Update app.py

app.py

@@ -1,23 +1,33 @@
 import os
 import streamlit as st
+import torch
 from groq import Groq
 from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 
 # Set up the Groq API Key
-GROQ_API_KEY = "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"
+GROQ_API_KEY = "your_groq_api_key_here"  # Replace with your actual key
 os.environ["GROQ_API_KEY"] = GROQ_API_KEY
 
 # Initialize the Groq client
 client = Groq(api_key=GROQ_API_KEY)
 
-# Initialize Hugging Face DeepSeek R1 model
-pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-R1", trust_remote_code=True)
+# Initialize Hugging Face DeepSeek R1 model correctly
+MODEL_NAME = "deepseek-ai/DeepSeek-R1"
 
-def generate_response_hf(user_message):
-    response = pipe(user_message, max_length=200, do_sample=True)
-    return response[0]['generated_text']
+try:
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, trust_remote_code=True, torch_dtype=torch.float16, device_map="auto")
 
-# Streamlit user interface setup
+    def generate_response_hf(user_message):
+        inputs = tokenizer(user_message, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
+        outputs = model.generate(**inputs, max_length=200)
+        return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+except Exception as e:
+    st.error(f"Error loading DeepSeek-R1: {str(e)}")
+    generate_response_hf = lambda x: "Error: Model not loaded."
+
+# Streamlit UI setup
 st.set_page_config(page_title="AI Study Assistant", page_icon="🤖", layout="wide")
 st.title("📚 Subject-specific AI Chatbot")
 st.write("Hello! I'm your AI Study Assistant. You can ask me any questions related to your subjects, and I'll try to help.")
@@ -31,7 +41,7 @@ chat_model = st.sidebar.radio("Choose AI Model:", ["Groq API", "DeepSeek R1 (Hug
 if 'conversation_history' not in st.session_state:
     st.session_state.conversation_history = []
 
-# Define a list of subjects for which the chatbot will answer
+# Define subjects
 subjects = ["Chemistry", "Computer", "English", "Islamiat", "Mathematics", "Physics", "Urdu"]
 
 def generate_chatbot_response(user_message):
@@ -51,7 +61,7 @@ def generate_chatbot_response(user_message):
     else:
         return generate_response_hf(prompt)
 
-# User input for conversation
+# User input
 st.markdown("### 💬 Chat with me")
 user_input = st.chat_input("Ask me a subject-related question:")