Upload app.py with huggingface_hub

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, TextIteratorStreamer, AutoModelForCausalLM
+import requests
+import json
+from peft import PeftModel
+from threading import Thread
+
+
+
+# --- Configuration ---
+
+BASE_MODEL_PATH = "algorythmtechnologies/zenith_coder_v1.1"
+
+ADAPTER_SUBFOLDER = "checkpoint-300"
+
+SERPER_API_KEY = "e43f937b155ec4feafb0458e4a7693b0d4889db4"
+
+
+
+# --- Model Loading ---
+
+# Load the tokenizer
+
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_PATH)
+
+
+
+# Load the model
+
+base_model = AutoModelForCausalLM.from_pretrained(
+
+    BASE_MODEL_PATH,
+
+    trust_remote_code=True,
+
+    low_cpu_mem_usage=True,
+
+    torch_dtype=torch.bfloat16,
+
+)
+
+
+
+# Move model to appropriate device
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+base_model.to(device)
+
+
+
+# Load the PEFT adapter from the subfolder in the Hub repository
+
+model = PeftModel.from_pretrained(base_model, BASE_MODEL_PATH, subfolder=ADAPTER_SUBFOLDER)
+
+model.eval()
+
+# --- Web Search Function ---
+def search(query):
+    """Performs a web search using the Serper API."""
+    url = "https://google.serper.dev/search"
+    payload = json.dumps({"q": query})
+    headers = {
+        'X-API-KEY': SERPER_API_KEY,
+        'Content-Type': 'application/json'
+    }
+    try:
+        response = requests.request("POST", url, headers=headers, data=payload)
+        response.raise_for_status()
+        results = response.json()
+        return results.get('organic', [])
+    except requests.exceptions.RequestException as e:
+        print(f"Error during web search: {e}")
+        return []
+
+# --- Response Generation ---
+def generate_response(message, history):
+    """Generates a response from the model, with optional web search."""
+    
+    full_prompt = ""
+    for user_msg, assistant_msg in history:
+        full_prompt += f"User: {user_msg}\nAssistant: {assistant_msg}\n"
+    full_prompt += f"User: {message}\nAssistant:"
+
+    search_results = None
+    if message.lower().startswith("search for "):
+        search_query = message[len("search for "):]
+        search_results = search(search_query)
+
+    if search_results:
+        context = " ".join([res.get('snippet', '') for res in search_results[:5]])
+        full_prompt = f"Based on the following search results: {context}\n\nUser: {message}\nAssistant:"
+
+    inputs = tokenizer(full_prompt, return_tensors="pt").to(device)
+    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=512)
+    
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+
+    generated_text = ""
+    for new_text in streamer:
+        generated_text += new_text
+        yield generated_text
+
+
+# --- Gradio UI ---
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="sky")) as demo:
+    gr.Markdown("# Zenith")
+    gr.ChatInterface(
+        generate_response,
+        chatbot=gr.Chatbot(
+            height=600,
+            avatar_images=(None, "https://i.imgur.com/9kAC4pG.png"),
+            bubble_full_width=False,
+        ),
+        textbox=gr.Textbox(
+            placeholder="Ask me anything or type 'search for <your query>'...",
+            container=False,
+            scale=7,
+        ),
+        theme="soft",
+        title=None,
+        submit_btn="Send",
+    )
+
+if __name__ == "__main__":
+    demo.launch()