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app.py

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+import os
+import gradio as gr
+
+
+def response(USER_DATA, TOKEN) -> str:
+    return "ok"
+
+
+with gr.Blocks() as llm:
+    slider = gr.Slider(10, 100, render=False)
+    chatbot = gr.ChatInterface(
+        fn=response,
+        multimodal=True,
+        title="MultiModel LLM for Testcase generation",
+        cache_examples=True,
+    )
+
+llm.launch(debug=True)

llm.py

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+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from PIL import Image
+import gradio as gr
+import time
+
+model = AutoModelForCausalLM.from_pretrained(
+    "MILVLG/imp-v1-3b",
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+tokenizer = AutoTokenizer.from_pretrained("MILVLG/imp-v1-3b", trust_remote_code=True)
+
+
+def response(USER_DATA, TOKEN) -> str:
+    print(USER_DATA)
+    MESSAGE = USER_DATA["text"]
+    NUM_FILES = len(USER_DATA["files"])
+    FILES = USER_DATA["files"]
+
+    SYSTEM_PROMPT = f"""
+    A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed testcase related to image uploaded. You are tasked with generating detailed, step-by-step test cases for software functionality based on uploaded images. The user will provide one or more images of a software or website interface. For each image, generate a separate set of test cases following the format below:
+
+    '''Description: Provide a brief explanation of the functionality being tested, as inferred from the image.
+    Pre-conditions: Identify any setup requirements, dependencies, or conditions that must be met before testing can begin (e.g., user logged in, specific data pre-populated, etc.).
+    Testing Steps: Outline a clear, numbered sequence of actions that a user would take to test the functionality in the image.
+    Expected Result: Specify the expected outcome if the functionality is working as intended.'''
+
+    Ensure that:
+    Testcases should be related to validation of data, component interactions, navigation, etc.
+    Each testcase should have it's own Description, Pre-conidtions, Testing Steps, Expected Result.
+
+    USER: <image>\n{MESSAGE}
+    ASSISTANT:
+    """
+
+    RES = generate_answer(FILES, SYSTEM_PROMPT)
+
+    response = f"{RES}."
+    return response
+    for i in range(len(response)):
+        time.sleep(0.025)
+        yield response[: i + 1]
+
+
+def generate_answer(IMAGES: list, SYSTEM_PROMPT) -> str:
+    print(len(IMAGES))
+
+    INPUT_IDS = tokenizer(SYSTEM_PROMPT, return_tensors="pt").input_ids
+
+    RESULT = ""
+    for EACH_IMG in IMAGES:
+        image_path = EACH_IMG["path"]
+        image = Image.open(image_path)
+        image_tensor = model.image_preprocess(image)
+
+        output_ids = model.generate(
+            inputs=INPUT_IDS,
+            max_new_tokens=500,
+            images=image_tensor,
+            use_cache=False,
+        )[0]
+        CUR_RESULT = tokenizer.decode(
+            output_ids[INPUT_IDS.shape[1] :], skip_special_tokens=True
+        ).strip()
+
+        RESULT = f"{RESULT} /n/n {CUR_RESULT}"
+
+    return RESULT