Update app.py

app.py

@@ -1,57 +1,10 @@
-# import os
-# import gradio as gr
-# from llama_cpp import Llama
-# from huggingface_hub import snapshot_download
-
-# # Optionally increase the HTTP timeout via an environment variable
-# os.environ["HF_HUB_HTTP_TIMEOUT"] = "3600"  # 1 hour
-
-# # Download the model repository locally; this call blocks until the download completes.
-# local_repo = snapshot_download(
-#     repo_id="DebabrataHalder/mysqlmodel",
-#     revision="main",
-#     local_dir_use_symlinks=False,
-#     resume_download=True
-# )
-
-# # Load the model using the remote repo_id and specify the local cache directory
-# llm = Llama.from_pretrained(
-#     repo_id="DebabrataHalder/mysqlmodel",  # valid repo id format
-#     filename="mysqlmodel.gguf",
-#     cache_dir=local_repo,                   # point to the downloaded snapshot
-#     n_ctx=2048,                           # adjust context size if needed
-#     local_files_only=True                   # ensure only local files are used
-# )
-
-# def chat(prompt):
-#     output = llm(prompt)
-#     return output["choices"][0]["text"]
-
-# iface = gr.Interface(
-#     fn=chat,
-#     inputs="text",
-#     outputs="text",
-#     title="MySQL LLM Chat",
-#     description="Chat interface for the MySQL finetuned model"
-# )
-
-# iface.launch()
-
-
-
-
-
-
-
-
 import os
-from fastapi import FastAPI
-from pydantic import BaseModel
+import gradio as gr
 from llama_cpp import Llama
 from huggingface_hub import snapshot_download
 
-# Optionally increase the HTTP timeout via an environment variable (1 hour)
-os.environ["HF_HUB_HTTP_TIMEOUT"] = "3600"
+# Optionally increase the HTTP timeout via an environment variable
+os.environ["HF_HUB_HTTP_TIMEOUT"] = "3600"  # 1 hour
 
 # Download the model repository locally; this call blocks until the download completes.
 local_repo = snapshot_download(
@@ -65,34 +18,27 @@ local_repo = snapshot_download(
 llm = Llama.from_pretrained(
     repo_id="DebabrataHalder/mysqlmodel",  # valid repo id format
     filename="mysqlmodel.gguf",
-    cache_dir=local_repo,  # point to the downloaded snapshot
-    n_ctx=2048,          # adjust context size if needed
-    local_files_only=True  # ensure only local files are used
+    cache_dir=local_repo,                   # point to the downloaded snapshot
+    n_ctx=2048,                           # adjust context size if needed
+    local_files_only=True                   # ensure only local files are used
 )
 
-# Define a simple function to interact with the model
-def chat(prompt: str) -> str:
+def chat(prompt):
     output = llm(prompt)
     return output["choices"][0]["text"]
 
-# Set up FastAPI
-app = FastAPI(title="MySQL LLM Chat API")
+iface = gr.Interface(
+    fn=chat,
+    inputs="text",
+    outputs="text",
+    title="MySQL LLM Chat",
+    description="Chat interface for the MySQL finetuned model"
+)
+
+iface.launch()
+
 
-# Define request and response models using Pydantic
-class ChatRequest(BaseModel):
-    prompt: str
 
-class ChatResponse(BaseModel):
-    response: str
 
-@app.post("/chat", response_model=ChatResponse)
-async def chat_endpoint(chat_request: ChatRequest):
-    prompt = chat_request.prompt
-    response_text = chat(prompt)
-    return ChatResponse(response=response_text)
 
-# If running locally, use: uvicorn app:app --reload
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)