Update app.py

app.py

@@ -1,46 +1,25 @@
-import os
 import streamlit as st
-#from groq import Groq
-#from fpdf import FPDF  # For PDF generation
 from transformers import pipeline
+from fpdf import FPDF  # For PDF generation
 
-api = "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"
+# Initialize the pipeline for text generation using Llama model
+llama_model = "meta-llama/Llama-2-7b-hf"  # Update with the specific Llama model you want to use
 
-# --- Groq API Setup ---
-client = Groq(api_key=api)
-# Function to get code from StarCoder model
+# Function to get code from Hugging Face Llama model
 def generate_code(summary, language):
     try:
-        chat_completion = client.chat.completions.create(
-            messages=[
-                {
-                    "role": "user",
-                    "content": f"Generate efficient {language} code for the following task without any explanations or comments: {summary}",
-                }
-            ],
-            model="llama3-8b-8192",  # Specify the model
-            stream=False,
-        )
-        generated_code = chat_completion.choices[0].message.content
+        generator = pipeline('text-generation', model=llama_model, tokenizer=llama_model)
+        generated_code = generator(f"Generate {language} code: {summary}", max_length=150)[0]['generated_text']
         return generated_code
     except Exception as e:
         st.error(f"Error generating code: {e}")
         return ""
 
-# Function to explain the generated code using Llama
+# Function to explain the generated code using Llama (or a suitable summarization model)
 def explain_code(code):
     try:
-        chat_completion = client.chat.completions.create(
-            messages=[
-                {
-                    "role": "user",
-                    "content": f"Explain the following code descriptively and attractively so the user can easily understand it:\n\n{code}",
-                }
-            ],
-            model="llama3-8b-8192",
-            stream=False,
-        )
-        explanation = chat_completion.choices[0].message.content
+        explainer = pipeline('summarization', model=llama_model, tokenizer=llama_model)  # You can use a summarization model or a specific Llama-based explanation model
+        explanation = explainer(f"Explain the following code:\n\n{code}", max_length=250, min_length=50)[0]['summary_text']
         return explanation
     except Exception as e:
         st.error(f"Error explaining code: {e}")
@@ -59,7 +38,7 @@ def save_code_as_pdf(code, file_name="generated_code.pdf"):
 st.set_page_config(page_title="Generative AI Code Generator", page_icon="🧑‍💻", layout="wide")
 
 # Page Title
-st.title("🚀 Generative AI Code Generator Using StarCoder")
+st.title("🚀 Generative AI Code Generator Using Hugging Face Llama")
 
 # Input Fields
 summary = st.text_area("📝 Enter the Task Summary", "For example: Create a function to add two numbers.")
@@ -100,4 +79,4 @@ if st.button("Generate New Code"):
 
 # Footer Information
 st.markdown("---")
-st.write("🌟 Powered by **Streamlit**, **Groq**, and **StarCoder** | Deployed on Hugging Face")
+st.write("🌟 Powered by **Streamlit**, **Hugging Face**, and **Transformers** | Deployed on Hugging Face")