Update app.py

app.py

@@ -1,64 +1,119 @@
+# Hugging Face Space Adaptation for Autistic Assistant 2024 Ultra
+
+# Install necessary libraries (if running locally)
+# !pip install transformers torch textblob numpy gradio
+
+# Import necessary libraries
+import torch
+import random
+import torch.nn as nn
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+from textblob import TextBlob
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+import pickle
+import numpy as np
+
+# ---- Constants and Setup ----
+model_name = 'gpt2'
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+model = GPT2LMHeadModel.from_pretrained(model_name)
+model.eval()
+
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+tokenizer.clean_up_tokenization_spaces = True
+
+# Ensure model and tensors are moved to the GPU if available
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.to(device)
+
+# ---- Memory Management ----
+session_memory = []
+
+def save_memory(memory, filename='chat_memory.pkl'):
+    with open(filename, 'wb') as f:
+        pickle.dump(memory, f)
+
+def load_memory(filename='chat_memory.pkl'):
+    try:
+        with open(filename, 'rb') as f:
+            return pickle.load(f)
+    except FileNotFoundError:
+        return []
+
+session_memory = load_memory()
+
+# ---- Sentiment Analysis ----
+def analyze_sentiment(text):
+    blob = TextBlob(text)
+    return blob.sentiment.polarity  # Range from -1 (negative) to 1 (positive)
+
+def adjust_for_emotion(response, sentiment):
+    if sentiment > 0.2:
+        return f"That's wonderful! I'm glad you're feeling good: {response}"
+    elif sentiment < -0.2:
+        return f"I'm truly sorry to hear that: {response}. How can I make it better?"
+    return response
+
+# ---- Response Generation ----
+def generate_response(prompt, max_length=1024):
+    inputs = tokenizer(prompt, return_tensors='pt', padding=True, truncation=True, max_length=max_length)
+    input_ids = inputs['input_ids'].to(device)
+    attention_mask = inputs['attention_mask'].to(device)
+    pad_token_id = tokenizer.pad_token_id
+
+    with torch.no_grad():
+        output = model.generate(
+            input_ids,
+            attention_mask=attention_mask,
+            max_length=max_length,
+            num_return_sequences=1,
+            no_repeat_ngram_size=2,
+            do_sample=True,
+            temperature=0.9,
+            top_k=50,
+            top_p=0.95,
+            early_stopping=False,
+            pad_token_id=pad_token_id
+        )
+
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    return response.strip()
+
+# ---- Interactive Chat Function ----
+def advanced_agi_chat(user_input):
+    # Add user input to session memory
+    session_memory.append({"input": user_input})
+    save_memory(session_memory)
+
+    # Sentiment analysis
+    user_sentiment = analyze_sentiment(user_input)
+
+    # Generate the response
+    prompt = f"User: {user_input}\nAutistic-Assistant:"
+    response = generate_response(prompt)
+
+    # Adjust response for emotional alignment
+    adjusted_response = adjust_for_emotion(response, user_sentiment)
+
+    return adjusted_response
+
+# ---- Gradio Interface ----
+def chat_interface(user_input):
+    response = advanced_agi_chat(user_input)
+    return response
+
+with gr.Blocks() as app:
+    gr.Markdown("# Autistic Assistant 2024 Ultra")
+    with gr.Row():
+        with gr.Column():
+            user_input = gr.Textbox(label="Your Message", placeholder="Type something here...")
+            submit_button = gr.Button("Send")
+        with gr.Column():
+            chatbot = gr.Textbox(label="Assistant Response", interactive=False)
+
+    submit_button.click(chat_interface, inputs=user_input, outputs=chatbot)
+
+# Launch the Gradio app
+app.launch()