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main.py

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+
+import torch
+from fastapi import FastAPI
+from pydantic import BaseModel
+from duckduckgo_search import ddg
+import chromadb
+from sentence_transformers import SentenceTransformer
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# ===============================
+# 1️⃣ Load Model (Llama-3-8B-Instruct)
+# ===============================
+MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct"
+
+print("🚀 Loading Billy AI model...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.float32,  # CPU-friendly
+    device_map="auto"
+)
+
+def generate_text(prompt: str, max_tokens: int = 512) -> str:
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    output = model.generate(
+        **inputs,
+        max_new_tokens=max_tokens,
+        do_sample=True,
+        temperature=0.7,
+        top_p=0.9
+    )
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+# ===============================
+# 2️⃣ Setup RAG (Memory + Search)
+# ===============================
+db = chromadb.PersistentClient(path="./billy_rag_db")
+try:
+    collection = db.get_collection("billy_rag")
+except:
+    collection = db.create_collection("billy_rag")
+
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+
+def search_web(query: str):
+    try:
+        results = ddg(query, max_results=3)
+        return [r.get("body") or r.get("snippet") or "" for r in results if r]
+    except:
+        return []
+
+def store_knowledge(text: str):
+    vec = embedder.encode(text).tolist()
+    try:
+        collection.add(documents=[text], embeddings=[vec], ids=[str(abs(hash(text)))])
+    except:
+        pass
+
+def retrieve_knowledge(query: str) -> str:
+    vec = embedder.encode(query).tolist()
+    results = collection.query(query_embeddings=[vec], n_results=3)
+    return " ".join(results["documents"][0]) if results and results["documents"] else ""
+
+# ===============================
+# 3️⃣ Tool Functions
+# ===============================
+def summarize_text(text: str) -> str:
+    prompt = f"Summarize the following text in simple terms:\n\n{text}\n\nSummary:"
+    return generate_text(prompt, max_tokens=200)
+
+def translate_text(text: str, lang: str) -> str:
+    prompt = f"Translate the following text to {lang}:\n\n{text}\n\nTranslation:"
+    return generate_text(prompt, max_tokens=200)
+
+def explain_code(code: str) -> str:
+    prompt = f"Explain the following code in simple terms:\n\n```{code}```\n\nExplanation:"
+    return generate_text(prompt, max_tokens=300)
+
+# ===============================
+# 4️⃣ FastAPI App
+# ===============================
+app = FastAPI(title="Billy AI - Free Chatbot")
+
+class Query(BaseModel):
+    message: str
+    user_id: str = "anonymous"
+
+@app.post("/chat")
+def chat(req: Query):
+    user_msg = req.message.strip()
+
+    # --- Special Commands ---
+    if user_msg.lower().startswith("/summarize "):
+        return {"response": summarize_text(user_msg[11:])}
+
+    if user_msg.lower().startswith("/translate "):
+        try:
+            lang, text = user_msg[10:].split(" ", 1)
+            return {"response": translate_text(text, lang)}
+        except:
+            return {"response": "Format: /translate <language> <text>"}
+
+    if user_msg.lower().startswith("/explaincode "):
+        return {"response": explain_code(user_msg[13:])}
+
+    # --- Search & RAG ---
+    local_knowledge = retrieve_knowledge(user_msg)
+
+    if not local_knowledge:
+        web_results = search_web(user_msg)
+        for r in web_results:
+            if r.strip():
+                store_knowledge(r)
+        local_knowledge = " ".join(web_results)
+
+    # --- Personality & Context ---
+    context = (
+        "You are Billy AI — a helpful, witty, and slightly funny AI assistant. "
+        "You are a bit smarter than GPT-3.5, but not too advanced. "
+        "When answering, be friendly, concise, and give useful info. "
+        f"Use this info if helpful: {local_knowledge}\n\n"
+        f"User: {user_msg}\nAssistant:"
+    )
+
+    reply = generate_text(context)
+    return {"response": reply.strip()}
+
+@app.get("/")
+def home():
+    return {"message": "Billy AI is running and ready to chat!"}
+
+# ===============================
+# 5️⃣ Run
+# ===============================
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

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+fastapi
+uvicorn
+torch
+transformers
+sentence-transformers
+chromadb
+duckduckgo-search
+pydantic
+huggingface_hub