Update app.py

app.py

@@ -1,12 +1,98 @@
-# import pip
-# def upgrade_packages():
-#     try:
-#         pip.main(['install', '--upgrade', 'transformers','peft'])  # Replace with other packages to upgrade
-#         print("Packages upgraded successfully!")
-#     except Exception as e:
-#         print(f"Error upgrading packages: {e}")
-# Call the upgrade function
-#upgrade_packages()
-#checks
 import gradio as gr
-gr.load("models/Ikeofai/outputs").launch()
+import os
+from huggingface_hub import login
+token = os.environ.get("token")
+login(token)
+##########
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel, PeftConfig
+
+from transformers import AutoTokenizer, pipeline
+
+# Assuming you have it stored securely
+
+model_name="models/Ikeofai/outputs"
+max_length=200
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+config = PeftConfig.from_pretrained(model_name,token=token)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
+model = PeftModel.from_pretrained(model, model_name)
+
+#testing
+
+#tokenizer = AutoTokenizer.from_pretrained("Orcawise/eu-ai-act-align", use_fast=True,max_length=200)
+#pipe = pipeline("text2text-generation", model=model,tokenizer=tokenizer)
+#pipe = pipeline("conversational", model="google/vit-base-patch16-224")
+
+#gr.Interface.from_pipeline(pipe).launch()
+
+def generate_text(prompt):
+    """Generates text using the PEFT model.
+    Args:
+        prompt (str): The user-provided prompt to start the generation.
+           Returns:
+        str: The generated text.
+    """
+
+
+    # Preprocess the prompt
+    input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"]
+
+    # Generate text using beam search
+    output = model.generate(
+        input_ids=input_ids,
+        max_length=max_length,
+        num_beams=1,  # Adjust num_beams for better quality (may increase processing time)
+    )
+
+    # Decode the generated tokens
+    generated_text = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
+
+
+    return generated_text
+#############
+
+
+### working correctly but  the welcoming message isnt rendering
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(placeholder="Ask your question...")  # Add placeholder text
+    submit_button = gr.Button("Submit")
+    clear = gr.Button("Clear")
+
+
+    def user(user_message, history):
+        return "", history + [[user_message, None]]
+
+
+    def bot(history):
+      history[-1][1] = ""  # Update the last bot message (welcome message or response)
+      if len(history) < 0:  # Check if it's the first interaction
+          bot_message = "Hi there! How can I help you today?"
+          history.append([None, bot_message])  # Add welcome message to history
+          for character in bot_message:
+            history[-1][1] += character
+            yield history  # Yield the updated history character by character
+
+      else:
+          previous_message = history[-1][0]  # Access the previous user message
+          bot_message = generate_text(previous_message)  # Generate response based on previous message
+          for character in bot_message:
+            history[-1][1] += character
+            yield history  # Yield the updated history character by character
+
+
+
+    # Connect submit button to user and then bot functions
+    submit_button.click(user, [msg,  chatbot], [msg, chatbot], queue=False).then(
+        bot, chatbot, chatbot
+    )
+
+    # Trigger user function on Enter key press (same chain as submit button)
+    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot, chatbot, chatbot
+    )
+
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+demo.launch()