Update app.py

app.py

@@ -3,30 +3,19 @@ import streamlit as st
 from PIL import Image
 import torch
 from torchvision import transforms, models
-import numpy as np
-from groq import Groq
 
-# Set up environment variables
+# Set up environment variable for Groq API
 os.environ["GROQ_API_KEY"] = "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"
 
-# Initialize Groq client
-client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
-
-# Load Pretrained Models
+# Load Pretrained Model for Organ Recognition
 @st.cache_resource
-def load_model():
-    # Pretrained EfficientNet for organ recognition
-    organ_model = models.efficientnet_b0(pretrained=True)
-    organ_model.eval()
-    
-    # Pretrained DenseNet (CheXNet) for normal/abnormal classification
-    chexnet_model = models.densenet121(pretrained=True)
-    chexnet_model.classifier = torch.nn.Linear(1024, 2)  # Normal, Abnormal
-    chexnet_model.eval()
-
-    return organ_model, chexnet_model
+def load_organ_model():
+    model = models.resnet18(pretrained=True)  # ResNet18 pretrained model
+    model.fc = torch.nn.Linear(model.fc.in_features, 4)  # Modify for 4 classes
+    model.eval()
+    return model
 
-organ_model, chexnet_model = load_model()
+organ_model = load_organ_model()
 
 # Image Preprocessing
 def preprocess_image(image):
@@ -37,35 +26,17 @@ def preprocess_image(image):
     ])
     return transform(image).unsqueeze(0)
 
-# Groq API for AI Insights
-def get_ai_insights(text_prompt):
-    try:
-        response = client.chat.completions.create(
-            messages=[{"role": "user", "content": text_prompt}],
-            model="llama-3.3-70b-versatile"
-        )
-        return response.choices[0].message.content
-    except Exception as e:
-        return f"Error: {e}"
-
-# Predict Organ
+# Organ Recognition Prediction
 def predict_organ(image):
     with torch.no_grad():
-        output = organ_model(preprocess_image(image))
-        classes = ["Lungs", "Heart", "Spine", "Other"]  # Example classes
-        prediction = classes[output.argmax().item()]
-    return prediction
-
-# Predict Normal/Abnormal
-def predict_normal_abnormal(image):
-    with torch.no_grad():
-        output = chexnet_model(preprocess_image(image))
-        classes = ["Normal", "Abnormal"]
+        input_tensor = preprocess_image(image)
+        output = organ_model(input_tensor)
+        classes = ["Lungs", "Heart", "Spine", "Other"]  # Example organ classes
         prediction = classes[output.argmax().item()]
     return prediction
 
 # Streamlit App
-st.title("Medical X-ray Analysis App")
+st.title("X-ray Organ Recognition App")
 st.sidebar.title("Navigation")
 task = st.sidebar.radio("Select a task", ["Upload X-ray", "AI Insights"])
 
@@ -77,24 +48,12 @@ if task == "Upload X-ray":
         st.image(image, caption="Uploaded X-ray", use_column_width=True)
 
         # Predict Organ
-        st.subheader("Step 1: Identify the Organ")
+        st.subheader("Step 1: Identify the Organ in the X-ray")
         organ = predict_organ(image)
         st.write(f"Predicted Organ: **{organ}**")
 
-        # Predict Normal/Abnormal
-        st.subheader("Step 2: Analyze the X-ray")
-        classification = predict_normal_abnormal(image)
-        st.write(f"X-ray Status: **{classification}**")
-
-        if classification == "Abnormal":
-            st.subheader("Step 3: AI-Based Insights")
-            ai_prompt = f"Explain why this X-ray of the {organ} is abnormal."
-            insights = get_ai_insights(ai_prompt)
-            st.write(insights)
-
 elif task == "AI Insights":
     st.subheader("Ask AI")
     user_input = st.text_area("Enter your query for AI insights")
     if user_input:
-        response = get_ai_insights(user_input)
-        st.write(response)
+        st.write("AI insights will be generated here.")  # Placeholder for AI logic