Update app.py

app.py

@@ -1,40 +1,35 @@
-import requests
-import json
 import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-url="https://huggingface.co/curiouscurrent/omnicode"
+# Load your text generation model from Hugging Face using its identifier
+model_identifier = "curiouscurrent/omnicode"
+model = AutoModelForCausalLM.from_pretrained(model_identifier)
+tokenizer = AutoTokenizer.from_pretrained(model_identifier)
 
-headers={
-
-    'Content-Type':'application/json'
-}
-
-history=[]
+history = []
 
 def generate_response(prompt):
     history.append(prompt)
-    final_prompt="\n".join(history)
-
-    data={
-        "model":"curiouscurrent/omnicode",
-        "prompt":final_prompt,
-        "stream":False
-    }
-
-    response=requests.post(url,headers=headers,data=json.dumps(data))
-
-    if response.status_code==200:
-        response=response.text
-        data=json.loads(response)
-        actual_response=data['response']
-        return actual_response
-    else:
-        print("error:",response.text)
-
-
-interface=gr.Interface(
-    fn=generate_response,
-    inputs=gr.Textbox(lines=4,placeholder="Enter your Prompt"),
-    outputs="text"
-)
-interface.launch()
+    final_prompt = "\n".join(history)
+    
+    # Tokenize input prompt
+    input_ids = tokenizer.encode(final_prompt, return_tensors="pt", max_length=512, truncation=True)
+    
+    # Generate response
+    output_ids = model.generate(input_ids, max_length=100, num_return_sequences=1)
+    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    
+    return response
+
+# Create Gradio interface
+input_prompt = gr.inputs.Textbox(lines=4, label="Input Prompt")
+output_text = gr.outputs.Textbox(label="Response")
+
+gr.Interface(
+    generate_response,
+    inputs=input_prompt,
+    outputs=output_text,
+    title="OmniCode",
+    description="Multi programming coding assistant",
+    theme="compact"
+).launch()