Update app.py

app.py

@@ -1,25 +1,14 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
 import os
+from groq import Groq
 
-# Get the Hugging Face token from environment variable
-hf_token = os.environ.get('MISTRAL_ACCESS_TOKEN')
-
-# Load the Mistral 7B Instruct model and tokenizer from Hugging Face
-model_name = "mistralai/Mistral-7B-Instruct-v0.3"
-tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token)
-model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto", token=hf_token)
-
-# Function to generate text using the Mistral model
-def generate_text(prompt, max_length=500):
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-    outputs = model.generate(**inputs, max_length=max_length, num_return_sequences=1)
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+# Initialize the Groq client
+client = Groq(api_key=os.environ["GROQ_API_KEY"])
 
 # Function to generate personalized learning plan
 def generate_learning_plan(name, age, subject, learning_style, strengths, weaknesses):
-    prompt = f"""<s>[INST] Create a personalized learning plan for a student with the following details:
+    prompt = f"""
+    Create a personalized learning plan for a student with the following details:
     Name: {name}
     Age: {age}
     Subject: {subject}
@@ -32,10 +21,24 @@ def generate_learning_plan(name, age, subject, learning_style, strengths, weakne
     2. Suggested resources
     3. Personalized learning goals
     4. Strategies to address weaknesses
-    5. Ways to leverage strengths [/INST]"""
+    5. Ways to leverage strengths
+    """
+    
+    # Call the Groq API to generate text
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {
+                "role": "user",
+                "content": prompt,
+            }
+        ],
+        model="mixtral-8x7b-32768",  # You can change this to the model you prefer
+        temperature=0.7,
+        max_tokens=2000,
+    )
     
-    response = generate_text(prompt)
-    return response
+    # Extract and return the generated text
+    return chat_completion.choices[0].message.content
 
 # Gradio interface
 iface = gr.Interface(
@@ -54,5 +57,4 @@ iface = gr.Interface(
 )
 
 # Launch the Gradio app
-if __name__ == "__main__":
-    iface.launch()
+iface.launch()