Update app.py

app.py

@@ -1,13 +1,13 @@
 import gradio as gr
-from transformers import pipeline
-from PIL import Image
 import cv2
 import numpy as np
 import requests
 import g4f
 import time
 import os
-import base64
+from transformers import pipeline
+from PIL import Image
+import google.generativeai as genai
 
 theme = gr.themes.Base(
     primary_hue="cyan",
@@ -16,35 +16,26 @@ theme = gr.themes.Base(
 )
 
 API_KEY = os.getenv("API_KEY")
+genai.configure(api_key = os.environ['GOOGLE_API_KEY'])
+txt_model = genai.GenerativeModel('gemini-pro')
 
-# BRAIN_TUMOR_API_URL = "https://api-inference.huggingface.co/models/Devarshi/Brain_Tumor_Classification"
+BRAIN_TUMOR_API_URL = "https://api-inference.huggingface.co/models/Devarshi/Brain_Tumor_Classification"
 BREAST_CANCER_API_URL = "https://api-inference.huggingface.co/models/MUmairAB/Breast_Cancer_Detector"
 # ALZHEIMER_API_URL = "https://api-inference.huggingface.co/models/dewifaj/alzheimer_mri_classification"
 headers = {"Authorization": "Bearer "+API_KEY+"", 'Content-Type': 'application/json'}
 alzheimer_classifier = pipeline("image-classification", model="dewifaj/alzheimer_mri_classification")
 # breast_cancer_classifier = pipeline("image-classification", model="MUmairAB/Breast_Cancer_Detector")
-brain_tumor_classifier = pipeline("image-classification", model="Devarshi/Brain_Tumor_Classification")
+# brain_tumor_classifier = pipeline("image-classification", model="Devarshi/Brain_Tumor_Classification")
+
 
 # Create a function to Detect/Classify Alzheimer
 def classify_alzheimer(image):
-    # image_data = np.array(image, dtype=np.uint8)
-    # _, buffer = cv2.imencode('.jpg', image_data)
-    # binary_data = buffer.tobytes()
-
-    # response = requests.post(ALZHEIMER_API_URL, headers=headers, data=binary_data)
-    # result = {}
-    # print(response.json())
-    # for ele in response.json():
-    #     label, score = ele.values()
-    #     result[label] = score
-
-    # return result
-    image_pil = Image.open(image)
     result = alzheimer_classifier(image)
-    prediction = result[0]
-    score = prediction['score']
-    label = prediction['label']
-    return {"score": score, "label": label}
+    prediction = {}
+    for ele in result:
+        score, label = ele.values()
+        prediction[label] = score
+    return prediction
 
 
 # Create a function to Detect/Classify Breast_Cancer
@@ -54,26 +45,27 @@ def classify_breast_cancer(image):
     binary_data = buffer.tobytes()
 
     response = requests.post(BREAST_CANCER_API_URL, headers=headers, data=binary_data)
-    result = {}
-    print(response.json())
+    prediction = {}
     for ele in response.json():
         label, score = ele.values()
-        result[label] = score
-
-    return result
+        prediction[label] = score
+    
+    return prediction
 
 
 # Create a function to Detect/Classify Brain_Tumor
 def classify_brain_tumor(image):
-    with open(image, 'rb') as img:
-        encoded_string = base64.b64encode(img.read())
-    image_base = encoded_string.decode('utf-8')
+    image_data = np.array(image, dtype=np.uint8)
+    _, buffer = cv2.imencode('.jpg', image_data)
+    binary_data = buffer.tobytes()
+
+    response = requests.post(BRAIN_TUMOR_API_URL, headers=headers, data=binary_data)
+    prediction = {}
+    for ele in response.json():
+        label, score = ele.values()
+        prediction[label] = score
     
-    result = brain_tumor_classifier(image_base)
-    prediction = result[0]
-    score = prediction['score']
-    label = prediction['label']
-    return {"score": score, "label": label}
+    return prediction
 
 
 # Create the Gradio interface
@@ -94,14 +86,14 @@ with gr.Blocks(theme=theme) as Alzheimer:
         button.click(classify_alzheimer, [image], [output])
 
         def respond(message, history):
-            bot_message = g4f.ChatCompletion.create(
-                model="gpt-3.5-turbo",
-                provider=g4f.Provider.Vercel,
-                messages=[{"role": "user",
-                           "content": "Your role is Alzheimer Disease Expert. Now I will provide you with the user query. First check if the user query is related to Alzheimer or not. If it is not related to Alzheimer then simply avoid the query by saying this is not my expertise, whereas if related to Alzheimer reply it as usual. Here's the user Query:" + message}],
-            )
-            time.sleep(1)
-            return str(bot_message)
+            # bot_message = g4f.ChatCompletion.create(
+            #     model="gemini",
+            #     provider=g4f.Provider.GeminiProChat,
+            #     messages=[{"role": "user",
+            #                "content": "Your role is Alzheimer Disease Expert. Now I will provide you with the user query. First check if the user query is related to Alzheimer or not. If it is not related to Alzheimer then simply avoid the query by saying this is not my expertise, whereas if related to Alzheimer reply it as usual. Here's the user Query:" + message}],
+            # )
+            bot_message = txt_model.generate_content("Your role is Alzheimer Disease Expert. Now I will provide you with the user query. First check if the user query is related to Alzheimer or not. If it is not related to Alzheimer then simply avoid the query by saying this is not my expertise, whereas if related to Alzheimer reply it as usual. Here's the user Query:" + message)
+            return str(bot_message.text)
 
