Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline, AutoTokenizer, AutoModelForMaskedLM, AutoModelForSeq2SeqLM
+
+# Load DNA Analysis Model
+dna_tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t6_8M_UR50D")
+dna_model = AutoModelForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D")
+dna_pipeline = pipeline("fill-mask", model=dna_model, tokenizer=dna_tokenizer)
+
+# Load Ethical Inquiry and Learning Support Model
+ethics_tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base")
+ethics_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base")
+ethics_pipeline = pipeline("text2text-generation", model=ethics_model, tokenizer=ethics_tokenizer)
+
+# Query Classification
+def classify_query(query):
+    """Classify the query into DNA Analysis, Ethical Inquiry, or Learning Support."""
+    if "DNA" in query or "sequence" in query:
+        return "dna_analysis"
+    elif "ethics" in query or "privacy" in query:
+        return "ethical_inquiry"
+    else:
+        return "learning_support"
+
+# Process Query
+def handle_query(query):
+    """Route the query to the appropriate model and generate a response."""
+    task = classify_query(query)
+
+    if task == "dna_analysis":
+        try:
+            # Example DNA sequence processing
+            masked_sequence = query.replace("X", "[MASK]")
+            output = dna_pipeline(masked_sequence)
+            return f"DNA Analysis Result: {output}"
+        except Exception as e:
+            return f"Error in DNA Analysis: {e}"
+
+    elif task == "ethical_inquiry":
+        try:
+            # Ethical guidance response
+            response = ethics_pipeline(query)
+            return f"Ethical Inquiry Response: {response[0]['generated_text']}"
+        except Exception as e:
+            return f"Error in Ethical Inquiry: {e}"
+
+    else:
+        try:
+            # Learning support or general question response
+            response = ethics_pipeline(query)
+            return f"Learning Support Response: {response[0]['generated_text']}"
+        except Exception as e:
+            return f"Error in Learning Support: {e}"
+
+# Gradio Interface
+def chatbot(query):
+    return handle_query(query)
+
+# Deploy with Gradio
+interface = gr.Interface(
+    fn=chatbot,
+    inputs="text",
+    outputs="text",
+    title="BioSphere AI Chatbot",
+    description="A chatbot for DNA Analysis, Ethical Guidance, and Learning Support in Biotech.",
+)
+
+# Add Gemmini API Key Integration
+def deploy_with_gemmini(api_key):
+    print(f"Deploying using Gemmini API Key: {api_key}")
+    interface.launch()
+
+# Replace 'your_api_key' with your actual Gemmini API key
+gemmini_api_key = "your_api_key"
+deploy_with_gemmini(gemmini_api_key)