Update app.py

app.py

@@ -1,22 +1,37 @@
-import streamlit as st
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import os
 
-# Load model and tokenizer
+# Ensure correct model and tokenizer initialization
 model_name = "kalyani2599/emotional_support_bot"
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-def chatbot_response(input_text):
-    inputs = tokenizer(input_text, return_tensors="pt")
-    outputs = model.generate(**inputs, max_length=100)
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return response
+# Clear Hugging Face cache (optional but useful if there are issues with cached files)
+cache_dir = os.path.join(os.path.expanduser("~"), ".cache", "huggingface")
+if os.path.exists(cache_dir):
+    for file in os.listdir(cache_dir):
+        file_path = os.path.join(cache_dir, file)
+        os.remove(file_path)
 
-# Streamlit UI
-st.title("Emotional Support Chatbot")
-st.write("Chat with the emotional support bot below:")
+# Load model and tokenizer
+try:
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)  # Using the fast tokenizer version
+except Exception as e:
+    print(f"Error loading model or tokenizer: {e}")
+    # If the model/tokenizer fails to load, try a different one
+    model_name = "facebook/blenderbot-3B"  # Example fallback model
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+
+def chatbot_response(input_text):
+    try:
+        inputs = tokenizer(input_text, return_tensors="pt")
+        outputs = model.generate(**inputs, max_length=100)
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return response
+    except Exception as e:
+        return f"Error in generating response: {e}"
 
-user_input = st.text_input("Your Message")
-if user_input:
-    response = chatbot_response(user_input)
-    st.text_area("Chatbot Response", response, height=150)
+# Example chatbot interaction
+input_text = "Hello, how are you?"
+response = chatbot_response(input_text)
+print(response)