Create app.py

app.py

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+import spacy
+from transformers import pipeline
+from collections import defaultdict
+import pandas as pd
+import gradio as gr
+import os
+
+# Load spaCy model for prompt parsing
+nlp = spacy.load("en_core_web_sm")
+
+# Load text generation model
+text_generator = pipeline('text-generation', model='gpt2')
+
+# Load dataset
+dataset_path = "public_service_data.csv"  # Ensure this file is uploaded to your Hugging Face Space
+if os.path.exists(dataset_path):
+    data_df = pd.read_csv(dataset_path)
+else:
+    data_df = pd.DataFrame({"growth_rate": [0]})  # Default structure if the file isn't present
+
+def generate_project_document(prompt, document_type):
+    # Parse prompt using spaCy
+    doc = nlp(prompt)
+    
+    # Identify key elements and required sections
+    industry = next((ent.text for ent in doc.ents if ent.label_ == 'ORG'), "the relevant industry")
+    product = next((ent.text for ent in doc.ents if ent.label_ == 'PRODUCT'), "the specified product")
+    
+    if document_type == 'Project Proposal':
+        sections = ['Introduction', 'Objectives', 'Methodology', 'Expected Outcomes']
+    elif document_type == 'Report':
+        sections = ['Executive Summary', 'Findings', 'Analysis', 'Conclusion']
+    elif document_type == 'Summary':
+        sections = ['Overview', 'Key Points', 'Conclusion']
+    else:
+        return None, "Invalid document type"
+    
+    # Generate content for each section
+    content = defaultdict(str)
+    for section in sections:
+        prompt_text = f"Generate {section.lower()} for a {industry} project on {product}."
+        content[section] = text_generator(prompt_text, max_length=200, num_return_sequences=1)[0]['generated_text']
+    
+    # Enrich content with data
+    if document_type == 'Project Proposal':
+        growth_rate = data_df['growth_rate'].mean() if not data_df.empty else 0
+        content['Introduction'] += f"\nThe {industry} industry has witnessed significant advancements in {product} technology. According to the latest industry data, the market for {product} is expected to grow by {growth_rate:.2f}% annually."
+    
+    # Compile document as string to return
+    document_content = "\n\n".join([f"{section}\n{text}" for section, text in content.items()])
+    return document_content
+
+# Gradio interface
+def app():
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                prompt = gr.Textbox(label="Document Prompt")
+                document_type = gr.Dropdown(label="Document Type", choices=["Project Proposal", "Report", "Summary"])
+                submit = gr.Button("Generate Document")
+            with gr.Column():
+                output = gr.Textbox(label="Generated Document Content", lines=15)
+        
+        submit.click(generate_project_document, inputs=[prompt, document_type], outputs=output)
+    
+    demo.launch()
+
+if __name__ == "__main__":
+    app()