Update app.py

app.py

@@ -1,79 +1,51 @@
 import gradio as gr
-from fastapi import FastAPI, Query
-from fastapi.middleware.wsgi import WSGIMiddleware
-import uvicorn
-from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import urllib.parse
 
-# Load model and tokenizer once
-model_id = "microsoft/DialoGPT-medium"
+# Load model and tokenizer
+model_id = "microsoft/phi-2"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id)
 
-# Your AI persona prompt
-PERSONA = """
-[System: You are 𝕴 𝖆𝖒 𝖍𝖎𝖒 - a fun, smooth, emotionally intelligent AI.
-You speak like a real person, not a robot. Keep it under 15 words. 😊😏]
-"""
+# Global memory for all users
+chat_history = {}
 
+# Format past messages
 def format_context(history):
-    context = PERSONA + "\n"
-    if not history:
-        return context
-    # Use only last 3 exchanges to keep it short
-    for user, bot in history[-3:]:
+    context = ""
+    for user, bot in history[-3:]:  # Last 3 exchanges
         context += f"You: {user}\n𝕴 𝖆𝖒 𝖍𝖎𝖒: {bot}\n"
     return context
 
-def enhance_response(resp, message):
-    if any(x in message.lower() for x in ["?", "think", "why"]):
-        resp += " 🤔"
-    elif any(x in resp.lower() for x in ["cool", "great", "love", "fun"]):
-        resp += " 😏"
-    return " ".join(resp.split()[:15])
-
-def generate_ai_reply(user_input, history):
-    context = format_context(history) + f"You: {user_input}\n𝕴 𝖆𝖒 𝖍𝖎𝖒:"
-    inputs = tokenizer.encode(context, return_tensors="pt", truncation=True, max_length=1024)
-
-    outputs = model.generate(
-        inputs,
-        max_new_tokens=50,
-        temperature=0.9,
-        top_k=40,
-        do_sample=True,
-        pad_token_id=tokenizer.eos_token_id
-    )
+# Main chat function with memory per user
+def chat_with_memory(query_string):
+    parsed = urllib.parse.parse_qs(query_string)
+    user_input = parsed.get("query", [""])[0]
+    user_id = parsed.get("user_id", ["default"])[0]
 
-    full_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    response = full_text.split("𝕴 𝖆𝖒 𝖍𝖎𝖒:")[-1].split("\nYou:")[0].strip()
-    response = enhance_response(response, user_input)
-    return response
+    # Get or init user history
+    history = chat_history.get(user_id, [])
 
-app = FastAPI()
+    # Format prompt
+    context = format_context(history) + f"You: {user_input}\n𝕴 𝖆𝖒 𝖍𝖎𝖒:"
 
-# GET /ai?query=some+text  => returns {"reply": "AI reply here"}
-@app.get("/ai")
-async def ai_endpoint(query: str = Query(..., min_length=1)):
-    # For stateless API calls, history is empty (or you can extend to save history)
-    reply = generate_ai_reply(query, history=[])
-    return {"reply": reply}
+    # Tokenize & generate
+    inputs = tokenizer(context, return_tensors="pt", return_attention_mask=True)
+    outputs = model.generate(**inputs, max_new_tokens=100, pad_token_id=tokenizer.eos_token_id)
+    reply = tokenizer.decode(outputs[0], skip_special_tokens=True).split("𝕴 𝖆𝖒 𝖍𝖎𝖒:")[-1].strip()
 
-# Gradio chat interface for interactive web UI
-def chat(user_input, history):
-    history = history or []
-    reply = generate_ai_reply(user_input, history)
+    # Save memory
     history.append((user_input, reply))
-    return history, history
+    chat_history[user_id] = history[-10:]
 
-with gr.Blocks() as demo:
-    chatbot = gr.Chatbot()
-    msg = gr.Textbox(placeholder="Say something...")
-    state = gr.State()
-    msg.submit(chat, inputs=[msg, state], outputs=[chatbot, state])
+    return {"reply": reply}
 
-# Mount Gradio UI at root
-app.mount("/", WSGIMiddleware(demo.launch(prevent_thread_lock=True, share=False)))
+# Create public /ai?query=&user_id=
+iface = gr.Interface(
+    fn=chat_with_memory,
+    inputs="text",  # URL query string
+    outputs="json"
+)
 
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+iface.launch()