Update app.py

app.py

@@ -1,44 +1,56 @@
-# Initialize Replit LLM Pipeline
-try:
-    nlp_pipeline = pipeline("text2text-generation", model=model_name)
-    st.success("Model loaded successfully!")
-except Exception as e:
-    st.error(f"Error loading model: {e}. Please verify the model name or your internet connection.")
-    st.stop()
-
-# Input Section
-st.subheader("Input Your Query")
-user_input = st.text_area("Enter your query or task description", height=150)
-
-# Submit Button
-if st.button("Generate Output"):
-    if user_input.strip() == "":
-        st.warning("Please enter a valid input.")
+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the model and tokenizer
+@st.cache_resource
+def load_model():
+    model_name = "replit/replit-code-v1-3b"  # Replace with your fine-tuned model if applicable
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+    return model, tokenizer
+
+model, tokenizer = load_model()
+
+# App title and description
+st.title("Replit-code-v1-3b Code Assistant 📊")
+st.markdown("""
+This application allows you to interact with the **Replit-code-v1-3b** large language model.  
+You can use it to generate, debug, or optimize code snippets.  
+Simply provide a prompt, and the model will respond with suggestions!
+""")
+
+# User input
+st.header("Enter Your Prompt")
+prompt = st.text_area("Describe your coding task or paste your code for debugging:")
+
+# Temperature and max length controls
+st.sidebar.header("Model Settings")
+temperature = st.sidebar.slider("Temperature (Creativity)", 0.0, 1.0, 0.7)
+max_length = st.sidebar.slider("Max Response Length", 50, 500, 200)
+
+# Submit button
+if st.button("Generate Response"):
+    if prompt.strip():
+        with st.spinner("Generating response..."):
+            # Tokenize and generate response
+            inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
+            outputs = model.generate(
+                inputs.input_ids, 
+                max_length=max_length, 
+                temperature=temperature, 
+                pad_token_id=tokenizer.eos_token_id
+            )
+            response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        # Display response
+        st.subheader("Generated Code/Response")
+        st.code(response, language="python")
     else:
-        with st.spinner("Processing..."):
-            try:
-                # Generate response using Replit LLM
-                output = nlp_pipeline(user_input)
-                response = output[0]["generated_text"]
-                st.subheader("AI Response")
-                st.write(response)
-            except Exception as e:
-                st.error(f"An error occurred: {e}")
-
-# Handling Code Optimization:
-if task_type == "Code Optimization":
-    st.info("Paste your Python code for optimization recommendations.")
-    user_code = st.text_area("Paste your Python code", height=200)
-    if st.button("Optimize Code"):
-        if user_code.strip() == "":
-            st.warning("Please paste valid Python code to optimize.")
-        else:
-            with st.spinner("Analyzing and optimizing..."):
-                try:
-                    optimization_prompt = f"Optimize the following Python code:\n\n{user_code}"
-                    output = nlp_pipeline(optimization_prompt)
-                    optimized_code = output[0]["generated_text"]
-                    st.subheader("Optimized Code")
-                    st.code(optimized_code)
-                except Exception as e:
-                    st.error(f"An error occurred while optimizing code: {e}")
+        st.warning("Please enter a prompt to generate a response.")
+
+# Footer
+st.markdown("---")
+st.markdown("""
+**Replit-code-v1-3b Code Assistant**  
+Built with [Streamlit](https://streamlit.io/) and the [Hugging Face Transformers Library](https://huggingface.co/docs/transformers).
+""")