Create app.py

app.py

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+import gradio as gr
+from langchain.prompts import PromptTemplate
+from langchain_huggingface import HuggingFaceEndpoint
+from langchain_core.output_parsers import JsonOutputParser
+import time
+
+# Initialize the LLM and other components
+llm = HuggingFaceEndpoint(
+    repo_id="mistralai/Mistral-7B-Instruct-v0.3",
+    task="text-generation",
+    max_new_tokens=128,
+    temperature=0.7,
+    do_sample=False,
+)
+
+# Provide the family labels directly in the prompt
+family_labels = [
+    "Batteries and generators and kinetic power transmission",
+    "Building and facility maintenance and repair services",
+    "Business administration services",
+    "Communications Devices and Accessories",
+    "Components for information technology or broadcasting or telecommunications",
+    "Computer Equipment and Accessories",
+    "Consumer electronics",
+    "Data Voice or Multimedia Network Equipment or Platforms and Accessories",
+    "Domestic appliances",
+    "Electrical equipment and components and supplies",
+    "Electrical wire and cable and harness",
+    "Electronic hardware and component parts and accessories",
+    "Electronic manufacturing machinery and equipment and accessories",
+    "General agreements and contracts",
+    "Heating and ventilation and air circulation",
+    "Heavy construction machinery and equipment",
+    "Industrial process machinery and equipment and supplies",
+    "Management advisory services",
+    "Marketing and distribution",
+    "Office and desk accessories",
+    "Office machines and their supplies and accessories",
+    "Office supply",
+    "Power generation",
+    "Power sources",
+    "Printing and publishing equipment",
+    "Software",
+    "Structural components and basic shapes"
+]
+
+# Modify the prompt to focus on selecting a UNSPSC family label from the given list
+template_classify = '''
+You are a classifier bot that assigns a UNSPSC family label to the given text.
+Your task is to classify the text into one of the following UNSPSC family labels:
+{family_labels}
+Provide only the family label in your answer. If unsure, label as "Unknown".
+Convert it to JSON format using 'Answer' as the key and return it.
+Your final response MUST contain only the response, no other text.
+Example:
+{{"Answer":["Family Label"]}}
+
+What is the UNSPSC family label for the following text?:
+<text>  
+{TEXT}  
+</text>
+'''
+
+json_output_parser = JsonOutputParser()
+
+# Define the classify_text function
+def classify_text(text):
+    global llm
+
+    start = time.time()
+
+    # Join the family labels into a string for the prompt
+    family_labels_str = "\n".join(family_labels)
+
+    prompt_classify = PromptTemplate(
+        template=template_classify,
+        input_variables=["TEXT", "family_labels"]
+    )
+    formatted_prompt = prompt_classify.format(TEXT=text, family_labels=family_labels_str)
+    classify = llm.invoke(formatted_prompt)
+
+    parsed_output = json_output_parser.parse(classify)
+    end = time.time()
+    duration = end - start
+    return parsed_output["Answer"][0], duration
+
+# Create the Gradio interface
+def create_gradio_interface():
+    with gr.Blocks() as iface:
+        text_input = gr.Textbox(label="Text")
+        output_text = gr.Textbox(label="Detected UNSPSC Family")
+        time_taken = gr.Textbox(label="Time Taken (seconds)")
+        submit_btn = gr.Button("Classify UNSPSC Family")
+
+        def on_submit(text):
+            classification, duration = classify_text(text)
+            return classification, f"Time taken: {duration:.2f} seconds"
+
+        submit_btn.click(fn=on_submit, inputs=text_input, outputs=[output_text, time_taken])
+
+    iface.launch()
+
+if __name__ == "__main__":
+    create_gradio_interface()