Update app.py

app.py

@@ -1,47 +1,41 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
-
-
-title = "LocalAI ChatBot"
-description = "A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)"
-examples = [["How are you?"]]
-
-
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
-model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
-
-
-def predict(input, history=[]):
-    # tokenize the new 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=4000, pad_token_id=tokenizer.eos_token_id
-    ).tolist()
-
-    # convert the tokens to text, and then split the responses into lines
-    response = tokenizer.decode(history[0]).split("<|endoftext|>")
-    # print('decoded_response-->>'+str(response))
-    response = [
-        (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)
-    ]  # convert to tuples of list
-    # print('response-->>'+str(response))
-    return response, history
-
-
-gr.Interface(
-    fn=predict,
-    title=title,
-    description=description,
-    examples=examples,
-    inputs=["text", "state"],
-    outputs=["chatbot", "state"],
-    theme="coding-alt/soft",
-).launch()
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+
+# Load a pre-trained model and tokenizer from Hugging Face
+model_name = "sentence-transformers/all-MiniLM-L6-v2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Sample dataset of questions and answers
+dataset = [
+    ("What is the capital of France?", "Paris is the capital of France."),
+    ("Who is the creator of Python?", "Guido van Rossum created Python."),
+    ("What is the tallest mountain in the world?", "Mount Everest is the tallest mountain in the world."),
+]
+
+# Function to find the most relevant answer
+def find_most_relevant_answer(question):
+    question_embedding = model(**tokenizer(question, return_tensors="pt", padding=True, truncation=True))[0].mean(dim=1).detach().numpy()
+    
+    highest_similarity = -1
+    most_relevant_answer = ""
+    
+    for q, a in dataset:
+        answer_embedding = model(**tokenizer(q, return_tensors="pt", padding=True, truncation=True))[0].mean(dim=1).detach().numpy()
+        similarity = cosine_similarity([question_embedding], [answer_embedding])[0][0]
+        
+        if similarity > highest_similarity:
+            highest_similarity = similarity
+            most_relevant_answer = a
+            
+    return most_relevant_answer
+
+# Set up Gradio interface
+def chat_with_bot(question):
+    return find_most_relevant_answer(question)
+
+iface = gr.Interface(fn=chat_with_bot, inputs="text", outputs="text", title="Simple QA Chatbot")
+iface.launch()