Create app.py

app.py

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+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+import random
+import gradio as gr
+
+# Load the Hugging Face model and tokenizer
+model_name = "EleutherAI/gpt-neo-1.3B"  # Replace with your preferred model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)
+
+# Predefined random options
+names = ["John Doe", "Jane Smith", "Ali Khan"]
+work_types = ["Pump Maintenance", "Chemical Handling", "Valve Inspection"]
+locations = ["North Sector Valve House", "Central Processing Unit"]
+conclusions = [
+    "Spill identified and cleaned; training on PPE compliance conducted.",
+    "No major issues observed; team adherence to safety protocols noted.",
+    "Minor deviation in housekeeping; corrective action implemented."
+]
+observations = [
+    "- Energy sources controlled (Good Practice)",
+    "- Leaks contained (Deviation: Minor leakage in Valve 12; action taken to tighten seals).",
+    "- Housekeeping standards maintained (Good Practice)."
+]
+
+# Random SOC generator
+def generate_random_soc():
+    report = f"""
+    SAFETY OBSERVATION AND CONVERSATION (SOC) REPORT
+
+    1. Key Safety Conclusions/Comments/Agreements Made:
+    {random.choice(conclusions)}
+
+    2. Observer's Name:
+    {random.choice(names)}
+
+    3. KOC ID No.:
+    {random.randint(100000, 999999)}
+
+    4. Type of Work Observed:
+    {random.choice(work_types)}
+
+    5. Location:
+    {random.choice(locations)}
+
+    6. Duration:
+    {random.randint(30, 120)} minutes
+
+    7. Plant Observations:
+    {random.choice(observations)}
+
+    8. People Observations:
+    - Personnel followed PPE standards.
+    - One deviation: Operator missed wearing gloves during chemical handling (Corrective action: Operator was instructed and gloves provided).
+
+    9. Process Observations:
+    - Job safety analysis conducted properly (Good Practice).
+    - Improvement needed: Ensure pre-task meetings cover all hazards.
+
+    10. Performance Observations:
+    - Pace of work was efficient and safety prioritized.
+    """
+    return report
+
+# AI refinement function
+def refine_report(report, instructions):
+    prompt = f"Refine the following SOC report based on the instructions provided:\n\nSOC Report:\n{report}\n\nInstructions: {instructions}\n\nRefined Report:"
+    outputs = generator(prompt, max_length=512, num_return_sequences=1)
+    return outputs[0]["generated_text"]
+
+# Gradio Interface
+def app_interface(instructions=None):
+    # Generate random SOC report
+    random_report = generate_random_soc()
+
+    if instructions:
+        # Refine the report using the Hugging Face model
+        refined_report = refine_report(random_report, instructions)
+        return refined_report
+    else:
+        return random_report
+
+# Gradio Layout
+with gr.Blocks() as app:
+    gr.Markdown("# Safety Observation and Conversation (SOC) Generator")
+    gr.Markdown(
+        """
+        This app generates random SOC reports for a water injection plant. You can also provide specific instructions to refine the report using AI assistance.
+        """
+    )
+
+    with gr.Row():
+        generate_btn = gr.Button("Create Random SOC")
+        instructions_box = gr.Textbox(
+            label="Refinement Instructions (Optional)",
+            placeholder="E.g., Make the report more concise or focus on PPE compliance...",
+        )
+    
+    output_box = gr.Textbox(
+        label="Generated SOC Report",
+        placeholder="Your SOC report will appear here...",
+        lines=15
+    )
+    
+    with gr.Row():
+        refine_btn = gr.Button("Refine Report with AI")
+        copy_btn = gr.Button("Copy to Clipboard")
+
+    # Button Actions
+    generate_btn.click(app_interface, inputs=None, outputs=output_box)
+    refine_btn.click(app_interface, inputs=instructions_box, outputs=output_box)
+    copy_btn.click(lambda text: text, inputs=output_box, outputs=None)
+
+# Launch the app
+app.launch()