Create app.py

app.py

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+import os
+import streamlit as st
+import google.generativeai as genai
+from dotenv import load_dotenv
+
+# Load environment variables if running locally
+load_dotenv()
+
+# Streamlit web app layout
+st.title("Gemini Model Suggestions Generator")
+api_key = st.sidebar.text_input("Enter your GEMINI API Key", type="password")
+
+if api_key:
+    # Configure Gemini with the user-provided API key
+    genai.configure(api_key=api_key)
+
+    # Gemini model configuration
+    MODEL_NAME = 'gemini-1.5-pro'
+    SAFETY_SETTINGS = {
+        'HATE': 'BLOCK_NONE',
+        'HARASSMENT': 'BLOCK_NONE',
+        'SEXUAL': 'BLOCK_NONE',
+        'DANGEROUS': 'BLOCK_NONE'
+    }
+
+    # Function to generate suggestions
+    def generate_suggestions(free_text: str, patient_info: str, instructions: list) -> list:
+        responses = []
+        configs = [
+            {'instruction': instructions[0], 'temperature': 0.3, 'max_tokens': 1000},
+            {'instruction': instructions[1], 'temperature': 0.7, 'max_tokens': 1000},
+            {'instruction': instructions[2], 'temperature': 1.0, 'max_tokens': 1000}
+        ]
+
+        # Create model instance
+        model = genai.GenerativeModel(MODEL_NAME)
+
+        for config in configs:
+            try:
+                # Construct the full prompt
+                full_prompt = f"""
+                [Free Text]: {free_text}
+                [Patient Information]: {patient_info}
+                [Instruction]: {config['instruction']}
+                Please provide your response below:
+                """
+
+                # Generate content
+                response = model.generate_content(
+                    contents=full_prompt,
+                    generation_config=genai.types.GenerationConfig(
+                        temperature=config['temperature'],
+                        max_output_tokens=config['max_tokens']
+                    ),
+                    safety_settings=SAFETY_SETTINGS
+                )
+
+                responses.append({
+                    'success': True,
+                    'instruction': config['instruction'],
+                    'temperature': config['temperature'],
+                    'response': response.text
+                })
+
+            except Exception as e:
+                responses.append({
+                    'success': False,
+                    'error': str(e),
+                    'instruction': config['instruction'],
+                    'temperature': config['temperature']
+                })
+
+        return responses
+
+    # Example usage in Streamlit
+    if st.button("Generate Suggestions"):
+        free_text = st.text_area("Enter Free Text Information")
+        patient_info = st.text_area("Enter Patient Information")
+        instructions = [
+            "a) Provide possible diagnoses in bullet points, b) Suggest lifestyle modifications, c) Recommend diagnostic tests",
+            "a) Provide possible course of action for physio in bullet points, b) Suggest lifestyle modifications that can be achieved by the patient",
+            "a) General preventive advice for the patient through physio routines, b) Suggest lifestyle modifications that can be achieved by the patient"
+        ]
+
+        results = generate_suggestions(free_text, patient_info, instructions)
+
+        st.write("Generated Suggestions:")
+        for idx, result in enumerate(results, 1):
+            st.markdown(f"### Suggestion {idx}:")
+            st.write(f"Temperature: {result['temperature']}")
+            st.write(f"Instruction: {result['instruction']}")
+
+            if result['success']:
+                st.write(f"Response:\n{result['response']}")
+            else:
+                st.error(f"Error: {result['error']}")
+
+else:
+    st.sidebar.warning("Please enter a valid API key.")