create app.py

app.py

@@ -0,0 +1,212 @@
+import inspect
+import json
+import ast
+
+def function_to_json(func_str, func_description, param_descriptions, required_params):
+    # Create a new Module instance with the missing field
+    module_ast = ast.Module(body=[ast.Pass()], type_ignores=[])
+
+    # Parse the function string into the AST and replace the body
+    func_ast = ast.parse(func_str)
+    module_ast.body = func_ast.body
+
+    # Extract the function definition node
+    func_def = next(node for node in module_ast.body if isinstance(node, ast.FunctionDef))
+
+    # Get function signature
+    code_obj = compile(module_ast, '<string>', 'exec')
+    func_globals = {}
+    exec(code_obj, func_globals)
+    signature = inspect.signature(func_globals[func_def.name])
+    parameters = signature.parameters
+
+    # Convert param_descriptions string to a dictionary
+    param_desc_dict = json.loads(param_descriptions)
+
+    # Create JSON structure
+    function_json = {
+        "name": func_def.name,
+        "description": func_description,
+        "parameters": {
+            "type": "object",
+            "properties": {}
+        }
+    }
+
+    # Add parameter information to JSON structure
+    for param_name, param in parameters.items():
+        param_info = param_desc_dict.get(param_name, {})
+        param_type = param_info.get("type", str(param.annotation))
+        param_desc = param_info.get("description", param_name.replace('_', ' '))
+
+        function_json["parameters"]["properties"][param_name] = {
+            "type": param_type,
+            "description": param_desc
+        }
+
+        # Add required parameters based on user input
+        if param_name in required_params:
+            if "required" not in function_json["parameters"]:
+                function_json["parameters"]["required"] = []
+            function_json["parameters"]["required"].append(param_name)
+
+    return json.dumps(function_json, indent=4)
+
+
+
+""" Example uasge:
+# Example usage with user-provided function information
+sample_function_str = '''
+def generate_music(input_text, input_melody):
+    """
+    generate music based on an input text
+    """
+    client = Client("https://ysharma-musicgendupe.hf.space/", hf_token="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")
+    result = client.predict(
+        "melody",
+        input_text,
+        input_melody,
+        5,
+        250,
+        0,
+        1,
+        3,
+        fn_index=1
+    )
+    return result
+'''
+
+sample_func_description = "generate music based on an input text and input melody"
+
+sample_param_descriptions = '''
+{
+    "input_text": {
+        "type": "str",
+        "description": "Input text for music generation."
+    },
+    "input_melody": {
+        "type": "str",
+        "description": "File path of the input melody."
+    }
+}
+'''
+
+sample_required_params = ["input_text"]
+
+# Convert the sample function information to JSON
+json_str = function_to_json(sample_function_str, sample_func_description, sample_param_descriptions, sample_required_params)
+print(json_str)
+
+{
+    "name": "generate_music",
+    "description": "generate music based on an input text and input melody",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "input_text": {
+                "type": "str",
+                "description": "Input text for music generation."
+            },
+            "input_melody": {
+                "type": "str",
+                "description": "File path of the input melody."
+            }
+        },
+        "required": [
+            "input_text"
+        ]
+    }
+}
+
+"""
+
+
+title = "<h1 align='center'>Convert any function to function definitions required for GPT</h1>"
+demo = gr.Blocks()
+
+with demo:
+  gr.HTML(title)
+  with gr.Row():
+    input_function_str = gr.Code(label="Enter function definition", language='python', lines=10)
+    #input_function_str = gr.Textbox(lines=10, label='Enter function definition')
+    with gr.Column():
+      input_func_description = gr.Textbox(placeholder='', label='Enter your function description:')
+      input_param_description = gr.Textbox(
+                                placeholder="""Enter description as a dictionary with keys as param_name and values as param type and description as shown, eg. -
+                                {
+                                  "param1": {
+                                          "type": "str",
+                                          "description": "description of param1"
+                                      },
+                                  "param2": {
+                                          "type": "int/float/list/tuple/dict/set/bool etc..",
+                                          "description": "description of param2"
+                                      }
+                                  }""",
+                                label='Enter descriptions for parameters:')
+      input_required_params = gr.Textbox(placeholder="""Enter a list of required parameters, eg. - ['param1', 'param2', ...]""",
+                                         label='Enter required parameters for your function:')
+  generate_json = gr.Button('Get JSON definition')
+  gpt_function = gr.Code(label="GPT function definition", language='python', lines=7)
+
+  generate_json.click(function_to_json,
+                    [input_function_str, input_func_description, input_param_description, input_required_params],
+                    [gpt_function])
+
+  gr.Examples(
+      [ ["""
+        def generate_music(input_text, input_melody):
+            "generate music based on an input text"
+            client = Client("https://ysharma-musicgendupe.hf.space/", hf_token="hf_...")
+            result = client.predict(
+                "melody",
+                input_text,
+                input_melody,
+                5,
+                250,
+                0,
+                1,
+                3,
+                fn_index=1
+            )
+            return result
+      """,
+      """Generate music based on an input text.""",
+      """{
+            "input_text": {
+                "type": "string",
+                "description": "Input text for music generation."
+            },
+            "input_melody": {
+                "type": "string",
+                "description": "File path of the input melody."
+            }
+        }""",
+      """["input_text"]""" ],
+
+       ["""
+       def generate_image(prompt):
+            client = Client("https://jingyechen22-textdiffuser.hf.space/")
+            result = client.predict(
+                    prompt,
+                    20,
+                    7.5,
+                    1,
+                    "Stable Diffusion v2.1",
+                    fn_index=1)
+            return result[0]
+        """,
+        """generate image based on the input text prompt""",
+        """{
+              "prompt": {
+                  "type": "string",
+                  "description": "input text prompt for the image generation."
+              }
+          }""",
+        """["prompt"]""" ,
+         ],
+       ],
+        [input_function_str, input_func_description, input_param_description, input_required_params],
+    )
+  demo.launch() #(debug=True)
+