Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+# Load dataset
+dataset = load_dataset("Cosmos-AI/Cosmos-dataset")
+
+# Convert dataset to pandas DataFrame
+dataset_df = pd.DataFrame(dataset['train'])  # Assuming 'train' split contains both questions and answers
+
+# Prepare data
+questions = dataset_df['Question'].astype(str).tolist()
+answers = dataset_df['Answer'].astype(str).tolist()
+
+# Load tokenizer
+tokenizer = Tokenizer()
+tokenizer.fit_on_texts(questions + answers)
+word_index = tokenizer.word_index
+
+# Load trained model
+model = load_model("conversation_model.h5")
+
+# Function to generate response
+def generate_response(input_text):
+    # Tokenize input text
+    input_sequence = tokenizer.texts_to_sequences([input_text])
+    input_sequence = pad_sequences(input_sequence, maxlen=max_sequence_length, padding='post')
+
+    # Generate response
+    predicted_sequence = model.predict(input_sequence)
+
+    # Decode predicted sequence
+    response = ""
+    for timestep in predicted_sequence[0]:
+        predicted_word_index = np.argmax(timestep)
+        if predicted_word_index in word_index.values():
+            predicted_word = next(word for word, idx in word_index.items() if idx == predicted_word_index)
+            if predicted_word == 'eos':  # 'eos' marks the end of the sequence
+                break
+            response += predicted_word + " "
+        else:
+            response += ' '  # If predicted index not found in word_index
+
+    return response.strip()
+
+# Define Gradio interface
+iface = gr.Interface(
+    fn=generate_response,
+    inputs="text",
+    outputs="text",
+    title="Conversation Model",
+    description="Enter your message and get a response from the conversational model."
+)
+
+# Launch the interface
+iface.launch()