init

app.py

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+import json
+import torch
+from transformers import pipeline
+import streamlit as st
+
+# Load the text-generation pipeline
+pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-alpha", torch_dtype=torch.bfloat16, device_map="auto")
+
+delimiter = "####"
+system_message = f"""
+You will be provided with user data. \
+The user data will be delimited with \
+{delimiter} characters.
+
+Extract key information as shown in the examples shown below, to add an item in the ERPnext application. Give the output as a python dictionary object. Do not use information outside from what
+is given inside the text to fill the values. Do not provide any explaination.
+
+Example1:  
+prompt: "I had made an order for 6 Units of item Logitech G15. The platform said that there were 10 Units available in stock before i made my purchase, but now it shows that the item is out of stock even though I have made a payment of 6000 towards my order.",
+"item_code": "None",
+"item_name": "Logitech G15",
+"item_group": "None",
+"stock_uom": "Unit",
+"description": "None",
+"standard_rate": 1000
+
+Example2:
+prompt": "Hello, I have not received my order of 5Litres Saffola Gold oil. I made an order on 07-09-2023",
+"item_code": "None",
+"item_name": "Saffola Gold Oil",
+"item_group": "None",
+"stock_uom": "Litres",
+"description": "None",
+"standard_rate": "None"
+
+Example3: 
+prompt": "Please add an entry of a new item in our inventory, Code IB707, and name i-ball Keyboard K-5. Current stock includes 5000 Nos of the item, grouped under Products. Price per unit is Rs. 1500.",
+"item_code": "IB707",
+"item_name": "i-ball Keyboard K-5",
+"item_group": "Product",
+"stock_uom": "Nos",
+"description": "None",
+"standard_rate": 1500
+
+"""
+
+# Create a Streamlit app
+def main():
+    st.title("Prompt to JSON Tool")
+
+    st.write("This tool generates JSON output based on the user's prompt.")
+
+    user_input = st.text_area("Enter your prompt here")
+
+    if st.button("Generate JSON"):
+        messages = [
+            {"role": "system", "content": system_message},
+            {"role": "user", "content": f"{delimiter}{user_input}{delimiter}"},
+        ]
+
+        prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+        output_text = outputs[0]["generated_text"]
+
+        # Parse the relevant JSON information from the output text
+        json_output = output_text.split("Example1:")[-1].strip()
+
+        # Display the JSON output on the Streamlit app
+        st.write("JSON Output:")
+        st.write(json_output)
+
+        # Save the JSON output to a file
+        with open("output.json", "w") as f:
+            json.dump(json_output, f, indent=4)
+            st.write("JSON output saved to 'output.json'.")
+
+if __name__ == "__main__":
+    main()