Create app.py

app.py

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+!pip install langchain google-generativeai langchain-google-genai gradio
+import os
+
+# Set the path to the service account key
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/content/firm-catalyst-437006-s4-407500537db5.json"
+
+import os
+import gradio as gr
+from langchain_google_genai.chat_models import ChatGoogleGenerativeAI
+
+# Set the path to the service account key
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/content/firm-catalyst-437006-s4-407500537db5.json"
+
+# Initialize the LLM
+llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro")
+
+def chat_with_gemini(user_input):
+    try:
+        # Prepare the prompt in the expected format
+        response = llm.predict(user_input)  # Using the 'predict' method instead
+        return response
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=chat_with_gemini,
+    inputs="text",
+    outputs="text",
+    title="Chatbot with Gemini 1.5",
+    description="Ask me anything!"
+)
+
+# Launch the interface with debugging
+iface.launch(debug=True)