Spaces:
Sleeping
Sleeping
File size: 4,979 Bytes
8748236 1d25dd5 8748236 1d25dd5 8748236 ba20081 8748236 ba20081 8748236 ba20081 8748236 7536935 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 |
import gradio as gr
import os
from PIL import Image
from transformers import pipeline
import google.generativeai as genai
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
api_key = os.getenv("GEMINI_API_KEY")
# Configure Gemini AI
if not api_key:
print("Warning: GEMINI_API_KEY not found in environment variables.")
else:
print(f"GEMINI_API_KEY found: {api_key[:4]}...{api_key[-4:]}")
try:
genai.configure(api_key=api_key)
except Exception as e:
print(f"Error configuring Gemini API: {e}")
generation_config = {
"temperature": 0.9,
"top_p": 0.95,
"top_k": 64,
"max_output_tokens": 8192,
}
model_genai = genai.GenerativeModel(
model_name="gemini-1.5-flash",
generation_config=generation_config
)
# Lazy-load ML model
pipe = None
def get_model():
global pipe
if pipe is None:
from transformers import pipeline
pipe = pipeline("image-classification", "dima806/medicinal_plants_image_detection")
return pipe
def predict_plant(image):
"""Identify medicinal plant from image"""
if image is None:
return "Please upload an image first!"
try:
model = get_model()
outputs = model(image)
plant_name = outputs[0]['label']
confidence = outputs[0]['score']
result = f"🌿 **Plant Identified**: {plant_name}\n\n"
result += f"📊 **Confidence**: {confidence:.2%}\n\n"
result += f"Click 'Get Plant Info' to learn more about {plant_name}!"
return result
except Exception as e:
return f"❌ Error: {str(e)}"
def get_plant_info(plant_name):
"""Get detailed information about a medicinal plant"""
if not plant_name:
return "Please identify a plant first!"
try:
chat = model_genai.start_chat(history=[])
prompt = f"Tell me everything about the medicinal plant '{plant_name}'. Include scientific name, medicinal properties, traditional uses, preparation methods, health benefits, and precautions. Format with emojis and clear sections."
response = chat.send_message(prompt)
return response.text
except Exception as e:
return f"❌ Error: {str(e)}"
def chat_with_ai(message, history):
"""Chat with Gemini AI about Ayurveda and medicinal plants"""
try:
# Initialize history if None
if history is None:
history = []
chat = model_genai.start_chat(history=[])
chat.send_message("You are AyurVedik AI, an expert in medicinal plants and Ayurveda. Answer questions helpfully with emojis.")
response = chat.send_message(message)
# Append new message and response to history in 'messages' format
history.append({"role": "user", "content": message})
history.append({"role": "assistant", "content": response.text})
return history, "" # Return updated history and empty string to clear input
except Exception as e:
if history is None:
history = []
history.append({"role": "user", "content": message})
history.append({"role": "assistant", "content": f"❌ Error: {str(e)}"})
return history, ""
# Create Gradio Interface
with gr.Blocks(title="AyurVedik AI", theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🌿 AyurVedik AI - Medicinal Plant Identifier")
gr.Markdown("### Identify medicinal plants and learn about Ayurveda")
with gr.Tab("🔍 Identify Plant"):
with gr.Row():
with gr.Column():
image_input = gr.Image(type="pil", label="Upload Plant Image")
identify_btn = gr.Button("🔍 Identify Plant", variant="primary")
with gr.Column():
prediction_output = gr.Markdown(label="Identification Result")
# plant_name_state removed
with gr.Row():
plant_name_input = gr.Textbox(label="Plant Name (from identification above)", placeholder="Enter plant name or use identification result")
get_info_btn = gr.Button("📚 Get Plant Info", variant="secondary")
info_output = gr.Markdown(label="Plant Information")
identify_btn.click(
fn=predict_plant,
inputs=image_input,
outputs=prediction_output
)
get_info_btn.click(
fn=get_plant_info,
inputs=plant_name_input,
outputs=info_output
)
with gr.Tab("💬 Chat with AI"):
gr.Markdown("### Ask me anything about medicinal plants and Ayurveda!")
chatbot = gr.Chatbot(height=400, type="messages")
msg = gr.Textbox(label="Your Question", placeholder="Ask about medicinal plants, Ayurveda, health benefits...")
msg.submit(chat_with_ai, [msg, chatbot], [chatbot, msg])
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860)
|