Revert "feat: Replace OpenAI API with local TinyLlama inference"

This reverts commit 85673b36e3dba9c94ceb5fd2feef61d5d4f8dbf1.

Dockerfile

@@ -4,11 +4,6 @@ WORKDIR /code
 
 COPY . .
 
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    build-essential \
-    && rm -rf /var/lib/apt/lists/*
-
-
 RUN pip install --upgrade pip \
     && pip install --no-cache-dir \
     torch torchvision torchaudio \

api/generate_response.py

@@ -1,64 +1,63 @@
 import logging
-import os
-from llama_cpp import Llama
+from openai import OpenAI
+import tiktoken
 
 from utils.token import count_tokens
-from config import SYSTEM_PROMPT
+from config import API_URL, MODEL, GH_API_TOKEN, SYSTEM_PROMPT
 
 logger = logging.getLogger(__name__)
 
-llm = None
+try:
+    encoder = tiktoken.encoding_for_model(MODEL)
+except KeyError:
+    # fallback for custom or unrecognized model names
+    encoder = tiktoken.get_encoding("cl100k_base")
 
-def load_model_at_startup():
-    global llm
-    try:
-        logger.info("Loading Qwen2.5-1.5B model into RAM...")
-
-        llm = Llama.from_pretrained(
-            repo_id="Qwen/Qwen2.5-1.5B-Instruct-GGUF",
-            filename="qwen2.5-1.5b-instruct-q4_k_m.gguf",
-            n_threads=4,
-            n_gpu_layers=0,  # CPU only (safe for HF Spaces)
-            verbose=True,
-            n_ctx=4096,  # plenty for your RAG context
-        )
-        logger.info("Qwen2.5-1.5B model loaded into RAM successfully.")
+try:
+    client = OpenAI(base_url=API_URL, api_key=GH_API_TOKEN, timeout=60)
+    logging.info("OpenAI client initialized.")
+except Exception as e:
+    logging.critical("Failed to initialize OpenAI client as %s", e)
+    client = None
 
-    except Exception as e:
-        logger.error("Failed to load model: %s", e)
-        llm = None
 
 def generate_response(query: str, context: str) -> str:
 
-    if llm is None:
-        return "Error: Model not loaded.."
+    if client is None:
+        return "Error: AI client not configured."
 
     prompt = f"Context:\n{context}\n\nQuestion: {query}\nAnswer:"
     logging.info("Total number of tokens in prompt: %s", count_tokens(prompt))
 
     try:
-        full_prompt = (
-            f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n"
-            f"<|im_start|>user\n{prompt}<|im_end|>\n"
-            f"<|im_start|>assistant\n"
-        )
-        answer = llm(
-            full_prompt,
-            max_tokens=512,
-            temperature=0.25,
-            top_p=0.95,
-            stop=["<|im_end|>", "<|im_start|>"],
-            echo=False
+
+        response = client.chat.completions.create(
+            messages=[
+                {
+                    "role": "system",
+                    "content": SYSTEM_PROMPT,
+                },
+                {
+                    "role": "user",
+                    "content": prompt,
+                },
+            ],
+            temperature=1,
+            top_p=1,
+            model=MODEL,
+            stream=False,
         )
-        answer = answer["choices"][0]["text"].strip()
 
-        if not answer:
-            logger.warning("Failed to generate response. Returning empty response.")
-            return "I couldn't generate response. Please try again."
+        # Extract text defensively (depends on SDK return shape)
+        try:
+            response = response.choices[0].message.content
+        except Exception as e:
+            response = getattr(response, "text", None) or str(response)
+            logging.warning("Fallback used for response parsing as %s", e)
 
-        logging.info('Answer Generation Succeeded.')
-        return answer
+        logging.info("Answer generation succeeded.")
+        return response
 
     except Exception as e:
-        logger.error("Failed to generate response: %s", e)
-        return "Sorry, there was an error generating the response."
+        logging.error("Error during API call as %s", e)
+        return "Sorry, there was an error generating the response."

