edit

app.py

@@ -22,57 +22,89 @@ def display_models():
     models = load_models()
     if not models:
         return "No models have been added yet."
-    output = "### List of AI Models:\n\n"
-    for idx, model in enumerate(models):
-        output += f"**{idx + 1}. {model['name']}**\n"
-        output += f"- **Description**: {model['description']}\n"
-        output += f"- **Year**: {model['year']}\n"
-        output += "\n"
+    models = sorted(models, key=lambda x: x['dateOfRelease'])  # Sort by release date
+    output = ""  # Prepare HTML output for timeline
+    for model in models:
+        output += "<div style='border: 1px solid #ccc; border-radius: 10px; padding: 10px; margin: 10px;'>"
+        output += f"<h3>{model['name']} ({model['dateOfRelease']})</h3>"
+        output += f"<p><strong>Description:</strong> {model['description']}</p>"
+        output += f"<p><strong>Developer:</strong> {model.get('developer', 'Unknown')}</p>"
+        output += f"<p><strong>Use Case:</strong> {model.get('use_case', 'General')}</p>"
+        output += f"<p><strong>Impact:</strong> {model.get('impact', 'Not specified')}</p>"
+        output += "</div>"
     return output
 
 # Function to add a new model
-def add_model(name, description, year):
+def add_model(name, description, dateOfRelease, developer, use_case, impact):
     models = load_models()
-    models.append({"name": name, "description": description, "year": year})
+    models.append({
+        "name": name,
+        "description": description,
+        "dateOfRelease": dateOfRelease,
+        "developer": developer,
+        "use_case": use_case,
+        "impact": impact
+    })
     save_models(models)
     return "Model added successfully!", display_models()
 
 # Function to edit an existing model
-def edit_model(index, name, description, year):
+def edit_model(index, name, description, dateOfRelease, developer, use_case, impact):
     models = load_models()
     if index < 1 or index > len(models):
         return "Invalid index. Please provide a valid model number.", display_models()
-    models[index - 1] = {"name": name, "description": description, "year": year}
+    models[index - 1] = {
+        "name": name,
+        "description": description,
+        "dateOfRelease": dateOfRelease,
+        "developer": developer,
+        "use_case": use_case,
+        "impact": impact
+    }
     save_models(models)
     return "Model updated successfully!", display_models()
 
 # Gradio interface
 with gr.Blocks() as app:
-    gr.Markdown("# AI Model Repository\n\nAdd, view, and edit AI models contributed by the community.")
+    gr.Markdown("# AI Timeline\n\nVisualize the development of AI models through an interactive timeline.")
 
-    with gr.Tab("View Models"):
-        view_button = gr.Button("Refresh List")
-        view_output = gr.Markdown(display_models())
+    with gr.Tab("View Timeline"):
+        view_button = gr.Button("Refresh Timeline")
+        view_output = gr.HTML(display_models())
         view_button.click(display_models, outputs=view_output)
 
     with gr.Tab("Add Model"):
         with gr.Row():
             name_input = gr.Textbox(label="Model Name", placeholder="Enter model name")
-            year_input = gr.Textbox(label="Publication Year", placeholder="Enter year of publication")
+            year_input = gr.Textbox(label="Publication Date", placeholder="Enter date of publication (YYYY-MM-DD)")
         description_input = gr.Textbox(label="Description", placeholder="Enter a short description")
+        developer_input = gr.Textbox(label="Developer", placeholder="Enter the developer or organization")
+        use_case_input = gr.Textbox(label="Use Case", placeholder="Enter the primary use case")
+        impact_input = gr.Textbox(label="Impact", placeholder="Enter the model's impact")
         add_button = gr.Button("Add Model")
         add_output = gr.Markdown()
-        add_button.click(add_model, inputs=[name_input, description_input, year_input], outputs=[add_output, view_output])
+        add_button.click(
+            add_model,
+            inputs=[name_input, description_input, year_input, developer_input, use_case_input, impact_input],
+            outputs=[add_output, view_output]
+        )
 
     with gr.Tab("Edit Model"):
         edit_index = gr.Number(label="Model Number", precision=0)
         with gr.Row():
             edit_name = gr.Textbox(label="New Model Name", placeholder="Enter new model name")
-            edit_year = gr.Textbox(label="New Publication Year", placeholder="Enter new year of publication")
+            edit_year = gr.Textbox(label="New Publication Date", placeholder="Enter new date of publication (YYYY-MM-DD)")
         edit_description = gr.Textbox(label="New Description", placeholder="Enter new description")
+        edit_developer = gr.Textbox(label="New Developer", placeholder="Enter new developer or organization")
+        edit_use_case = gr.Textbox(label="New Use Case", placeholder="Enter new primary use case")
+        edit_impact = gr.Textbox(label="New Impact", placeholder="Enter new impact")
         edit_button = gr.Button("Edit Model")
         edit_output = gr.Markdown()
-        edit_button.click(edit_model, inputs=[edit_index, edit_name, edit_description, edit_year], outputs=[edit_output, view_output])
+        edit_button.click(
+            edit_model,
+            inputs=[edit_index, edit_name, edit_description, edit_year, edit_developer, edit_use_case, edit_impact],
+            outputs=[edit_output, view_output]
+        )
 
 # Run the app
-app.launch()
+app.launch()

models.json

@@ -2,21 +2,21 @@
     {
         "name": "GPT-3",
         "description": "A language model capable of generating human-like text.",
-        "year": "2020"
+        "dateOfRelease": "2020"
     },
     {
         "name": "BERT",
         "description": "A model for natural language understanding tasks like question answering and sentiment analysis.",
-        "year": "2018"
+        "dateOfRelease": "2018"
     },
     {
         "name": "DALL·E",
         "description": "A model for generating images from textual descriptions.",
-        "year": "2021"
+        "dateOfRelease": "2021"
     },
     {
         "name": "ResNet",
         "description": "A deep convolutional network for image recognition tasks.",
-        "year": "2015"
+        "dateOfRelease": "2015"
     }
 ]