Updated

Dockerfile

@@ -1,59 +1,33 @@
 # ==============================================================
-# Tech Disciples AI Backend — Dockerfile (Authenticated Build)
+# Tech Disciples AI Backend — Dockerfile (for Hugging Face Spaces)
 # ==============================================================
 
-# Use lightweight official Python image
 FROM python:3.10-slim
 
-# Set environment variables
+# Environment setup
 ENV PYTHONDONTWRITEBYTECODE=1 \
     PYTHONUNBUFFERED=1 \
     APP_HOME=/app
 
-# Set work directory
 WORKDIR $APP_HOME
 
-# --------------------------------------------------------------
-# System dependencies
-# --------------------------------------------------------------
+# Install system dependencies
 RUN apt-get update && apt-get install -y \
     git \
     && rm -rf /var/lib/apt/lists/*
 
-# --------------------------------------------------------------
-# Copy requirement file first (for caching)
-# --------------------------------------------------------------
-COPY requirements.txt .
-
-# --------------------------------------------------------------
 # Install Python dependencies
-# --------------------------------------------------------------
+COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 
-# --------------------------------------------------------------
-# Copy application files
-# --------------------------------------------------------------
+# Copy all source files
 COPY . .
 
-# --------------------------------------------------------------
-# Set up Hugging Face token (must be provided at runtime)
-# --------------------------------------------------------------
-# This expects the token to be passed in during container run:
-#   docker run -e HUGGINGFACEHUB_API_TOKEN=hf_xxx -p 7860:7860 techdisciplesai
+# Define Hugging Face token env var (Spaces injects the secret automatically)
 ENV HUGGINGFACEHUB_API_TOKEN=""
 
-# --------------------------------------------------------------
-# Authenticate to Hugging Face Hub (non-interactive)
-# --------------------------------------------------------------
-RUN python -c "from huggingface_hub import login; import os; token=os.getenv('HUGGINGFACEHUB_API_TOKEN'); \
-    print('⚠️ No HF token set during build!' if not token else '✅ Hugging Face token configured.')"
-
-# --------------------------------------------------------------
 # Expose port for FastAPI
-# --------------------------------------------------------------
 EXPOSE 7860
 
-# --------------------------------------------------------------
-# Run the FastAPI app with Uvicorn
-# --------------------------------------------------------------
+# Launch the app
 CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

@@ -1,13 +1,15 @@
 # ===============================================
-# Tech Disciples AI Backend — Updated for LangChain ≥1.0
+# Tech Disciples AI Backend — LangChain ≥1.0
 # ===============================================
 
 from fastapi import FastAPI, HTTPException, Header
 from pydantic import BaseModel
 import torch
 import logging
+import os
+from huggingface_hub import login
 
-# LangChain imports (modern >=1.0)
+# LangChain imports (modern ≥1.0)
 from langchain.llms.huggingface_pipeline import HuggingFacePipeline
 from langchain.chains import LLMChain
 from langchain.prompts.prompt import PromptTemplate
@@ -34,24 +36,36 @@ logger = logging.getLogger("TechDisciplesAI")
 # ===============================================
 app = FastAPI(title="Tech Disciples AI", version="3.0")
 
+# ===============================================
+# LOGIN TO HUGGINGFACE HUB
+# ===============================================
+hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+if hf_token:
+    try:
+        login(token=hf_token)
+        logger.info("✅ Logged into Hugging Face Hub successfully.")
+    except Exception as e:
+        logger.error(f"⚠️ Hugging Face login failed: {e}")
+else:
+    logger.warning("⚠️ No HUGGINGFACEHUB_API_TOKEN found in environment.")
+
 # ===============================================
 # LOAD MODEL USING PIPELINE + LANGCHAIN
 # ===============================================
 try:
     logger.info(f"🚀 Loading model: {MODEL_NAME}")
 
-
     hf_pipeline = pipeline(
         "text-generation",
         model=MODEL_NAME,
         device=DEVICE,
-        max_new_tokens=1024,  
-        temperature=0.7,      
-        do_sample=True,       
+        max_new_tokens=1024,
+        temperature=0.4,
+        do_sample=True,
+        top_p=0.9,
+        repetition_penalty=1.2,
     )
 
-
-
     llm = HuggingFacePipeline(pipeline=hf_pipeline)
     logger.info("✅ Model loaded successfully.")
 
@@ -80,7 +94,6 @@ User: {query}
 Tech Disciples AI (respond fully with examples, guidance, or Bible references):
 """
 
-
 prompt = PromptTemplate(
     template=prompt_template,
     input_variables=["conversation_history", "query"]
@@ -100,7 +113,7 @@ chain = LLMChain(
 # ===============================================
 class QueryInput(BaseModel):
     query: str
-    session_id: str | None = "default"  # optional: could be user/session-based
+    session_id: str | None = "default"
 
 # ===============================================
 # ROUTES
@@ -111,17 +124,16 @@ async def root():
 
 @app.post("/ai-chat")
 async def ai_chat(data: QueryInput, x_api_key: str = Header(None)):
-    # --- Authentication ---
     if x_api_key != API_SECRET:
         raise HTTPException(status_code=403, detail="Forbidden: Invalid API key")
 
     if not llm:
         raise HTTPException(status_code=500, detail="Model not initialized")
 
-    # --- Process Query ---
     try:
         response = chain.run(query=data.query.strip())
         return {"reply": response.strip()}
     except Exception as e:
         logger.error(f"⚠️ Model error: {e}")
         raise HTTPException(status_code=500, detail="Model failed to respond")
+