Update app.py

app.py

@@ -1,43 +1,54 @@
-import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
+import gradio as gr
 import torch
 import json
 
-title = "AI ChatBot"
-description = "A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)"
-examples = [["How are you?"]]
+title = "Smart AI ChatBot"
+description = "A conversational model capable of intelligently answering questions (DialoGPT)"
+examples = [["How are you?"], ["What's the weather like?"]]
 
+# Load DialoGPT model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
 model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
 
-def predict(input, history=[], file_path=None):
-    if file_path:
-        json_data = read_json_file(file_path)
-        print(f"Contents of {file_path}:")
-        print(json_data)
-        print()
-    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors="pt")
-    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
-    history = model.generate(bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id).tolist()
-    response = tokenizer.decode(history[0]).split("\n")  # Splitting on new lines
-    return response[0], history
+# Known question-answer pairs, you can add more as per your requirement
+known_questions_answers = {
+    "How are you?": "I'm fine, thank you for asking.",
+    "What's the weather like?": "The weather is nice today, sunny and warm.",
+    "What's your name?": "I am Smart AI ChatBot.",
+    "Do you speak English?": "I can understand and respond to English questions.",
+}
 
-def read_json_file(file_path):
-    with open(file_path, 'r') as file:
-        data = json.load(file)
-    return data
+def predict(input, history=[]):
+    response = None
+    
+    # Check if the input question is in the known question-answer pairs
+    if input in known_questions_answers:
+        response = known_questions_answers[input]
+    else:
+        # Tokenize the new user input sentence
+        new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors="pt")
+        # Append the new user input tokens to the chat history
+        bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
+        # Generate a response
+        history = model.generate(
+            bot_input_ids, max_length=400, pad_token_id=tokenizer.eos_token_id
+        ).tolist()
+        # Convert tokens to text, and split the response into lines
+        response = tokenizer.decode(history[0], skip_special_tokens=True)
+    
+    return response, history
 
 def main():
-    gr.Interface(
-        fn=predict,
-        title=title,
-        description=description,
-        examples=examples,
-        inputs=[gr.inputs.Textbox(label="User Input"), gr.inputs.File(label="JSON File")],
-        outputs=["text", "text"],
-        theme="finlaymacklon/boxy_violet",
-    ).launch()
-
+    # You can add logic here to read known question-answer pairs, for example, from a JSON file
+    pass
 
-if __name__ == "__main__":
-    main()
+gr.Interface(
+    fn=predict,
+    title=title,
+    description=description,
+    examples=examples,
+    inputs=["text", "state"],
+    outputs=["chatbot", "state"],
+    theme="finlaymacklon/boxy_violet",
+).launch()