from huggingface_hub import InferenceClient
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-
-model_name = "microsoft/Phi-3-mini-4k-instruct"
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-
-pipe = pipeline( 
-    "text-generation", 
-    model=model, 
-    tokenizer=tokenizer, 
-) 
-
-generation_args = { 
-    "max_new_tokens": 500, 
-    "return_full_text": False, 
-    "temperature": 0.0, 
-    "do_sample": False, 
-} 
+from groq import Groq
 
 app = Flask(__name__)
 
-
+HF_API_KEY=os.getenv("HF_API_KEY")
+print(HF_API_KEY)
+client = Groq(api_key=HF_API_KEY)
+print(client)
 
 
 
@@ -79,10 +58,13 @@ def analyse():
 	{ "role": "user", "content": f"{item}: {text}" }
 ]
 
-    output = pipe(messages, **generation_args) 
-
-    print("LLM", output[0]['generated_text'], " - done")
-    return output[0]['generated_text']
+    completion = client.chat.completions.create(
+    model="llama3-8b-8192", 
+	messages=messages, 
+	max_tokens=500
+)
+    print("LLM", str(completion.choices[0].message.content), " - done")
+    return str(completion.choices[0].message.content)
 
 
 def data_uri_to_image(data_uri):