Sarvam_m_indian_chat

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prakhardoneria commited on May 22

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2223fe4

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1 Parent(s): 1c98a01

Update app.py

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app.py +50 -36

app.py CHANGED Viewed

@@ -1,39 +1,53 @@
 import gradio as gr
-from llama_cpp import Llama
-from huggingface_hub import hf_hub_download
-# Lazy global model
-llm = None
-def load_model():
-    global llm
-    if llm is None:
-        # Download GGUF model from HF Hub
-        model_path = hf_hub_download(
-            repo_id="TheBloke/deepseek-coder-1.3b-instruct-GGUF",
-            filename="deepseek-coder-1.3b-instruct.Q4_K_M.gguf"
-        )
-        # Load LLaMA model
-        llm = Llama(
-            model_path=model_path,
-            n_ctx=1024,
-            n_threads=4,
-            n_gpu_layers=0,  # Set 0 for CPU-only
-            use_mlock=False
-        )
-def generate_response(prompt):
-    load_model()
-    full_prompt = f"### Instruction:\n{prompt}\n\n### Response:\n"
-    output = llm(full_prompt, max_tokens=512, stop=["###"])
-    return output["choices"][0]["text"]
-demo = gr.Interface(
-    fn=generate_response,
-    inputs=gr.Textbox(lines=5, label="Enter your instruction"),
-    outputs=gr.Textbox(lines=10, label="Model Response"),
-    title="Viber AI",
-    description="Ask the model to generate or modify code, HTML, or general text via instructions."
-)
 demo.launch()

 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+# Load model and tokenizer
+model_name = "Salesforce/codet5-small"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+# Code generation function
+def generate_code(prompt, history):
+    history = history or []
+    input_text = "\n".join(history + [prompt])
+    inputs = tokenizer.encode(input_text, return_tensors="pt", truncation=True, max_length=512)
+    outputs = model.generate(inputs, max_length=512)
+    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    # Extract HTML, CSS, JS
+    html = css = js = ""
+    if "<html>" in decoded:
+        html = "<html>" + decoded.split("<html>")[1].split("</html>")[0] + "</html>"
+    if "<style>" in decoded:
+        css = decoded.split("<style>")[1].split("</style>")[0]
+    if "<script>" in decoded:
+        js = decoded.split("<script>")[1].split("</script>")[0]
+    history += [prompt, decoded]
+    return html.strip(), css.strip(), js.strip(), history
+# Function to clear chat
+def clear_chat():
+    return "", "", "", []
+# Build Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# Code Generator: HTML, CSS, JS")
+    with gr.Row():
+        prompt = gr.Textbox(label="Prompt", placeholder="Describe what you want to build...")
+        generate_btn = gr.Button("Generate")
+        new_chat_btn = gr.Button("New Chat")
+    with gr.Row():
+        html_output = gr.Code(label="HTML")
+        css_output = gr.Code(label="CSS")
+        js_output = gr.Code(label="JavaScript")
+    chat_state = gr.State([])
+    generate_btn.click(fn=generate_code, inputs=[prompt, chat_state],
+                       outputs=[html_output, css_output, js_output, chat_state])
+    new_chat_btn.click(fn=clear_chat,
+                       outputs=[html_output, css_output, js_output, chat_state])
 demo.launch()