 
         with gr.Column():
@@ -115,7 +107,7 @@ with gr.Blocks(theme=theme) as BreastCancer:
         with gr.Column():
             gr.Markdown("# Breast Cancer Detection and Classification")
             gr.Markdown("> Classify the breast cancer.")
-            image = gr.Image(type="pil")
+            image = gr.Image()
             output = gr.Label(label='Breast Cancer Classification', container=True, scale=2)
             with gr.Row():
                 button = gr.Button(value="Submit", variant="primary")
@@ -129,14 +121,14 @@ with gr.Blocks(theme=theme) as BreastCancer:
         button.click(classify_breast_cancer, [image], [output])
 
         def respond(message, history):
-            bot_message = g4f.ChatCompletion.create(
-                model="gpt-3.5-turbo",
-                provider=g4f.Provider.Vercel,
-                messages=[{"role": "user",
-                           "content": "Your role is Breast_Cancer Disease Expert. Now I will provide you with the user query. First check if the user query is related to Breast_Cancer or not. If it is not related to Breast_Cancer then simply avoid the query by saying this is not my expertise, whereas if related to Breast_Cancer reply it as usual. Here's the user Query:" + message}],
-            )
-            time.sleep(1)
-            yield str(bot_message)
+            # bot_message = g4f.ChatCompletion.create(
+            #     model="gpt-4-32k-0613",
+            #     provider=g4f.Provider.GeekGpt,
+            #     messages=[{"role": "user",
+            #                "content": "Your role is Breast_Cancer Disease Expert. Now I will provide you with the user query. First check if the user query is related to Breast_Cancer or not. If it is not related to Breast_Cancer then simply avoid the query by saying this is not my expertise, whereas if related to Breast_Cancer reply it as usual. Here's the user Query:" + message}],
+            # )
+            bot_message = txt_model.generate_content("Your role is Breast_Cancer Disease Expert. Now I will provide you with the user query. First check if the user query is related to Breast_Cancer or not. If it is not related to Breast_Cancer then simply avoid the query by saying this is not my expertise, whereas if related to Breast_Cancer reply it as usual. Here's the user Query:" + message)
+            yield str(bot_message.text)
 
         with gr.Column():
             gr.Markdown("# Health Bot for Breast Cancer")
@@ -149,7 +141,7 @@ with gr.Blocks(theme=theme) as BrainTumor:
         with gr.Column():
             gr.Markdown("# Brain Tumor Detection and Classification")
             gr.Markdown("> Classify the Brain Tumor.")
-            image = gr.Image(type="pil")
+            image = gr.Image()
             output = gr.Label(label='Brain_Tumor Classification', container=True, scale=2)
             with gr.Row():
                 button = gr.Button(value="Submit", variant="primary")
@@ -163,14 +155,14 @@ with gr.Blocks(theme=theme) as BrainTumor:
         button.click(classify_brain_tumor, [image], [output])
 
         def respond(message, history):
-            bot_message = g4f.ChatCompletion.create(
-                model="gpt-3.5-turbo",
-                provider=g4f.Provider.Vercel,
-                messages=[{"role": "user",
-                           "content": "Your role is Brain Tumor Disease Expert. Now I will provide you with the user query. First check if the user query is related to Brain Tumor or not. If it is not related to Brain Tumor then simply avoid the query by saying this is not my expertise, whereas if related to Brain Tumor reply it as usual. Here's the user Query:" + message}],
-            )
-            time.sleep(1)
-            return str(bot_message)
+            # bot_message = g4f.ChatCompletion.create(
+            #     model="gpt-4-32k-0613",
+            #     provider=g4f.Provider.GeekGpt,
+            #     messages=[{"role": "user",
+            #                "content": "Your role is Brain Tumor Disease Expert. Now I will provide you with the user query. First check if the user query is related to Brain Tumor or not. If it is not related to Brain Tumor then simply avoid the query by saying this is not my expertise, whereas if related to Brain Tumor reply it as usual. Here's the user Query:" + message}],
+            # )
+            bot_message = txt_model.generate_content("Your role is Brain Tumor Disease Expert. Now I will provide you with the user query. First check if the user query is related to Brain Tumor or not. If it is not related to Brain Tumor then simply avoid the query by saying this is not my expertise, whereas if related to Brain Tumor reply it as usual. Here's the user Query:" + message)
+            return str(bot_message.text)
 
         with gr.Column():
             gr.Markdown("# Health Bot for Brain Tumor")