Removed requirements ... more pt 2

app.py

@@ -1,131 +0,0 @@
-from fastapi import FastAPI, HTTPException
-from fastapi.middleware.cors import CORSMiddleware
-from pydantic import BaseModel
-from typing import List, Optional
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import re
-import os
-
-app = FastAPI()
-
-# Enable CORS for frontend access
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],  # Or specify your frontend domain
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# Hugging Face model config
-REPO_NAME = "jaydatech/phi3-finetuned-project"
-HF_TOKEN = "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"  # Hardcoded token
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-tokenizer = AutoTokenizer.from_pretrained(REPO_NAME, token=HF_TOKEN)
-model = AutoModelForCausalLM.from_pretrained(
-    REPO_NAME,
-    token=HF_TOKEN,
-    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-    device_map="auto"
-)
-
-# Message and request models
-class ChatMessage(BaseModel):
-    role: str
-    text: str
-
-class ChatRequest(BaseModel):
-    message: str
-    history: Optional[List[ChatMessage]] = []
-
-def is_farewell(message: str) -> bool:
-    farewells = ["bye", "goodbye", "see you", "farewell", "exit", "quit", "end"]
-    message_lower = message.lower().strip()
-    return any(farewell in message_lower for farewell in farewells)
-
-@app.post("/chat")
-async def chat(request: ChatRequest):
-    try:
-        history = request.history
-        user_message = request.message
-
-        if is_farewell(user_message):
-            return {
-                "response": "Goodbye! Feel free to chat again if you have more questions.",
-                "terminate": True
-            }
-
-        conversation = (
-            "<|system|>\nYou are an AI assistant for the Federal Reserve Bank of St. Louis. "
-            "Answer questions based ONLY on your knowledge of the Federal Reserve Bank of St. Louis. "
-            "If the answer is NOT in the training data, respond with: 'I don't think this information is available. Maybe rephrase for me!'. "
-            "Answer ONLY what the user asks. Do not volunteer information unless specifically requested. "
-            "Do NOT ask: 'How can I assist you today?' after every response you give. "
-            "Provide concise answers to the exact question asked and nothing more.\n"
-        )
-
-        seen_messages = set()
-        for msg in history:
-            if msg.role == "user" and msg.text.strip() not in seen_messages:
-                conversation += f"<|user|>\n{msg.text.strip()}\n"
-                seen_messages.add(msg.text.strip())
-            elif msg.role == "model":
-                conversation += f"<|assistant|>\n{msg.text.strip()}\n"
-
-        conversation += f"<|user|>\n{user_message.strip()}\n<|assistant|>"
-
-        inputs = tokenizer(conversation, return_tensors="pt", padding=True, truncation=True, max_length=4096).to(device)
-
-        with torch.no_grad():
-            outputs = model.generate(
-                **inputs,
-                max_new_tokens=130,
-                do_sample=True,
-                temperature=0.1,
-                top_k=5,
-                pad_token_id=tokenizer.eos_token_id
-            )
-
-        full_response = tokenizer.decode(outputs[0], skip_special_tokens=False)
-        assistant_response = ""
-
-        if "<|assistant|>" in full_response:
-            assistant_sections = full_response.split("<|assistant|>")
-            for section in reversed(assistant_sections):
-                cleaned = section.strip()
-                if cleaned:
-                    cleaned = re.split(
-                        r"(<\|user\|>|<\|system\|>|<\|assistant\|>|\nuser[:\s]|<\|endoftext\|>)",
-                        cleaned
-                    )[0]
-                    cleaned = re.sub(r"\n?(User|Assistant)\s*[:：\-–]\s*.*", "", cleaned, flags=re.IGNORECASE).strip()
-                    assistant_response = cleaned
-                    break
-
-        if not assistant_response:
-            assistant_response = "⚠️ Sorry, I couldn't generate a response."
-
-        assistant_response = re.sub(r'\*\* Instruction \*\*:.*?(?=\n\n|\n$|$)', '', assistant_response, flags=re.DOTALL)
-        assistant_response = re.sub(r'\*\* Instruction \*\*.*?(?=\n\n|\n$|$)', '', assistant_response, flags=re.DOTALL)
-        assistant_response = re.sub(r'\n{3,}', '\n\n', assistant_response).strip()
-        assistant_response = re.sub(r"(How can I assist you today\?|What else can I help you with\?|How can I help you today\?)", "", assistant_response, flags=re.IGNORECASE).strip()
-
-        def remove_repeated_sentences(response):
-            sentences = response.split(". ")
-            seen = set()
-            cleaned = []
-            for sentence in sentences:
-                if sentence not in seen:
-                    cleaned.append(sentence)
-                    seen.add(sentence)
-            return ". ".join(cleaned)
-
-        assistant_response = remove_repeated_sentences(assistant_response)
-
-        return {"response": assistant_response}
-
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
+size 499723