Update app.py

app.py

@@ -1,114 +1,73 @@
 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=-1)  # Use CPU
-
-# 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
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=-1)  # Use CPU for Hugging Face Spaces
+
+# AI-based SOC report generation
+def generate_soc():
+    prompt = """
+    Write a detailed Safety Observation and Conversation (SOC) report for a water injection plant. The report should include:
 
     1. Key Safety Conclusions/Comments/Agreements Made:
-    {random.choice(conclusions)}
+    A brief summary of the observations, highlighting the main safety concerns and any corrective actions taken.
 
     2. Observer's Name:
-    {random.choice(names)}
+    Provide a placeholder for the observer's name.
 
     3. KOC ID No.:
-    {random.randint(100000, 999999)}
+    Include a placeholder for the observer's ID.
 
     4. Type of Work Observed:
-    {random.choice(work_types)}
+    Specify the activity being observed (e.g., pump maintenance, valve inspection, or chemical handling).
 
     5. Location:
-    {random.choice(locations)}
+    Mention the location within the plant where the observation occurred.
 
-    6. Duration:
-    {random.randint(30, 120)} minutes
+    6. Duration (in mins):
+    Provide a placeholder for the time spent observing.
 
-    7. Plant Observations:
-    {random.choice(observations)}
+    --- Plant Observations:
+    Include detailed observations such as energy sources control, housekeeping, and spill containment. Each observation should be marked as Good Practice or Deviation with explanations for deviations.
 
-    8. People Observations:
-    - Personnel followed PPE standards.
-    - One deviation: Operator missed wearing gloves during chemical handling (Corrective action: Operator was instructed and gloves provided).
+    --- People Observations:
+    Include details of personnel behavior, PPE compliance, hazard understanding, and examples of Good Practices or Deviations with corrective actions.
 
-    9. Process Observations:
-    - Job safety analysis conducted properly (Good Practice).
-    - Improvement needed: Ensure pre-task meetings cover all hazards.
+    --- Process Observations:
+    Include observations on job safety analysis, procedures followed, control of work standards, and improvements needed.
 
-    10. Performance Observations:
-    - Pace of work was efficient and safety prioritized.
-    """
-    return report
+    --- Performance Observations:
+    Evaluate the overall safety performance, including work pace, supervision, and prioritization of safety.
 
-# 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"]
+    Format the output neatly with headers for each section and placeholders where needed.
+    """
+    result = generator(prompt, max_length=1024, num_return_sequences=1)[0]["generated_text"]
+    return result
 
 # 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
+def app_interface():
+    return generate_soc()
 
 # Gradio Layout
 with gr.Blocks() as app:
-    gr.Markdown("# Safety Observation and Conversation (SOC) Generator")
+    gr.Markdown("# AI-Generated Safety Observation and Conversation (SOC) Reports")
     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.
+        This app generates detailed SOC reports for a water injection plant. AI assists in creating observations, including conclusions, corrective actions, and key performance evaluations.
         """
     )
-
-    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
+        lines=30,
     )
-    
-    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)
+    generate_btn = gr.Button("Generate SOC Report")
+    copy_btn = gr.Button("Copy to Clipboard")
+
+    generate_btn.click(app_interface, outputs=output_box)
     copy_btn.click(lambda text: text, inputs=output_box, outputs=None)
 
 # Launch the app