app.py

@@ -7,7 +7,7 @@ from fastapi.responses import JSONResponse
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.staticfiles import StaticFiles
 
-from api.generate_response import generate_response, load_model_at_startup
+from api.generate_response import generate_response
 from api.retrieve_context import retrieve_transcripts
 from utils.token import count_tokens, trim_to_token_limit
 from config import FILE_PATHS, TRANSCRIPTS, MAX_CONTEXT_TOKENS
@@ -31,8 +31,6 @@ def load_data():
     with open(TRANSCRIPTS, "rb") as f:
         transcripts = pickle.load(f)
 
-    load_model_at_startup()
-
     logger.info("Loaded %d transcripts", len(transcripts))
 
 

config.py

@@ -14,9 +14,11 @@ TRANSCRIPT_INDEX = "data/transcript_index.faiss"
 RETRIEVED_TRANSCRIPTS_FILE = Path("outputs/retrieved_transcripts.txt")
 RESPONSE_FILE = Path("outputs/generated_response.txt")
 COOKIES_FILE = Path("utils/youtube_cookies.txt")
-
-MAX_CONTEXT_TOKENS = 3000
-
+API_URL = "https://models.github.ai/inference"
+HF_API_TOKEN = os.environ.get("HF_API_TOKEN")
+GH_API_TOKEN = os.environ.get("GITHUB_TOKEN")
+MODEL = "openai/gpt-4.1"
+MAX_CONTEXT_TOKENS = 7000
 SYSTEM_PROMPT = """
 You are speaking as Acharya Prashant.
 

pyproject.toml

@@ -7,13 +7,13 @@ requires-python = ">=3.11"
 dependencies = [
     "faiss-cpu==1.9.0",
     "fastapi==0.116.1",
+    "openai==2.8.1",
     "pytz==2025.2",
     "sentence-transformers==3.0.1",
     "tiktoken>=0.12.0",
     "torch>=2.10.0",
     "transformers==4.57.1",
     "uvicorn==0.38.0",
-    "llama-cpp-python==0.3.4"
 ]
 
 [[tool.uv.index]]

requirements.txt

@@ -1,8 +1,9 @@
 faiss-cpu == 1.9.0
 fastapi == 0.116.1
+openai == 2.8.1
 pytz == 2025.2
+requests == 2.32.5
 sentence-transformers == 3.0.1
 tiktoken == 0.12.0
 transformers == 4.57.1
-uvicorn == 0.38.0
-llama-cpp-python==0.3.4
+uvicorn == 0.38.0

utils/token.py

@@ -1,6 +1,14 @@
 import tiktoken
+from config import MODEL
+
+
+# Limit the length of combined transcripts for ChatGPT ready
+try:
+    encoder = tiktoken.encoding_for_model(MODEL)
+except KeyError:
+    # fallback for custom or unrecognized model names
+    encoder = tiktoken.get_encoding("cl100k_base")
 
-encoder = tiktoken.get_encoding("cl100k_base")
 
 def count_tokens(text: str) -> int:
     """Return the number of tokens in a string, using your model's tokenizer."""

uv.lock

@@ -15,7 +15,7 @@ source = { virtual = "." }
 dependencies = [
     { name = "faiss-cpu" },
     { name = "fastapi" },
-    { name = "llama-cpp-python" },
+    { name = "openai" },
     { name = "pytz" },
     { name = "sentence-transformers" },
     { name = "tiktoken" },
@@ -29,7 +29,7 @@ dependencies = [
 requires-dist = [
     { name = "faiss-cpu", specifier = "==1.9.0" },
     { name = "fastapi", specifier = "==0.116.1" },
-    { name = "llama-cpp-python", specifier = "==0.3.4" },
+    { name = "openai", specifier = "==2.8.1" },
     { name = "pytz", specifier = "==2025.2" },
     { name = "sentence-transformers", specifier = "==3.0.1" },
     { name = "tiktoken", specifier = ">=0.12.0" },
@@ -164,12 +164,12 @@ wheels = [
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