Ajoute LLM et generation d'image

.gitignore

@@ -0,0 +1,6 @@
+bin
+flagged
+lib
+lib64
+pyvenv.cfg
+share

app.py

@@ -1,104 +1,73 @@
 import gradio as gr
-import cv2
 import requests
 import os
+import io
+from PIL import Image
+from dotenv import load_dotenv
 
-from ultralytics import YOLO
+load_dotenv()
+
+FLUX1_APIKEY = os.getenv('FLUX1_APIKEY')
+GEMINI_APIKEY = os.getenv('GEMINI_APIKEY')
+
+print(FLUX1_APIKEY)
+print(GEMINI_APIKEY)
+
+def Main(prompt, inputText):
+    return GenerateImageFromText(prompt), GenerateTextLLM(inputText)
+
+def GenerateImageFromText(prompt):
+    API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev"
+    headers = {"Authorization": f"Bearer {FLUX1_APIKEY}"}
+
+    def query(payload):
+        response = requests.post(API_URL, headers=headers, json=payload)
+        return response.content
+
+    image_bytes = query({
+        "inputs": prompt,
+    })
+    image = Image.open(io.BytesIO(image_bytes))
+
+    return image
+
+def GenerateTextLLM(inputText):
+    
+    url = f'https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash-latest:generateContent?key={GEMINI_APIKEY}'
+    headers = {
+        'Content-Type': 'application/json'
+    }
+    data = {
+        "contents": [
+            {
+                "parts": [
+                    {
+                        "text": inputText
+                    }
+                ]
+            }
+        ]
+    }
+
+    response = requests.post(url, headers=headers, json=data)
+
+    try:
+        return response.json()['candidates'][0]['content']['parts'][0]['text']
+    except:
+        return 'Error'
 
-file_urls = [
-    'https://www.dropbox.com/s/b5g97xo901zb3ds/pothole_example.jpg?dl=1',
-    'https://www.dropbox.com/s/86uxlxxlm1iaexa/pothole_screenshot.png?dl=1',
-    'https://www.dropbox.com/s/7sjfwncffg8xej2/video_7.mp4?dl=1'
-]
 
-def download_file(url, save_name):
-    url = url
-    if not os.path.exists(save_name):
-        file = requests.get(url)
-        open(save_name, 'wb').write(file.content)
-
-for i, url in enumerate(file_urls):
-    if 'mp4' in file_urls[i]:
-        download_file(
-            file_urls[i],
-            f"video.mp4"
-        )
-    else:
-        download_file(
-            file_urls[i],
-            f"image_{i}.jpg"
-        )
-
-model = YOLO('best.pt')
-path  = [['image_0.jpg'], ['image_1.jpg']]
-video_path = [['video.mp4']]
-
-def show_preds_image(image_path):
-    image = cv2.imread(image_path)
-    outputs = model.predict(source=image_path)
-    results = outputs[0].cpu().numpy()
-    for i, det in enumerate(results.boxes.xyxy):
-        cv2.rectangle(
-            image,
-            (int(det[0]), int(det[1])),
-            (int(det[2]), int(det[3])),
-            color=(0, 0, 255),
-            thickness=2,
-            lineType=cv2.LINE_AA
-        )
-    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-inputs_image = [
-    gr.components.Image(type="filepath", label="Input Image"),
-]
 outputs_image = [
-    gr.components.Image(type="numpy", label="Output Image"),
+    gr.components.Image(type="pil", label="Output Image"),
+    "text"
 ]
+
 interface_image = gr.Interface(
-    fn=show_preds_image,
-    inputs=inputs_image,
+    fn=Main,
+    inputs=["text", "text"],
     outputs=outputs_image,
-    title="Pothole detector",
-    examples=path,
-    cache_examples=False,
+    title="Feur",
 )
 
-def show_preds_video(video_path):
-    cap = cv2.VideoCapture(video_path)
-    while(cap.isOpened()):
-        ret, frame = cap.read()
-        if ret:
-            frame_copy = frame.copy()
-            outputs = model.predict(source=frame)
-            results = outputs[0].cpu().numpy()
-            for i, det in enumerate(results.boxes.xyxy):
-                cv2.rectangle(
-                    frame_copy,
-                    (int(det[0]), int(det[1])),
-                    (int(det[2]), int(det[3])),
-                    color=(0, 0, 255),
-                    thickness=2,
-                    lineType=cv2.LINE_AA
-                )
-            yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
-
-inputs_video = [
-    gr.components.Video(label="Input Video"),
-
-]
-outputs_video = [
-    gr.components.Image(label="Output Image"),
-]
-interface_video = gr.Interface(
-    fn=show_preds_video,
-    inputs=inputs_video,
-    outputs=outputs_video,
-    title="Pothole detector",
-    examples=video_path,
-    cache_examples=False,
-)
+interface_image.launch()
 
-gr.TabbedInterface(
-    [interface_image, interface_video],
-    tab_names=['Image inference', 'Video inference']
-).queue().launch()

requirements.txt

@@ -44,4 +44,6 @@ thop>=0.1.1  # FLOPs computation
 # roboflow
 
 # HUB -----------------------------------------
-GitPython>=3.1.24
+GitPython>=3.1.24
+
+python-dotenv