Initial Call to llava

.gitignore

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+.idea/

app.py

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+# import gradio as gr
+#
+# def greet(name):
+#     return "Hello " + name + "!!"
+#
+# demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+# demo.launch()
+
 import gradio as gr
+from transformers import AutoProcessor, AutoModelForCausalLM
+from PIL import Image
+import torch
+
+# Load LLaVA model
+model_id = "llava-hf/llava-1.5-7b-hf"
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id,
+                                             torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+                                             device_map="auto")
+
+
+def chat_with_llava(image, question, history=[]):
+    if image is None or not question.strip():
+        return history + [[question, "Please provide both an image and a prompt."]]
+
+    # Preprocess input
+    inputs = processor(text=question, images=image, return_tensors="pt").to(model.device)
+
+    # Generate output
+    output = model.generate(**inputs, max_new_tokens=512)
+    answer = processor.decode(output[0], skip_special_tokens=True)
+
+    # Append to chat history
+    history.append([question, answer])
+    return history
+
+
+# Create Gradio chatbot interface
+chat_interface = gr.ChatInterface(
+    fn=chat_with_llava,
+    inputs=[gr.Image(type="pil", label="Palm Image"),
+            gr.Textbox(label="Your Question", placeholder="What does my palm say about my future?")],
+    title="🖐️ AI Palm Reader",
+    description="Upload a palm image and ask a question. The LLaVA model will respond like a palmistry expert!",
+)
 
-def greet(name):
-    return "Hello " + name + "!!"
+chat_interface.launch()
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()

requirements.txt

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+transformers
+torch
+Pillow
+gradio