Update app.py

app.py

@@ -1,21 +1,15 @@
 import streamlit as st
-from transformers import PegasusForConditionalGeneration, PegasusTokenizer
-import torch
+import os
+from groq import Groq
 
-# Load Hugging Face Model and Tokenizer
-@st.cache_resource
-def load_model():
-    model_name = "tuner007/pegasus_paraphrase"
-    model = PegasusForConditionalGeneration.from_pretrained(model_name)
-    tokenizer = PegasusTokenizer.from_pretrained(model_name)
-    return model, tokenizer
-
-# Initialize model and tokenizer
-model, tokenizer = load_model()
+# Initialize Groq client
+GROQ_API_KEY = "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"  # Replace with your Groq API key
+os.environ["GROQ_API_KEY"] = GROQ_API_KEY
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
 
 # Streamlit App
-st.title("Humanizer & Rephraser App")
-st.subheader("Powered by Pegasus and Streamlit")
+st.title("Humanizer & Rephraser App (Groq-powered)")
+st.subheader("Powered by Groq and Streamlit")
 
 # User Input
 input_text = st.text_area("Enter the text you want to process:", "")
@@ -47,34 +41,26 @@ if st.button("Generate Output"):
                 else:
                     task_prompt = f"Rephrase the following text: {input_text}"
 
-                # Tokenize and process input
-                inputs = tokenizer(task_prompt, return_tensors="pt", max_length=512, truncation=True)
-                st.write("Debug: Tokenized Input Shape:", inputs.input_ids.shape)  # Debugging tokenized input shape
-
-                # Generate output
-                output_ids = model.generate(
-                    inputs.input_ids,
-                    max_length=512,
-                    num_beams=5,
-                    num_return_sequences=1,
-                    early_stopping=True,
+                # Call Groq API
+                chat_completion = client.chat.completions.create(
+                    messages=[{"role": "user", "content": task_prompt}],
+                    model="llama-3.3-70b-versatile",  # Replace with the desired model
+                    stream=False,
                 )
 
-                if output_ids is None or len(output_ids) == 0:
-                    raise ValueError("The model generated no output.")
+                # Extract the generated content
+                output_text = chat_completion.choices[0].message.content.strip()
 
-                # Decode and validate the output
-                output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
-                if not output_text.strip():
-                    raise ValueError("Decoded output is empty.")
+                if not output_text:
+                    raise ValueError("Groq API returned an empty output.")
 
                 output_word_count = len(output_text.split())
+
+                # Display Output
                 st.success("Done!")
                 st.text_area("Generated Output:", value=output_text, height=200)
                 st.write(f"**Output Word Count:** {output_word_count}")
 
-        except torch.cuda.OutOfMemoryError:
-            st.error("CUDA out of memory. Try reducing input size or using CPU.")
         except Exception as e:
             st.error(f"An error occurred: {str(e)}")