Upload Streamlit app file

app.py

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+import streamlit as st
+import torch
+from unsloth import FastLanguageModel
+from transformers import TextStreamer
+import json
+
+@st.cache_resource
+def load_model():
+    model, tokenizer = FastLanguageModel.from_pretrained(
+        model_name="ahmeterdempmk/Gemma2-2b-E-Commerce-Tuned",
+        max_seq_length=2048,
+        dtype=None,
+        load_in_4bit=True,
+    )
+    FastLanguageModel.for_inference(model)
+    return model, tokenizer
+
+model, tokenizer = load_model()
+
+st.title(" E-Commerce Text Generation")
+
+text = st.text_area("Enter product information:", placeholder="Example: Rosehip Marmalade, keep it cold")
+
+if st.button("Apply"):
+    if text:
+        with st.spinner("Generating response..."):
+            prompt = f"""
+            You are extracting product title and description from given text and rewriting the description and enhancing it when necessary.
+            Always give response in the user's input language.
+            Always answer in the given json format. Do not use any other keywords. Do not make up anything.
+            Explanations should contain at least five sentences each.
+
+            Json Format:
+            {{
+            "title": "<title of the product>",
+            "description": "<description of the product>"
+            }}
+
+            Examples:
+
+            Product Information: Rosehip Marmalade, keep it cold
+            Answer: {{"title": "Rosehip Marmalade", "description": "You should store this delicisious roseship marmelade in cold conditions. You can use it in your breakfasts and meals."}}
+
+            Product Information: Blackberry jam spoils in the heat
+            Answer: {{"title": "Blackberry Jam", "description": "Please store it in cold conditions. Recommended to be consumed at breakfast. Very sweet."}}
+
+            Now answer this:
+            Product Information: {text}
+            """
+            inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
+
+            output = model.generate(**inputs, max_new_tokens=128)
+            response = tokenizer.decode(output[0], skip_special_tokens=True)
+            answer_start = response.find("Now answer this:") + len("Now answer this:")
+            answer = response[answer_start:].strip()
+
+            json_start = answer.find("{")
+            json_end = answer.find("}") + 1 
+            json_response = answer[json_start:json_end].strip()
+
+            st.subheader("JSON Format Answer:")
+            st.text(f"{json_response}")
+
+            try:
+                json_data = json.loads(json_response)
+                title = json_data["title"]
+                description = json_data["description"]
+
+                st.subheader("Product Title:")
+                st.text(title)
+
+                st.subheader("Product Description:")
+                st.text(description)
+
+            except json.JSONDecodeError:
+                st.error("An error has occurred! Please try again.")
+    else:
+        st.warning("Please enter product information.")