Update app.py

app.py

@@ -1,31 +1,42 @@
 import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
 import json
 
-qs = {}
+# Load model and tokenizer
+model_name = "microsoft/DialoGPT-large"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
-def app_inits():
-    global qs
-    
-    # List of JSON file names
-    filenames = ['fileone.json', 'filesecond.json', 'filethird.json', 'filefourth.json', 'filefifth.json']
+# Load JSON files
+json_files = [
+    "fileone.json",
+    "filesecond.json",
+    "filethird.json",
+    "filefourth.json",
+    "filefifth.json"
+]
 
-    # Load data from each JSON file
-    for filename in filenames:
-        with open(filename, 'r') as file:
-            data = json.load(file)
-            qs.update(data)
-        
-    print("Loaded Q & A")
-    print("JSON Files:", filenames)
+json_data = {}
+for file in json_files:
+    with open(file, 'r') as f:
+        json_data.update(json.load(f))
 
-app_inits()
+def chatbot(input_text):
+    # Tokenize input text
+    input_ids = tokenizer.encode(input_text, return_tensors='pt')
 
-def bot(text):
-    global qs
-    
-    bot_message = qs.get(text, "Sorry, I couldn't find an answer to your question.")
-    return bot_message
+    # Generate response
+    response_ids = model.generate(input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
 
-chatbot = gr.Chatbot(bot)
+    # Decode response and return
+    return tokenizer.decode(response_ids[0], skip_special_tokens=True)
 
-gr.Interface(fn=bot, inputs="text", outputs="text").launch()
+iface = gr.Interface(
+    fn=chatbot,
+    inputs=gr.inputs.Textbox(lines=7, label="Input"),
+    outputs="text",
+    title="Chatbot",
+    description="An AI chatbot that can understand and respond to your queries."
+)
+
+iface.launch()